A Robust Design Methodology for Optimal Microscale Secondary Flow Control in Compact Inlet Diffusers

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Keller, Dennis J.

2001-01-01

It is the purpose of this study to develop an economical Robust design methodology for microscale secondary flow control in compact inlet diffusers. To illustrate the potential of economical Robust Design methodology, two different mission strategies were considered for the subject inlet, namely Maximum Performance and Maximum HCF Life Expectancy. The Maximum Performance mission maximized total pressure recovery while the Maximum HCF Life Expectancy mission minimized the mean of the first five Fourier harmonic amplitudes, i.e., 'collectively' reduced all the harmonic 1/2 amplitudes of engine face distortion. Each of the mission strategies was subject to a low engine face distortion constraint, i.e., DC60<0.10, which is a level acceptable for commercial engines. For each of these missions strategies, an 'Optimal Robust' (open loop control) and an 'Optimal Adaptive' (closed loop control) installation was designed over a twenty degree angle-of-incidence range. The Optimal Robust installation used economical Robust Design methodology to arrive at a single design which operated over the entire angle-of-incident range (open loop control). The Optimal Adaptive installation optimized all the design parameters at each angle-of-incidence. Thus, the Optimal Adaptive installation would require a closed loop control system to sense a proper signal for each effector and modify that effector device, whether mechanical or fluidic, for optimal inlet performance. In general, the performance differences between the Optimal Adaptive and Optimal Robust installation designs were found to be marginal. This suggests, however, that Optimal Robust open loop installation designs can be very competitive with Optimal Adaptive close loop designs. Secondary flow control in inlets is inherently robust, provided it is optimally designed. Therefore, the new methodology presented in this paper, combined array 'Lower Order' approach to Robust DOE, offers the aerodynamicist a very viable and economical way of exploring the concept of Robust inlet design, where the mission variables are brought directly into the inlet design process and insensitivity or robustness to the mission variables becomes a design objective.

Experimental Validation of L1 Adaptive Control: Rohrs' Counterexample in Flight

NASA Technical Reports Server (NTRS)

Xargay, Enric; Hovakimyan, Naira; Dobrokhodov, Vladimir; Kaminer, Issac; Kitsios, Ioannis; Cao, Chengyu; Gregory, Irene M.; Valavani, Lena

2010-01-01

The paper presents new results on the verification and in-flight validation of an L1 adaptive flight control system, and proposes a general methodology for verification and validation of adaptive flight control algorithms. The proposed framework is based on Rohrs counterexample, a benchmark problem presented in the early 80s to show the limitations of adaptive controllers developed at that time. In this paper, the framework is used to evaluate the performance and robustness characteristics of an L1 adaptive control augmentation loop implemented onboard a small unmanned aerial vehicle. Hardware-in-the-loop simulations and flight test results confirm the ability of the L1 adaptive controller to maintain stability and predictable performance of the closed loop adaptive system in the presence of general (artificially injected) unmodeled dynamics. The results demonstrate the advantages of L1 adaptive control as a verifiable robust adaptive control architecture with the potential of reducing flight control design costs and facilitating the transition of adaptive control into advanced flight control systems.

Optimal Control Modification for Robust Adaptation of Singularly Perturbed Systems with Slow Actuators

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Ishihara, Abraham; Stepanyan, Vahram; Boskovic, Jovan

2009-01-01

Recently a new optimal control modification has been introduced that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. This modification is based on an optimal control formulation to minimize the L2 norm of the tracking error. The optimal control modification adaptive law results in a stable adaptation in the presence of a large adaptive gain. This study examines the optimal control modification adaptive law in the context of a system with a time scale separation resulting from a fast plant with a slow actuator. A singular perturbation analysis is performed to derive a modification to the adaptive law by transforming the original system into a reduced-order system in slow time. The model matching conditions in the transformed time coordinate results in increase in the feedback gain and modification of the adaptive law.

Application of Bounded Linear Stability Analysis Method for Metrics-Driven Adaptive Control

NASA Technical Reports Server (NTRS)

Bakhtiari-Nejad, Maryam; Nguyen, Nhan T.; Krishnakumar, Kalmanje

2009-01-01

This paper presents the application of Bounded Linear Stability Analysis (BLSA) method for metrics-driven adaptive control. The bounded linear stability analysis method is used for analyzing stability of adaptive control models, without linearizing the adaptive laws. Metrics-driven adaptive control introduces a notion that adaptation should be driven by some stability metrics to achieve robustness. By the application of bounded linear stability analysis method the adaptive gain is adjusted during the adaptation in order to meet certain phase margin requirements. Analysis of metrics-driven adaptive control is evaluated for a second order system that represents a pitch attitude control of a generic transport aircraft. The analysis shows that the system with the metrics-conforming variable adaptive gain becomes more robust to unmodeled dynamics or time delay. The effect of analysis time-window for BLSA is also evaluated in order to meet the stability margin criteria.

Reconfigurable Flight Control Design using a Robust Servo LQR and Radial Basis Function Neural Networks

NASA Technical Reports Server (NTRS)

Burken, John J.

2005-01-01

This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.

Advances in Adaptive Control Methods

NASA Technical Reports Server (NTRS)

Nguyen, Nhan

2009-01-01

This poster presentation describes recent advances in adaptive control technology developed by NASA. Optimal Control Modification is a novel adaptive law that can improve performance and robustness of adaptive control systems. A new technique has been developed to provide an analytical method for computing time delay stability margin for adaptive control systems.

Distributed robust adaptive control of high order nonlinear multi agent systems.

PubMed

Hashemi, Mahnaz; Shahgholian, Ghazanfar

2018-03-01

In this paper, a robust adaptive neural network based controller is presented for multi agent high order nonlinear systems with unknown nonlinear functions, unknown control gains and unknown actuator failures. At first, Neural Network (NN) is used to approximate the nonlinear uncertainty terms derived from the controller design procedure for the followers. Then, a novel distributed robust adaptive controller is developed by combining the backstepping method and the Dynamic Surface Control (DSC) approach. The proposed controllers are distributed in the sense that the designed controller for each follower agent only requires relative state information between itself and its neighbors. By using the Young's inequality, only few parameters need to be tuned regardless of NN nodes number. Accordingly, the problems of dimensionality curse and explosion of complexity are counteracted, simultaneously. New adaptive laws are designed by choosing the appropriate Lyapunov-Krasovskii functionals. The proposed approach proves the boundedness of all the closed-loop signals in addition to the convergence of the distributed tracking errors to a small neighborhood of the origin. Simulation results indicate that the proposed controller is effective and robust. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Decentralized adaptive control of robot manipulators with robust stabilization design

NASA Technical Reports Server (NTRS)

Yuan, Bau-San; Book, Wayne J.

1988-01-01

Due to geometric nonlinearities and complex dynamics, a decentralized technique for adaptive control for multilink robot arms is attractive. Lyapunov-function theory for stability analysis provides an approach to robust stabilization. Each joint of the arm is treated as a component subsystem. The adaptive controller is made locally stable with servo signals including proportional and integral gains. This results in the bound on the dynamical interactions with other subsystems. A nonlinear controller which stabilizes the system with uniform boundedness is used to improve the robustness properties of the overall system. As a result, the robot tracks the reference trajectories with convergence. This strategy makes computation simple and therefore facilitates real-time implementation.

Adaptive nonsingular fast terminal sliding-mode control for the tracking problem of uncertain dynamical systems.

PubMed

Boukattaya, Mohamed; Mezghani, Neila; Damak, Tarak

2018-06-01

In this paper, robust and adaptive nonsingular fast terminal sliding-mode (NFTSM) control schemes for the trajectory tracking problem are proposed with known or unknown upper bound of the system uncertainty and external disturbances. The developed controllers take the advantage of the NFTSM theory to ensure fast convergence rate, singularity avoidance, and robustness against uncertainties and external disturbances. First, a robust NFTSM controller is proposed which guarantees that sliding surface and equilibrium point can be reached in a short finite-time from any initial state. Then, in order to cope with the unknown upper bound of the system uncertainty which may be occurring in practical applications, a new adaptive NFTSM algorithm is developed. One feature of the proposed control law is their adaptation techniques where the prior knowledge of parameters uncertainty and disturbances is not needed. However, the adaptive tuning law can estimate the upper bound of these uncertainties using only position and velocity measurements. Moreover, the proposed controller eliminates the chattering effect without losing the robustness property and the precision. Stability analysis is performed using the Lyapunov stability theory, and simulation studies are conducted to verify the effectiveness of the developed control schemes. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Robust control of accelerators

NASA Astrophysics Data System (ADS)

Joel, W.; Johnson, D.; Chaouki, Abdallah T.

1991-07-01

The problem of controlling the variations in the rf power system can be effectively cast as an application of modern control theory. Two components of this theory are obtaining a model and a feedback structure. The model inaccuracies influence the choice of a particular controller structure. Because of the modelling uncertainty, one has to design either a variable, adaptive controller or a fixed, robust controller to achieve the desired objective. The adaptive control scheme usually results in very complex hardware; and, therefore, shall not be pursued in this research. In contrast, the robust control method leads to simpler hardware. However, robust control requires a more accurate mathematical model of the physical process than is required by adaptive control. Our research at the Los Alamos National Laboratory (LANL) and the University of New Mexico (UNM) has led to the development and implementation of a new robust rf power feedback system. In this article, we report on our research progress. In section 1, the robust control problem for the rf power system and the philosophy adopted for the beginning phase of our research is presented. In section 2, the results of our proof-of-principle experiments are presented. In section 3, we describe the actual controller configuration that is used in LANL FEL physics experiments. The novelty of our approach is that the control hardware is implemented directly in rf. without demodulating, compensating, and then remodulating.

Robustness of continuous-time adaptive control algorithms in the presence of unmodeled dynamics

NASA Technical Reports Server (NTRS)

Rohrs, C. E.; Valavani, L.; Athans, M.; Stein, G.

1985-01-01

This paper examines the robustness properties of existing adaptive control algorithms to unmodeled plant high-frequency dynamics and unmeasurable output disturbances. It is demonstrated that there exist two infinite-gain operators in the nonlinear dynamic system which determines the time-evolution of output and parameter errors. The pragmatic implications of the existence of such infinite-gain operators is that: (1) sinusoidal reference inputs at specific frequencies and/or (2) sinusoidal output disturbances at any frequency (including dc), can cause the loop gain to increase without bound, thereby exciting the unmodeled high-frequency dynamics, and yielding an unstable control system. Hence, it is concluded that existing adaptive control algorithms as they are presented in the literature referenced in this paper, cannot be used with confidence in practical designs where the plant contains unmodeled dynamics because instability is likely to result. Further understanding is required to ascertain how the currently implemented adaptive systems differ from the theoretical systems studied here and how further theoretical development can improve the robustness of adaptive controllers.

Robust distributed control of spacecraft formation flying with adaptive network topology

NASA Astrophysics Data System (ADS)

Shasti, Behrouz; Alasty, Aria; Assadian, Nima

2017-07-01

In this study, the distributed six degree-of-freedom (6-DOF) coordinated control of spacecraft formation flying in low earth orbit (LEO) has been investigated. For this purpose, an accurate coupled translational and attitude relative dynamics model of the spacecraft with respect to the reference orbit (virtual leader) is presented by considering the most effective perturbation acceleration forces on LEO satellites, i.e. the second zonal harmonic and the atmospheric drag. Subsequently, the 6-DOF coordinated control of spacecraft in formation is studied. During the mission, the spacecraft communicate with each other through a switching network topology in which the weights of its graph Laplacian matrix change adaptively based on a distance-based connectivity function between neighboring agents. Because some of the dynamical system parameters such as spacecraft masses and moments of inertia may vary with time, an adaptive law is developed to estimate the parameter values during the mission. Furthermore, for the case that there is no knowledge of the unknown and time-varying parameters of the system, a robust controller has been developed. It is proved that the stability of the closed-loop system coupled with adaptation in network topology structure and optimality and robustness in control is guaranteed by the robust contraction analysis as an incremental stability method for multiple synchronized systems. The simulation results show the effectiveness of each control method in the presence of uncertainties and parameter variations. The adaptive and robust controllers show their superiority in reducing the state error integral as well as decreasing the control effort and settling time.

Control algorithms for aerobraking in the Martian atmosphere

NASA Technical Reports Server (NTRS)

Ward, Donald T.; Shipley, Buford W., Jr.

1991-01-01

The Analytic Predictor Corrector (APC) and Energy Controller (EC) atmospheric guidance concepts were adapted to control an interplanetary vehicle aerobraking in the Martian atmosphere. Changes are made to the APC to improve its robustness to density variations. These changes include adaptation of a new exit phase algorithm, an adaptive transition velocity to initiate the exit phase, refinement of the reference dynamic pressure calculation and two improved density estimation techniques. The modified controller with the hybrid density estimation technique is called the Mars Hybrid Predictor Corrector (MHPC), while the modified controller with a polynomial density estimator is called the Mars Predictor Corrector (MPC). A Lyapunov Steepest Descent Controller (LSDC) is adapted to control the vehicle. The LSDC lacked robustness, so a Lyapunov tracking exit phase algorithm is developed to guide the vehicle along a reference trajectory. This algorithm, when using the hybrid density estimation technique to define the reference path, is called the Lyapunov Hybrid Tracking Controller (LHTC). With the polynomial density estimator used to define the reference trajectory, the algorithm is called the Lyapunov Tracking Controller (LTC). These four new controllers are tested using a six degree of freedom computer simulation to evaluate their robustness. The MHPC, MPC, LHTC, and LTC show dramatic improvements in robustness over the APC and EC.

Variable Neural Adaptive Robust Control: A Switched System Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lian, Jianming; Hu, Jianghai; Zak, Stanislaw H.

2015-05-01

Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multi-input multi-output uncertain systems. The controllers incorporate a variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. The variable-structure RBF network solves the problem of structure determination associated with fixed-structure RBF networks. It can determine the network structure on-line dynamically by adding or removing radial basis functions according to the tracking performance. The structure variation is taken into account in the stability analysis of the closed-loop system using a switched system approach with the aid of the piecewisemore » quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.« less

Robust adaptive precision motion control of hydraulic actuators with valve dead-zone compensation.

PubMed

Deng, Wenxiang; Yao, Jianyong; Ma, Dawei

2017-09-01

This paper addresses the high performance motion control of hydraulic actuators with parametric uncertainties, unmodeled disturbances and unknown valve dead-zone. By constructing a smooth dead-zone inverse, a robust adaptive controller is proposed via backstepping method, in which adaptive law is synthesized to deal with parametric uncertainties and a continuous nonlinear robust control law to suppress unmodeled disturbances. Since the unknown dead-zone parameters can be estimated by adaptive law and then the effect of dead-zone can be compensated effectively via inverse operation, improved tracking performance can be expected. In addition, the disturbance upper bounds can also be updated online by adaptive laws, which increases the controller operability in practice. The Lyapunov based stability analysis shows that excellent asymptotic output tracking with zero steady-state error can be achieved by the developed controller even in the presence of unmodeled disturbance and unknown valve dead-zone. Finally, the proposed control strategy is experimentally tested on a servovalve controlled hydraulic actuation system subjected to an artificial valve dead-zone. Comparative experimental results are obtained to illustrate the effectiveness of the proposed control scheme. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Verification and Tuning of an Adaptive Controller for an Unmanned Air Vehicle

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Matsutani, Megumi; Annaswamy, Anuradha M.

2010-01-01

This paper focuses on the analysis and tuning of a controller based on the Adaptive Control Technology for Safe Flight (ACTS) architecture. The ACTS architecture consists of a nominal, non-adaptive controller that provides satisfactory performance under nominal flying conditions, and an adaptive controller that provides robustness under off-nominal ones. A framework unifying control verification and gain tuning is used to make the controller s ability to satisfy the closed-loop requirements more robust to uncertainty. In this paper we tune the gains of both controllers using this approach. Some advantages and drawbacks of adaptation are identified by performing a global robustness assessment of both the adaptive controller and its non-adaptive counterpart. The analyses used to determine these characteristics are based on evaluating the degradation in closed-loop performance resulting from uncertainties having increasing levels of severity. The specific adverse conditions considered can be grouped into three categories: aerodynamic uncertainties, structural damage, and actuator failures. These failures include partial and total loss of control effectiveness, locked-in-place control surface deflections, and engine out conditions. The requirements considered are the peak structural loading, the ability of the controller to track pilot commands, the ability of the controller to keep the aircraft s state within the reliable flight envelope, and the handling/riding qualities of the aircraft. The nominal controller resulting from these tuning strategies was successfully validated using the NASA GTM Flight Test Vehicle.

Robust fault detection of turbofan engines subject to adaptive controllers via a Total Measurable Fault Information Residual (ToMFIR) technique.

PubMed

Chen, Wen; Chowdhury, Fahmida N; Djuric, Ana; Yeh, Chih-Ping

2014-09-01

This paper provides a new design of robust fault detection for turbofan engines with adaptive controllers. The critical issue is that the adaptive controllers can depress the faulty effects such that the actual system outputs remain the pre-specified values, making it difficult to detect faults/failures. To solve this problem, a Total Measurable Fault Information Residual (ToMFIR) technique with the aid of system transformation is adopted to detect faults in turbofan engines with adaptive controllers. This design is a ToMFIR-redundancy-based robust fault detection. The ToMFIR is first introduced and existing results are also summarized. The Detailed design process of the ToMFIRs is presented and a turbofan engine model is simulated to verify the effectiveness of the proposed ToMFIR-based fault-detection strategy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Decentralized adaptive robust control based on sliding mode and nonlinear compensator for the control of ankle movement using functional electrical stimulation of agonist-antagonist muscles

NASA Astrophysics Data System (ADS)

Kobravi, Hamid-Reza; Erfanian, Abbas

2009-08-01

A decentralized control methodology is designed for the control of ankle dorsiflexion and plantarflexion in paraplegic subjects with electrical stimulation of tibialis anterior and calf muscles. Each muscle joint is considered as a subsystem and individual controllers are designed for each subsystem. Each controller operates solely on its associated subsystem, with no exchange of information between the subsystems. The interactions between the subsystems are taken as external disturbances for each isolated subsystem. In order to achieve robustness with respect to external disturbances, unmodeled dynamics, model uncertainty and time-varying properties of muscle-joint dynamics, a robust control framework is proposed which is based on the synergistic combination of an adaptive nonlinear compensator with a sliding mode control and is referred to as an adaptive robust control. Extensive simulations and experiments on healthy and paraplegic subjects were performed to demonstrate the robustness against the time-varying properties of muscle-joint dynamics, day-to-day variations, subject-to-subject variations, fast convergence, stability and tracking accuracy of the proposed method. The results indicate that the decentralized robust control provides excellent tracking control for different reference trajectories and can generate control signals to compensate the muscle fatigue and reject the external disturbance. Moreover, the controller is able to automatically regulate the interaction between agonist and antagonist muscles under different conditions of operating without any preprogrammed antagonist activities.

Decentralized adaptive robust control based on sliding mode and nonlinear compensator for the control of ankle movement using functional electrical stimulation of agonist-antagonist muscles.

PubMed

Kobravi, Hamid-Reza; Erfanian, Abbas

2009-08-01

A decentralized control methodology is designed for the control of ankle dorsiflexion and plantarflexion in paraplegic subjects with electrical stimulation of tibialis anterior and calf muscles. Each muscle joint is considered as a subsystem and individual controllers are designed for each subsystem. Each controller operates solely on its associated subsystem, with no exchange of information between the subsystems. The interactions between the subsystems are taken as external disturbances for each isolated subsystem. In order to achieve robustness with respect to external disturbances, unmodeled dynamics, model uncertainty and time-varying properties of muscle-joint dynamics, a robust control framework is proposed which is based on the synergistic combination of an adaptive nonlinear compensator with a sliding mode control and is referred to as an adaptive robust control. Extensive simulations and experiments on healthy and paraplegic subjects were performed to demonstrate the robustness against the time-varying properties of muscle-joint dynamics, day-to-day variations, subject-to-subject variations, fast convergence, stability and tracking accuracy of the proposed method. The results indicate that the decentralized robust control provides excellent tracking control for different reference trajectories and can generate control signals to compensate the muscle fatigue and reject the external disturbance. Moreover, the controller is able to automatically regulate the interaction between agonist and antagonist muscles under different conditions of operating without any preprogrammed antagonist activities.

Event-Based Robust Control for Uncertain Nonlinear Systems Using Adaptive Dynamic Programming.

PubMed

Zhang, Qichao; Zhao, Dongbin; Wang, Ding

2018-01-01

In this paper, the robust control problem for a class of continuous-time nonlinear system with unmatched uncertainties is investigated using an event-based control method. First, the robust control problem is transformed into a corresponding optimal control problem with an augmented control and an appropriate cost function. Under the event-based mechanism, we prove that the solution of the optimal control problem can asymptotically stabilize the uncertain system with an adaptive triggering condition. That is, the designed event-based controller is robust to the original uncertain system. Note that the event-based controller is updated only when the triggering condition is satisfied, which can save the communication resources between the plant and the controller. Then, a single network adaptive dynamic programming structure with experience replay technique is constructed to approach the optimal control policies. The stability of the closed-loop system with the event-based control policy and the augmented control policy is analyzed using the Lyapunov approach. Furthermore, we prove that the minimal intersample time is bounded by a nonzero positive constant, which excludes Zeno behavior during the learning process. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

Adaptive fuzzy sliding control of single-phase PV grid-connected inverter.

PubMed

Fei, Juntao; Zhu, Yunkai

2017-01-01

In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV) grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP) by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance.

Robust adaptive uniform exact tracking control for uncertain Euler-Lagrange system

NASA Astrophysics Data System (ADS)

Yang, Yana; Hua, Changchun; Li, Junpeng; Guan, Xinping

2017-12-01

This paper offers a solution to the robust adaptive uniform exact tracking control for uncertain nonlinear Euler-Lagrange (EL) system. An adaptive finite-time tracking control algorithm is designed by proposing a novel nonsingular integral terminal sliding-mode surface. Moreover, a new adaptive parameter tuning law is also developed by making good use of the system tracking errors and the adaptive parameter estimation errors. Thus, both the trajectory tracking and the parameter estimation can be achieved in a guaranteed time adjusted arbitrarily based on practical demands, simultaneously. Additionally, the control result for the EL system proposed in this paper can be extended to high-order nonlinear systems easily. Finally, a test-bed 2-DOF robot arm is set-up to demonstrate the performance of the new control algorithm.

Robust control of electrostatic torsional micromirrors using adaptive sliding-mode control

NASA Astrophysics Data System (ADS)

Sane, Harshad S.; Yazdi, Navid; Mastrangelo, Carlos H.

2005-01-01

This paper presents high-resolution control of torsional electrostatic micromirrors beyond their inherent pull-in instability using robust sliding-mode control (SMC). The objectives of this paper are two-fold - firstly, to demonstrate the applicability of SMC for MEMS devices; secondly - to present a modified SMC algorithm that yields improved control accuracy. SMC enables compact realization of a robust controller tolerant of device characteristic variations and nonlinearities. Robustness of the control loop is demonstrated through extensive simulations and measurements on MEMS with a wide range in their characteristics. Control of two-axis gimbaled micromirrors beyond their pull-in instability with overall 10-bit pointing accuracy is confirmed experimentally. In addition, this paper presents an analysis of the sources of errors in discrete-time implementation of the control algorithm. To minimize these errors, we present an adaptive version of the SMC algorithm that yields substantial performance improvement without considerably increasing implementation complexity.

Adaptive fuzzy sliding control of single-phase PV grid-connected inverter

PubMed Central

Zhu, Yunkai

2017-01-01

In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV) grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP) by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance. PMID:28797060

A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter.

PubMed

Shabbouei Hagh, Yashar; Mohammadi Asl, Reza; Cocquempot, Vincent

2017-01-01

In this paper, a new hybrid robust fault tolerant control scheme is proposed. A robust H ∞ control law is used in non-faulty situation, while a Non-Singular Terminal Sliding Mode (NTSM) controller is activated as soon as an actuator fault is detected. Since a linear robust controller is designed, the system is first linearized through the feedback linearization method. To switch from one controller to the other, a fuzzy based switching system is used. An Adaptive Joint Unscented Kalman Filter (AJUKF) is used for fault detection and diagnosis. The proposed method is based on the simultaneous estimation of the system states and parameters. In order to show the efficiency of the proposed scheme, a simulated 3-DOF robotic manipulator is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Robust adaptive backstepping neural networks control for spacecraft rendezvous and docking with input saturation.

PubMed

Xia, Kewei; Huo, Wei

2016-05-01

This paper presents a robust adaptive neural networks control strategy for spacecraft rendezvous and docking with the coupled position and attitude dynamics under input saturation. Backstepping technique is applied to design a relative attitude controller and a relative position controller, respectively. The dynamics uncertainties are approximated by radial basis function neural networks (RBFNNs). A novel switching controller consists of an adaptive neural networks controller dominating in its active region combined with an extra robust controller to avoid invalidation of the RBFNNs destroying stability of the system outside the neural active region. An auxiliary signal is introduced to compensate the input saturation with anti-windup technique, and a command filter is employed to approximate derivative of the virtual control in the backstepping procedure. Globally uniformly ultimately bounded of the relative states is proved via Lyapunov theory. Simulation example demonstrates effectiveness of the proposed control scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Robustness of reduced-order multivariable state-space self-tuning controller

NASA Technical Reports Server (NTRS)

Yuan, Zhuzhi; Chen, Zengqiang

1994-01-01

In this paper, we present a quantitative analysis of the robustness of a reduced-order pole-assignment state-space self-tuning controller for a multivariable adaptive control system whose order of the real process is higher than that of the model used in the controller design. The result of stability analysis shows that, under a specific bounded modelling error, the adaptively controlled closed-loop real system via the reduced-order state-space self-tuner is BIBO stable in the presence of unmodelled dynamics.

Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks.

PubMed

Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho

2006-12-01

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.

On decentralized adaptive full-order sliding mode control of multiple UAVs.

PubMed

Xiang, Xianbo; Liu, Chao; Su, Housheng; Zhang, Qin

2017-11-01

In this study, a novel decentralized adaptive full-order sliding mode control framework is proposed for the robust synchronized formation motion of multiple unmanned aerial vehicles (UAVs) subject to system uncertainty. First, a full-order sliding mode surface in a decentralized manner is designed to incorporate both the individual position tracking error and the synchronized formation error while the UAV group is engaged in building a certain desired geometric pattern in three dimensional space. Second, a decentralized virtual plant controller is constructed which allows the embedded low-pass filter to attain the chattering free property of the sliding mode controller. In addition, robust adaptive technique is integrated in the decentralized chattering free sliding control design in order to handle unknown bounded uncertainties, without requirements for assuming a priori knowledge of bounds on the system uncertainties as stated in conventional chattering free control methods. Subsequently, system robustness as well as stability of the decentralized full-order sliding mode control of multiple UAVs is synthesized. Numerical simulation results illustrate the effectiveness of the proposed control framework to achieve robust 3D formation flight of the multi-UAV system. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Pilot Evaluation of Adaptive Control in Motion-Based Flight Simulator

NASA Technical Reports Server (NTRS)

Kaneshige, John T.; Campbell, Stefan Forrest

2009-01-01

The objective of this work is to assess the strengths, weaknesses, and robustness characteristics of several MRAC (Model-Reference Adaptive Control) based adaptive control technologies garnering interest from the community as a whole. To facilitate this, a control study using piloted and unpiloted simulations to evaluate sensitivities and handling qualities was conducted. The adaptive control technologies under consideration were ALR (Adaptive Loop Recovery), BLS (Bounded Linear Stability), Hybrid Adaptive Control, L1, OCM (Optimal Control Modification), PMRAC (Predictor-based MRAC), and traditional MRAC

Sliding Mode Control of Dynamic Voltage Restorer by Using a New Adaptive Reaching Law

NASA Astrophysics Data System (ADS)

Pandey, Achala; Agrawal, Rekha; Mandloi, Ravindra S.; Sarkar, Biswaroop

2017-12-01

This paper presents a new kind of adaptive reaching law for sliding mode control of Dynamic Voltage Restorer (DVR). Such an adaptive reaching law follows under-damped sinusoidal nature that causes the initial state to reach the sliding regime in extremely less time with negligible chattering. Moreover, it is robust in the sense the trajectory does not deviate from the sliding surface. This new approach is developed and successfully applied to DVR. The simulation results are presented that show its robustness.

Robust adaptive sliding mode control for uncertain systems with unknown time-varying delay input.

PubMed

Benamor, Anouar; Messaoud, Hassani

2018-05-02

This article focuses on robust adaptive sliding mode control law for uncertain discrete systems with unknown time-varying delay input, where the uncertainty is assumed unknown. The main results of this paper are divided into three phases. In the first phase, we propose a new sliding surface is derived within the Linear Matrix Inequalities (LMIs). In the second phase, using the new sliding surface, the novel Robust Sliding Mode Control (RSMC) is proposed where the upper bound of uncertainty is supposed known. Finally, the novel approach of Robust Adaptive Sliding ModeControl (RASMC) has been defined for this type of systems, where the upper limit of uncertainty which is assumed unknown. In this new approach, we have estimate the upper limit of uncertainties and we have determined the control law based on a sliding surface that will converge to zero. This novel control laws are been validated in simulation on an uncertain numerical system with good results and comparative study. This efficiency is emphasized through the application of the new controls on the two physical systems which are the process trainer PT326 and hydraulic system two tanks. Published by Elsevier Ltd.

Robust time and frequency domain estimation methods in adaptive control

NASA Technical Reports Server (NTRS)

Lamaire, Richard Orville

1987-01-01

A robust identification method was developed for use in an adaptive control system. The type of estimator is called the robust estimator, since it is robust to the effects of both unmodeled dynamics and an unmeasurable disturbance. The development of the robust estimator was motivated by a need to provide guarantees in the identification part of an adaptive controller. To enable the design of a robust control system, a nominal model as well as a frequency-domain bounding function on the modeling uncertainty associated with this nominal model must be provided. Two estimation methods are presented for finding parameter estimates, and, hence, a nominal model. One of these methods is based on the well developed field of time-domain parameter estimation. In a second method of finding parameter estimates, a type of weighted least-squares fitting to a frequency-domain estimated model is used. The frequency-domain estimator is shown to perform better, in general, than the time-domain parameter estimator. In addition, a methodology for finding a frequency-domain bounding function on the disturbance is used to compute a frequency-domain bounding function on the additive modeling error due to the effects of the disturbance and the use of finite-length data. The performance of the robust estimator in both open-loop and closed-loop situations is examined through the use of simulations.

Intelligent robust tracking control for a class of uncertain strict-feedback nonlinear systems.

PubMed

Chang, Yeong-Chan

2009-02-01

This paper addresses the problem of designing robust tracking controls for a large class of strict-feedback nonlinear systems involving plant uncertainties and external disturbances. The input and virtual input weighting matrices are perturbed by bounded time-varying uncertainties. An adaptive fuzzy-based (or neural-network-based) dynamic feedback tracking controller will be developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error should be as small as possible. First, the adaptive approximators with linearly parameterized models are designed, and a partitioned procedure with respect to the developed adaptive approximators is proposed such that the implementation of the fuzzy (or neural network) basis functions depends only on the state variables but does not depend on the tuning approximation parameters. Furthermore, we extend to design the nonlinearly parameterized adaptive approximators. Consequently, the intelligent robust tracking control schemes developed in this paper possess the properties of computational simplicity and easy implementation. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed control algorithms.

Lyapunov function-based control laws for revolute robot arms - Tracking control, robustness, and adaptive control

NASA Technical Reports Server (NTRS)

Wen, John T.; Kreutz-Delgado, Kenneth; Bayard, David S.

1992-01-01

A new class of joint level control laws for all-revolute robot arms is introduced. The analysis is similar to a recently proposed energy-like Liapunov function approach, except that the closed-loop potential function is shaped in accordance with the underlying joint space topology. This approach gives way to a much simpler analysis and leads to a new class of control designs which guarantee both global asymptotic stability and local exponential stability. When Coulomb and viscous friction and parameter uncertainty are present as model perturbations, a sliding mode-like modification of the control law results in a robustness-enhancing outer loop. Adaptive control is formulated within the same framework. A linear-in-the-parameters formulation is adopted and globally asymptotically stable adaptive control laws are derived by simply replacing unknown model parameters by their estimates (i.e., certainty equivalence adaptation).

Design of Robust Adaptive Unbalance Response Controllers for Rotors with Magnetic Bearings

NASA Technical Reports Server (NTRS)

Knospe, Carl R.; Tamer, Samir M.; Fedigan, Stephen J.

1996-01-01

Experimental results have recently demonstrated that an adaptive open loop control strategy can be highly effective in the suppression of unbalance induced vibration on rotors supported in active magnetic bearings. This algorithm, however, relies upon a predetermined gain matrix. Typically, this matrix is determined by an optimal control formulation resulting in the choice of the pseudo-inverse of the nominal influence coefficient matrix as the gain matrix. This solution may result in problems with stability and performance robustness since the estimated influence coefficient matrix is not equal to the actual influence coefficient matrix. Recently, analysis tools have been developed to examine the robustness of this control algorithm with respect to structured uncertainty. Herein, these tools are extended to produce a design procedure for determining the adaptive law's gain matrix. The resulting control algorithm has a guaranteed convergence rate and steady state performance in spite of the uncertainty in the rotor system. Several examples are presented which demonstrate the effectiveness of this approach and its advantages over the standard optimal control formulation.

Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

PubMed Central

Carmena, Jose M.

2016-01-01

Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics. PMID:27035820

(Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis.

PubMed

He, Fei; Fromion, Vincent; Westerhoff, Hans V

2013-11-21

Metabolic control analysis (MCA) and supply-demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply-demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect. This study integrates control engineering and classical MCA augmented with supply-demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the 'integral control' (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of 'integral control' should rarely be expected to lead to the 'perfect adaptation': although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily. A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems biology, correspond to the 'perfect' regulatory structures designed by control engineering vis-à-vis optimal functions such as robustness. To the extent that they are not, the analyses suggest how they may become so and this in turn should facilitate synthetic biology and metabolic engineering.

An Analysis of the Optimal Control Modification Method Applied to Flutter Suppression

NASA Technical Reports Server (NTRS)

Drew, Michael; Nguyen, Nhan T.; Hashemi, Kelley E.; Ting, Eric; Chaparro, Daniel

2017-01-01

Unlike basic Model Reference Adaptive Control (MRAC)l, Optimal Control Modification (OCM) has been shown to be a promising MRAC modification with robustness and analytical properties not present in other adaptive control methods. This paper presents an analysis of the OCM method, and how the asymptotic property of OCM is useful for analyzing and tuning the controller. We begin with a Lyapunov stability proof of an OCM controller having two adaptive gain terms, then the less conservative and easily analyzed OCM asymptotic property is presented. Two numerical examples are used to show how this property can accurately predict steady state stability and quantitative robustness in the presence of time delay, and relative to linear plant perturbations, and nominal Loop Transfer Recovery (LTR) tuning. The asymptotic property of the OCM controller is then used as an aid in tuning the controller applied to a large scale aeroservoelastic longitudinal aircraft model for flutter suppression. Control with OCM adaptive augmentation is shown to improve performance over that of the nominal non-adaptive controller when significant disparities exist between the controller/observer model and the true plant model.

Autonomous atmospheric entry on mars: Performance improvement using a novel adaptive control algorithm

NASA Astrophysics Data System (ADS)

Ulrich, Steve; de Lafontaine, Jean

2007-12-01

Upcoming landing missions to Mars will require on-board guidance and control systems in order to meet the scientific requirement of landing safely within hundreds of meters to the target of interest. More specifically, in the longitudinal plane, the first objective of the entry guidance and control system is to bring the vehicle to its specified velocity at the specified altitude (as required for safe parachute deployment), while the second objective is to reach the target position in the longitudinal plane. This paper proposes an improvement to the robustness of the constant flight path angle guidance law for achieving the first objective. The improvement consists of combining this guidance law with a novel adaptive control scheme, derived from the so-called Simple Adaptive Control (SAC) technique. Monte-Carlo simulation results are shown to demonstrate the accuracy and the robustness of the proposed guidance and adaptive control system.

Robustness study of the pseudo open-loop controller for multiconjugate adaptive optics.

PubMed

Piatrou, Piotr; Gilles, Luc

2005-02-20

Robustness of the recently proposed "pseudo open-loop control" algorithm against various system errors has been investigated for the representative example of the Gemini-South 8-m telescope multiconjugate adaptive-optics system. The existing model to represent the adaptive-optics system with pseudo open-loop control has been modified to account for misalignments, noise and calibration errors in deformable mirrors, and wave-front sensors. Comparison with the conventional least-squares control model has been done. We show with the aid of both transfer-function pole-placement analysis and Monte Carlo simulations that POLC remains remarkably stable and robust against very large levels of system errors and outperforms in this respect least-squares control. Approximate stability margins as well as performance metrics such as Strehl ratios and rms wave-front residuals averaged over a 1-arc min field of view have been computed for different types and levels of system errors to quantify the expected performance degradation.

Robust LS-SVM-based adaptive constrained control for a class of uncertain nonlinear systems with time-varying predefined performance

NASA Astrophysics Data System (ADS)

Luo, Jianjun; Wei, Caisheng; Dai, Honghua; Yuan, Jianping

2018-03-01

This paper focuses on robust adaptive control for a class of uncertain nonlinear systems subject to input saturation and external disturbance with guaranteed predefined tracking performance. To reduce the limitations of classical predefined performance control method in the presence of unknown initial tracking errors, a novel predefined performance function with time-varying design parameters is first proposed. Then, aiming at reducing the complexity of nonlinear approximations, only two least-square-support-vector-machine-based (LS-SVM-based) approximators with two design parameters are required through norm form transformation of the original system. Further, a novel LS-SVM-based adaptive constrained control scheme is developed under the time-vary predefined performance using backstepping technique. Wherein, to avoid the tedious analysis and repeated differentiations of virtual control laws in the backstepping technique, a simple and robust finite-time-convergent differentiator is devised to only extract its first-order derivative at each step in the presence of external disturbance. In this sense, the inherent demerit of backstepping technique-;explosion of terms; brought by the recursive virtual controller design is conquered. Moreover, an auxiliary system is designed to compensate the control saturation. Finally, three groups of numerical simulations are employed to validate the effectiveness of the newly developed differentiator and the proposed adaptive constrained control scheme.

An adaptive robust controller for time delay maglev transportation systems

NASA Astrophysics Data System (ADS)

Milani, Reza Hamidi; Zarabadipour, Hassan; Shahnazi, Reza

2012-12-01

For engineering systems, uncertainties and time delays are two important issues that must be considered in control design. Uncertainties are often encountered in various dynamical systems due to modeling errors, measurement noises, linearization and approximations. Time delays have always been among the most difficult problems encountered in process control. In practical applications of feedback control, time delay arises frequently and can severely degrade closed-loop system performance and in some cases, drives the system to instability. Therefore, stability analysis and controller synthesis for uncertain nonlinear time-delay systems are important both in theory and in practice and many analytical techniques have been developed using delay-dependent Lyapunov function. In the past decade the magnetic and levitation (maglev) transportation system as a new system with high functionality has been the focus of numerous studies. However, maglev transportation systems are highly nonlinear and thus designing controller for those are challenging. The main topic of this paper is to design an adaptive robust controller for maglev transportation systems with time-delay, parametric uncertainties and external disturbances. In this paper, an adaptive robust control (ARC) is designed for this purpose. It should be noted that the adaptive gain is derived from Lyapunov-Krasovskii synthesis method, therefore asymptotic stability is guaranteed.

Development of An Intelligent Flight Propulsion Control System

NASA Technical Reports Server (NTRS)

Calise, A. J.; Rysdyk, R. T.; Leonhardt, B. K.

1999-01-01

The initial design and demonstration of an Intelligent Flight Propulsion and Control System (IFPCS) is documented. The design is based on the implementation of a nonlinear adaptive flight control architecture. This initial design of the IFPCS enhances flight safety by using propulsion sources to provide redundancy in flight control. The IFPCS enhances the conventional gain scheduled approach in significant ways: (1) The IFPCS provides a back up flight control system that results in consistent responses over a wide range of unanticipated failures. (2) The IFPCS is applicable to a variety of aircraft models without redesign and,(3) significantly reduces the laborious research and design necessary in a gain scheduled approach. The control augmentation is detailed within an approximate Input-Output Linearization setting. The availability of propulsion only provides two control inputs, symmetric and differential thrust. Earlier Propulsion Control Augmentation (PCA) work performed by NASA provided for a trajectory controller with pilot command input of glidepath and heading. This work is aimed at demonstrating the flexibility of the IFPCS in providing consistency in flying qualities under a variety of failure scenarios. This report documents the initial design phase where propulsion only is used. Results confirm that the engine dynamics and associated hard nonlineaaities result in poor handling qualities at best. However, as demonstrated in simulation, the IFPCS is capable of results similar to the gain scheduled designs of the NASA PCA work. The IFPCS design uses crude estimates of aircraft behaviour. The adaptive control architecture demonstrates robust stability and provides robust performance. In this work, robust stability means that all states, errors, and adaptive parameters remain bounded under a wide class of uncertainties and input and output disturbances. Robust performance is measured in the quality of the tracking. The results demonstrate the flexibility of the IFPCS architecture and the ability to provide robust performance under a broad range of uncertainty. Robust stability is proved using Lyapunov like analysis. Future development of the IFPCS will include integration of conventional control surfaces with the use of propulsion augmentation, and utilization of available lift and drag devices, to demonstrate adaptive control capability under a greater variety of failure scenarios. Further work will specifically address the effects of actuator saturation.

Projection Operator: A Step Towards Certification of Adaptive Controllers

NASA Technical Reports Server (NTRS)

Larchev, Gregory V.; Campbell, Stefan F.; Kaneshige, John T.

2010-01-01

One of the major barriers to wider use of adaptive controllers in commercial aviation is the lack of appropriate certification procedures. In order to be certified by the Federal Aviation Administration (FAA), an aircraft controller is expected to meet a set of guidelines on functionality and reliability while not negatively impacting other systems or safety of aircraft operations. Due to their inherent time-variant and non-linear behavior, adaptive controllers cannot be certified via the metrics used for linear conventional controllers, such as gain and phase margin. Projection Operator is a robustness augmentation technique that bounds the output of a non-linear adaptive controller while conforming to the Lyapunov stability rules. It can also be used to limit the control authority of the adaptive component so that the said control authority can be arbitrarily close to that of a linear controller. In this paper we will present the results of applying the Projection Operator to a Model-Reference Adaptive Controller (MRAC), varying the amount of control authority, and comparing controller s performance and stability characteristics with those of a linear controller. We will also show how adjusting Projection Operator parameters can make it easier for the controller to satisfy the certification guidelines by enabling a tradeoff between controller s performance and robustness.

A Comprehensive Robust Adaptive Controller for Gust Load Alleviation

PubMed Central

Quagliotti, Fulvia

2014-01-01

The objective of this paper is the implementation and validation of an adaptive controller for aircraft gust load alleviation. The contribution of this paper is the design of a robust controller that guarantees the reduction of the gust loads, even when the nominal conditions change. Some preliminary results are presented, considering the symmetric aileron deflection as control device. The proposed approach is validated on subsonic transport aircraft for different mass and flight conditions. Moreover, if the controller parameters are tuned for a specific gust model, even if the gust frequency changes, no parameter retuning is required. PMID:24688411

Adaptive robust fault tolerant control design for a class of nonlinear uncertain MIMO systems with quantization.

PubMed

Ao, Wei; Song, Yongdong; Wen, Changyun

2017-05-01

In this paper, we investigate the adaptive control problem for a class of nonlinear uncertain MIMO systems with actuator faults and quantization effects. Under some mild conditions, an adaptive robust fault-tolerant control is developed to compensate the affects of uncertainties, actuator failures and errors caused by quantization, and a range of the parameters for these quantizers is established. Furthermore, a Lyapunov-like approach is adopted to demonstrate that the ultimately uniformly bounded output tracking error is guaranteed by the controller, and the signals of the closed-loop system are ensured to be bounded, even in the presence of at most m-q actuators stuck or outage. Finally, numerical simulations are provided to verify and illustrate the effectiveness of the proposed adaptive schemes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev neural networks.

PubMed

Zou, An-Min; Dev Kumar, Krishna; Hou, Zeng-Guang

2010-09-01

This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach.

Robust master-slave synchronization for general uncertain delayed dynamical model based on adaptive control scheme.

PubMed

Wang, Tianbo; Zhou, Wuneng; Zhao, Shouwei; Yu, Weiqin

2014-03-01

In this paper, the robust exponential synchronization problem for a class of uncertain delayed master-slave dynamical system is investigated by using the adaptive control method. Different from some existing master-slave models, the considered master-slave system includes bounded unmodeled dynamics. In order to compensate the effect of unmodeled dynamics and effectively achieve synchronization, a novel adaptive controller with simple updated laws is proposed. Moreover, the results are given in terms of LMIs, which can be easily solved by LMI Toolbox in Matlab. A numerical example is given to illustrate the effectiveness of the method. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Simple robust control laws for robot manipulators. Part 1: Non-adaptive case

NASA Technical Reports Server (NTRS)

Wen, J. T.; Bayard, D. S.

1987-01-01

A new class of exponentially stabilizing control laws for joint level control of robot arms is introduced. It has been recently recognized that the nonlinear dynamics associated with robotic manipulators have certain inherent passivity properties. More specifically, the derivation of the robotic dynamic equations from the Hamilton's principle gives rise to natural Lyapunov functions for control design based on total energy considerations. Through a slight modification of the energy Lyapunov function and the use of a convenient lemma to handle third order terms in the Lyapunov function derivatives, closed loop exponential stability for both the set point and tracking control problem is demonstrated. The exponential convergence property also leads to robustness with respect to frictions, bounded modeling errors and instrument noise. In one new design, the nonlinear terms are decoupled from real-time measurements which completely removes the requirement for on-line computation of nonlinear terms in the controller implementation. In general, the new class of control laws offers alternatives to the more conventional computed torque method, providing tradeoffs between robustness, computation and convergence properties. Furthermore, these control laws have the unique feature that they can be adapted in a very simple fashion to achieve asymptotically stable adaptive control.

A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

NASA Astrophysics Data System (ADS)

Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

Adaptive Control for Uncertain Nonlinear Multi-Input Multi-Output Systems

NASA Technical Reports Server (NTRS)

Cao, Chengyu (Inventor); Hovakimyan, Naira (Inventor); Xargay, Enric (Inventor)

2014-01-01

Systems and methods of adaptive control for uncertain nonlinear multi-input multi-output systems in the presence of significant unmatched uncertainty with assured performance are provided. The need for gain-scheduling is eliminated through the use of bandwidth-limited (low-pass) filtering in the control channel, which appropriately attenuates the high frequencies typically appearing in fast adaptation situations and preserves the robustness margins in the presence of fast adaptation.

Robust adaptive antiswing control of underactuated crane systems with two parallel payloads and rail length constraint.

PubMed

Zhang, Zhongcai; Wu, Yuqiang; Huang, Jinming

2016-11-01

The antiswing control and accurate positioning are simultaneously investigated for underactuated crane systems in the presence of two parallel payloads on the trolley and rail length limitation. The equations of motion for the crane system in question are established via the Euler-Lagrange equation. An adaptive control strategy is proposed with the help of system energy function and energy shaping technique. Stability analysis shows that under the designed adaptive controller, the payload swings can be suppressed ultimately and the trolley can be regulated to the destination while not exceeding the pre-specified boundaries. Simulation results are provided to show the satisfactory control performances of the presented control method in terms of working efficiency as well as robustness with respect to external disturbances. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Robust Adaptive Control Using a Filtering Action

DTIC Science & Technology

2009-09-01

research performed on this class of control systems , sensitivity to external disturbances and modeling errors together with poor transient response...dissertation, we address the problems of designing a class of Adaptive Control systems which yield fast adaptation, thus good transient response, and...unable to stabilize the system . Although this approach requires more knowledge about the system in order to control it, it is still attractive in cases

Adaptive Fuzzy Output Feedback Control for Switched Nonlinear Systems With Unmodeled Dynamics.

PubMed

Tong, Shaocheng; Li, Yongming

2017-02-01

This paper investigates a robust adaptive fuzzy control stabilization problem for a class of uncertain nonlinear systems with arbitrary switching signals that use an observer-based output feedback scheme. The considered switched nonlinear systems possess the unstructured uncertainties, unmodeled dynamics, and without requiring the states being available for measurement. A state observer which is independent of switching signals is designed to solve the problem of unmeasured states. Fuzzy logic systems are used to identify unknown lumped nonlinear functions so that the problem of unstructured uncertainties can be solved. By combining adaptive backstepping design principle and small-gain approach, a novel robust adaptive fuzzy output feedback stabilization control approach is developed. The stability of the closed-loop system is proved via the common Lyapunov function theory and small-gain theorem. Finally, the simulation results are given to demonstrate the validity and performance of the proposed control strategy.

DSP-based adaptive backstepping using the tracking errors for high-performance sensorless speed control of induction motor drive.

PubMed

Zaafouri, Abderrahmen; Regaya, Chiheb Ben; Azza, Hechmi Ben; Châari, Abdelkader

2016-01-01

This paper presents a modified structure of the backstepping nonlinear control of the induction motor (IM) fitted with an adaptive backstepping speed observer. The control design is based on the backstepping technique complemented by the introduction of integral tracking errors action to improve its robustness. Unlike other research performed on backstepping control with integral action, the control law developed in this paper does not propose the increase of the number of system state so as not increase the complexity of differential equations resolution. The digital simulation and experimental results show the effectiveness of the proposed control compared to the conventional PI control. The results analysis shows the characteristic robustness of the adaptive control to disturbances of the load, the speed variation and low speed. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A new class of energy based control laws for revolute robot arms - Tracking control, robustness enhancement and adaptive control

NASA Technical Reports Server (NTRS)

Wen, John T.; Kreutz, Kenneth; Bayard, David S.

1988-01-01

A class of joint-level control laws for all-revolute robot arms is introduced. The analysis is similar to the recently proposed energy Liapunov function approach except that the closed-loop potential function is shaped in accordance with the underlying joint space topology. By using energy Liapunov functions with the modified potential energy, a much simpler analysis can be used to show closed-loop global asymptotic stability and local exponential stability. When Coulomb and viscous friction and model parameter errors are present, a sliding-mode-like modification of the control law is proposed to add a robustness-enhancing outer loop. Adaptive control is also addressed within the same framework. A linear-in-the-parameters formulation is adopted, and globally asymptotically stable adaptive control laws are derived by replacing the model parameters in the nonadaptive control laws by their estimates.

A Reconfiguration Scheme for Accommodating Actuator Failures in Multi-Input, Multi-Output Flight Control Systems

NASA Technical Reports Server (NTRS)

Siwakosit, W.; Hess, R. A.; Bacon, Bart (Technical Monitor); Burken, John (Technical Monitor)

2000-01-01

A multi-input, multi-output reconfigurable flight control system design utilizing a robust controller and an adaptive filter is presented. The robust control design consists of a reduced-order, linear dynamic inversion controller with an outer-loop compensation matrix derived from Quantitative Feedback Theory (QFT). A principle feature of the scheme is placement of the adaptive filter in series with the QFT compensator thus exploiting the inherent robustness of the nominal flight control system in the presence of plant uncertainties. An example of the scheme is presented in a pilot-in-the-loop computer simulation using a simplified model of the lateral-directional dynamics of the NASA F18 High Angle of Attack Research Vehicle (HARV) that included nonlinear anti-wind up logic and actuator limitations. Prediction of handling qualities and pilot-induced oscillation tendencies in the presence of these nonlinearities is included in the example.

Multivariable robust adaptive sliding mode control of an industrial boiler-turbine in the presence of modeling imprecisions and external disturbances: A comparison with type-I servo controller.

PubMed

Ghabraei, Soheil; Moradi, Hamed; Vossoughi, Gholamreza

2015-09-01

To guarantee the safety and efficient performance of the power plant, a robust controller for the boiler-turbine unit is needed. In this paper, a robust adaptive sliding mode controller (RASMC) is proposed to control a nonlinear multi-input multi-output (MIMO) model of industrial boiler-turbine unit, in the presence of unknown bounded uncertainties and external disturbances. To overcome the coupled nonlinearities and investigate the zero dynamics, input-output linearization is performed, and then the new decoupled inputs are derived. To tackle the uncertainties and external disturbances, appropriate adaption laws are introduced. For constructing the RASMC, suitable sliding surface is considered. To guarantee the sliding motion occurrence, appropriate control laws are constructed. Then the robustness and stability of the proposed RASMC is proved via Lyapunov stability theory. To compare the performance of the purposed RASMC with traditional control schemes, a type-I servo controller is designed. To evaluate the performance of the proposed control schemes, simulation studies on nonlinear MIMO dynamic system in the presence of high frequency bounded uncertainties and external disturbances are conducted and compared. Comparison of the results reveals the superiority of proposed RASMC over the traditional control schemes. RAMSC acts efficiently in disturbance rejection and keeping the system behavior in desirable tracking objectives, without the existence of unstable quasi-periodic solutions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Prescribed-performance fault-tolerant control for feedback linearisable systems with an aircraft application

NASA Astrophysics Data System (ADS)

Gao, Gang; Wang, Jinzhi; Wang, Xianghua

2017-05-01

This paper investigates fault-tolerant control (FTC) for feedback linearisable systems (FLSs) and its application to an aircraft. To ensure desired transient and steady-state behaviours of the tracking error under actuator faults, the dynamic effect caused by the actuator failures on the error dynamics of a transformed model is analysed, and three control strategies are designed. The first FTC strategy is proposed as a robust controller, which relies on the explicit information about several parameters of the actuator faults. To eliminate the need for these parameters and the input chattering phenomenon, the robust control law is later combined with the adaptive technique to generate the adaptive FTC law. Next, the adaptive control law is further improved to achieve the prescribed performance under more severe input disturbance. Finally, the proposed control laws are applied to an air-breathing hypersonic vehicle (AHV) subject to actuator failures, which confirms the effectiveness of the proposed strategies.

The beauty of simple adaptive control and new developments in nonlinear systems stability analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barkana, Itzhak, E-mail: ibarkana@gmail.com

Although various adaptive control techniques have been around for a long time and in spite of successful proofs of stability and even successful demonstrations of performance, the eventual use of adaptive control methodologies in practical real world systems has met a rather strong resistance from practitioners and has remained limited. Apparently, it is difficult to guarantee or even understand the conditions that can guarantee stable operations of adaptive control systems under realistic operational environments. Besides, it is difficult to measure the robustness of adaptive control system stability and allow it to be compared with the common and widely used measuremore » of phase margin and gain margin that is utilized by present, mainly LTI, controllers. Furthermore, customary stability analysis methods seem to imply that the mere stability of adaptive systems may be adversely affected by any tiny deviation from the pretty idealistic and assumably required stability conditions. This paper first revisits the fundamental qualities of customary direct adaptive control methodologies, in particular the classical Model Reference Adaptive Control, and shows that some of their basic drawbacks have been addressed and eliminated within the so-called Simple Adaptive Control methodology. Moreover, recent developments in the stability analysis methods of nonlinear systems show that prior conditions that were customarily assumed to be needed for stability are only apparent and can be eliminated. As a result, sufficient conditions that guarantee stability are clearly stated and lead to similarly clear proofs of stability. As many real-world applications show, once robust stability of the adaptive systems can be guaranteed, the added value of using Add-On Adaptive Control along with classical Control design techniques is pushing the desired performance beyond any previous limits.« less

Asymptotic Linearity of Optimal Control Modification Adaptive Law with Analytical Stability Margins

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2010-01-01

Optimal control modification has been developed to improve robustness to model-reference adaptive control. For systems with linear matched uncertainty, optimal control modification adaptive law can be shown by a singular perturbation argument to possess an outer solution that exhibits a linear asymptotic property. Analytical expressions of phase and time delay margins for the outer solution can be obtained. Using the gradient projection operator, a free design parameter of the adaptive law can be selected to satisfy stability margins.

Method and apparatus for adaptive force and position control of manipulators

NASA Technical Reports Server (NTRS)

Seraji, Homayoun (Inventor)

1989-01-01

The present invention discloses systematic methods and apparatus for the design of real time controllers. Real-time control employs adaptive force/position by use of feedforward and feedback controllers, with the feedforward controller being the inverse of the linearized model of robot dynamics and containing only proportional-double-derivative terms is disclosed. The feedback controller, of the proportional-integral-derivative type, ensures that manipulator joints follow reference trajectories and the feedback controller achieves robust tracking of step-plus-exponential trajectories, all in real time. The adaptive controller includes adaptive force and position control within a hybrid control architecture. The adaptive controller, for force control, achieves tracking of desired force setpoints, and the adaptive position controller accomplishes tracking of desired position trajectories. Circuits in the adaptive feedback and feedforward controllers are varied by adaptation laws.

Adaptive control of a manipulator with a flexible link

NASA Technical Reports Server (NTRS)

Yang, Y. P.; Gibson, J. S.

1988-01-01

An adaptive controller for a manipulator with one rigid link and one flexible link is presented. The performance and robustness of the controller are demonstrated by numerical simulation results. In the simulations, the manipulator moves in a gravitational field and a finite element model represents the flexible link.

(Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis

PubMed Central

2013-01-01

Background Metabolic control analysis (MCA) and supply–demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply–demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect. Results This study integrates control engineering and classical MCA augmented with supply–demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the ‘integral control’ (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of ‘integral control’ should rarely be expected to lead to the ‘perfect adaptation’: although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily. Conclusions A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems biology, correspond to the ‘perfect’ regulatory structures designed by control engineering vis-à-vis optimal functions such as robustness. To the extent that they are not, the analyses suggest how they may become so and this in turn should facilitate synthetic biology and metabolic engineering. PMID:24261908

Adaptive GSA-based optimal tuning of PI controlled servo systems with reduced process parametric sensitivity, robust stability and controller robustness.

PubMed

Precup, Radu-Emil; David, Radu-Codrut; Petriu, Emil M; Radac, Mircea-Bogdan; Preitl, Stefan

2014-11-01

This paper suggests a new generation of optimal PI controllers for a class of servo systems characterized by saturation and dead zone static nonlinearities and second-order models with an integral component. The objective functions are expressed as the integral of time multiplied by absolute error plus the weighted sum of the integrals of output sensitivity functions of the state sensitivity models with respect to two process parametric variations. The PI controller tuning conditions applied to a simplified linear process model involve a single design parameter specific to the extended symmetrical optimum (ESO) method which offers the desired tradeoff to several control system performance indices. An original back-calculation and tracking anti-windup scheme is proposed in order to prevent the integrator wind-up and to compensate for the dead zone nonlinearity of the process. The minimization of the objective functions is carried out in the framework of optimization problems with inequality constraints which guarantee the robust stability with respect to the process parametric variations and the controller robustness. An adaptive gravitational search algorithm (GSA) solves the optimization problems focused on the optimal tuning of the design parameter specific to the ESO method and of the anti-windup tracking gain. A tuning method for PI controllers is proposed as an efficient approach to the design of resilient control systems. The tuning method and the PI controllers are experimentally validated by the adaptive GSA-based tuning of PI controllers for the angular position control of a laboratory servo system.

Feedback system design with an uncertain plant

NASA Technical Reports Server (NTRS)

Milich, D.; Valavani, L.; Athans, M.

1986-01-01

A method is developed to design a fixed-parameter compensator for a linear, time-invariant, SISO (single-input single-output) plant model characterized by significant structured, as well as unstructured, uncertainty. The controller minimizes the H(infinity) norm of the worst-case sensitivity function over the operating band and the resulting feedback system exhibits robust stability and robust performance. It is conjectured that such a robust nonadaptive control design technique can be used on-line in an adaptive control system.

Application of simple adaptive control to water hydraulic servo cylinder system

NASA Astrophysics Data System (ADS)

Ito, Kazuhisa; Yamada, Tsuyoshi; Ikeo, Shigeru; Takahashi, Koji

2012-09-01

Although conventional model reference adaptive control (MRAC) achieves good tracking performance for cylinder control, the controller structure is much more complicated and has less robustness to disturbance in real applications. This paper discusses the use of simple adaptive control (SAC) for positioning a water hydraulic servo cylinder system. Compared with MRAC, SAC has a simpler and lower order structure, i.e., higher feasibility. The control performance of SAC is examined and evaluated on a water hydraulic servo cylinder system. With the recent increased concerns over global environmental problems, the water hydraulic technique using pure tap water as a pressure medium has become a new drive source comparable to electric, oil hydraulic, and pneumatic drive systems. This technique is also preferred because of its high power density, high safety against fire hazards in production plants, and easy availability. However, the main problems for precise control in a water hydraulic system are steady state errors and overshoot due to its large friction torque and considerable leakage flow. MRAC has been already applied to compensate for these effects, and better control performances have been obtained. However, there have been no reports on the application of SAC for water hydraulics. To make clear the merits of SAC, the tracking control performance and robustness are discussed based on experimental results. SAC is confirmed to give better tracking performance compared with PI control, and a control precision comparable to MRAC (within 10 μm of the reference position) and higher robustness to parameter change, despite the simple controller. The research results ensure a wider application of simple adaptive control in real mechanical systems.

Evolutionary transitions in controls reconcile adaptation with continuity of evolution.

PubMed

Badyaev, Alexander V

2018-05-19

Evolution proceeds by accumulating functional solutions, necessarily forming an uninterrupted lineage from past solutions of ancestors to the current design of extant forms. At the population level, this process requires an organismal architecture in which the maintenance of local adaptation does not preclude the ability to innovate in the same traits and their continuous evolution. Representing complex traits as networks enables us to visualize a fundamental principle that resolves tension between adaptation and continuous evolution: phenotypic states encompassing adaptations traverse the continuous multi-layered landscape of past physical, developmental and functional associations among traits. The key concept that captures such traversing is network controllability - the ability to move a network from one state into another while maintaining its functionality (reflecting evolvability) and to efficiently propagate information or products through the network within a phenotypic state (maintaining its robustness). Here I suggest that transitions in network controllability - specifically in the topology of controls - help to explain how robustness and evolvability are balanced during evolution. I will focus on evolutionary transitions in degeneracy of metabolic networks - a ubiquitous property of phenotypic robustness where distinct pathways achieve the same end product - to suggest that associated changes in network controls is a common rule underlying phenomena as distinct as phenotypic plasticity, organismal accommodation of novelties, genetic assimilation, and macroevolutionary diversification. Capitalizing on well understood principles by which network structure translates into function of control nodes, I show that accumulating redundancy in one type of network controls inevitably leads to the emergence of another type of controls, forming evolutionary cycles of network controllability that, ultimately, reconcile local adaptation with continuity of evolution. Copyright © 2018 Elsevier Ltd. All rights reserved.

Model-based adaptive sliding mode control of the subcritical boiler-turbine system with uncertainties.

PubMed

Tian, Zhen; Yuan, Jingqi; Xu, Liang; Zhang, Xiang; Wang, Jingcheng

2018-05-25

As higher requirements are proposed for the load regulation and efficiency enhancement, the control performance of boiler-turbine systems has become much more important. In this paper, a novel robust control approach is proposed to improve the coordinated control performance for subcritical boiler-turbine units. To capture the key features of the boiler-turbine system, a nonlinear control-oriented model is established and validated with the history operation data of a 300 MW unit. To achieve system linearization and decoupling, an adaptive feedback linearization strategy is proposed, which could asymptotically eliminate the linearization error caused by the model uncertainties. Based on the linearized boiler-turbine system, a second-order sliding mode controller is designed with the super-twisting algorithm. Moreover, the closed-loop system is proved robustly stable with respect to uncertainties and disturbances. Simulation results are presented to illustrate the effectiveness of the proposed control scheme, which achieves excellent tracking performance, strong robustness and chattering reduction. Copyright © 2018. Published by Elsevier Ltd.

Real-Time Robust Adaptive Modeling and Scheduling for an Electronic Commerce Server

NASA Astrophysics Data System (ADS)

Du, Bing; Ruan, Chun

With the increasing importance and pervasiveness of Internet services, it is becoming a challenge for the proliferation of electronic commerce services to provide performance guarantees under extreme overload. This paper describes a real-time optimization modeling and scheduling approach for performance guarantee of electronic commerce servers. We show that an electronic commerce server may be simulated as a multi-tank system. A robust adaptive server model is subject to unknown additive load disturbances and uncertain model matching. Overload control techniques are based on adaptive admission control to achieve timing guarantees. We evaluate the performance of the model using a complex simulation that is subjected to varying model parameters and massive overload.

Flight Test of Composite Model Reference Adaptive Control (CMRAC) Augmentation Using NASA AirSTAR Infrastructure

NASA Technical Reports Server (NTRS)

Gregory, Irene M.; Gadient, ROss; Lavretsky, Eugene

2011-01-01

This paper presents flight test results of a robust linear baseline controller with and without composite adaptive control augmentation. The flight testing was conducted using the NASA Generic Transport Model as part of the Airborne Subscale Transport Aircraft Research system at NASA Langley Research Center.

Sliding Mode Fault Tolerant Control with Adaptive Diagnosis for Aircraft Engines

NASA Astrophysics Data System (ADS)

Xiao, Lingfei; Du, Yanbin; Hu, Jixiang; Jiang, Bin

2018-03-01

In this paper, a novel sliding mode fault tolerant control method is presented for aircraft engine systems with uncertainties and disturbances on the basis of adaptive diagnostic observer. By taking both sensors faults and actuators faults into account, the general model of aircraft engine control systems which is subjected to uncertainties and disturbances, is considered. Then, the corresponding augmented dynamic model is established in order to facilitate the fault diagnosis and fault tolerant controller design. Next, a suitable detection observer is designed to detect the faults effectively. Through creating an adaptive diagnostic observer and based on sliding mode strategy, the sliding mode fault tolerant controller is constructed. Robust stabilization is discussed and the closed-loop system can be stabilized robustly. It is also proven that the adaptive diagnostic observer output errors and the estimations of faults converge to a set exponentially, and the converge rate greater than some value which can be adjusted by choosing designable parameters properly. The simulation on a twin-shaft aircraft engine verifies the applicability of the proposed fault tolerant control method.

An Optimal Control Modification to Model-Reference Adaptive Control for Fast Adaptation

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Krishnakumar, Kalmanje; Boskovic, Jovan

2008-01-01

This paper presents a method that can achieve fast adaptation for a class of model-reference adaptive control. It is well-known that standard model-reference adaptive control exhibits high-gain control behaviors when a large adaptive gain is used to achieve fast adaptation in order to reduce tracking error rapidly. High gain control creates high-frequency oscillations that can excite unmodeled dynamics and can lead to instability. The fast adaptation approach is based on the minimization of the squares of the tracking error, which is formulated as an optimal control problem. The necessary condition of optimality is used to derive an adaptive law using the gradient method. This adaptive law is shown to result in uniform boundedness of the tracking error by means of the Lyapunov s direct method. Furthermore, this adaptive law allows a large adaptive gain to be used without causing undesired high-gain control effects. The method is shown to be more robust than standard model-reference adaptive control. Simulations demonstrate the effectiveness of the proposed method.

Adaptive Controller Effects on Pilot Behavior

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.; Gregory, Irene M.; Hempley, Lucas E.

2014-01-01

Adaptive control provides robustness and resilience for highly uncertain, and potentially unpredictable, flight dynamics characteristic. Some of the recent flight experiences of pilot-in-the-loop with an adaptive controller have exhibited unpredicted interactions. In retrospect, this is not surprising once it is realized that there are now two adaptive controllers interacting, the software adaptive control system and the pilot. An experiment was conducted to categorize these interactions on the pilot with an adaptive controller during control surface failures. One of the objectives of this experiment was to determine how the adaptation time of the controller affects pilots. The pitch and roll errors, and stick input increased for increasing adaptation time and during the segment when the adaptive controller was adapting. Not surprisingly, altitude, cross track and angle deviations, and vertical velocity also increase during the failure and then slowly return to pre-failure levels. Subjects may change their behavior even as an adaptive controller is adapting with additional stick inputs. Therefore, the adaptive controller should adapt as fast as possible to minimize flight track errors. This will minimize undesirable interactions between the pilot and the adaptive controller and maintain maneuvering precision.

Implementation and Evaluation of Multiple Adaptive Control Technologies for a Generic Transport Aircraft Simulation

NASA Technical Reports Server (NTRS)

Campbell, Stefan F.; Kaneshige, John T.; Nguyen, Nhan T.; Krishakumar, Kalmanje S.

2010-01-01

Presented here is the evaluation of multiple adaptive control technologies for a generic transport aircraft simulation. For this study, seven model reference adaptive control (MRAC) based technologies were considered. Each technology was integrated into an identical dynamic-inversion control architecture and tuned using a methodology based on metrics and specific design requirements. Simulation tests were then performed to evaluate each technology s sensitivity to time-delay, flight condition, model uncertainty, and artificially induced cross-coupling. The resulting robustness and performance characteristics were used to identify potential strengths, weaknesses, and integration challenges of the individual adaptive control technologies

Adaptive robust control of a class of non-affine variable-speed variable-pitch wind turbines with unmodeled dynamics.

PubMed

Bagheri, Pedram; Sun, Qiao

2016-07-01

In this paper, a novel synthesis of Nussbaum-type functions, and an adaptive radial-basis function neural network is proposed to design controllers for variable-speed, variable-pitch wind turbines. Dynamic equations of the wind turbine are highly nonlinear, uncertain, and affected by unknown disturbance sources. Furthermore, the dynamic equations are non-affine with respect to the pitch angle, which is a control input. To address these problems, a Nussbaum-type function, along with a dynamic control law are adopted to resolve the non-affine nature of the equations. Moreover, an adaptive radial-basis function neural network is designed to approximate non-parametric uncertainties. Further, the closed-loop system is made robust to unknown disturbance sources, where no prior knowledge of disturbance bound is assumed in advance. Finally, the Lyapunov stability analysis is conducted to show the stability of the entire closed-loop system. In order to verify analytical results, a simulation is presented and the results are compared to both a PI and an existing adaptive controllers. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Bi-Objective Optimal Control Modification Adaptive Control for Systems with Input Uncertainty

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2012-01-01

This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.

L(sub 1) Adaptive Flight Control System: Flight Evaluation and Technology Transition

NASA Technical Reports Server (NTRS)

Xargay, Enric; Hovakimyan, Naira; Dobrokhodov, Vladimir; Kaminer, Isaac; Gregory, Irene M.; Cao, Chengyu

2010-01-01

Certification of adaptive control technologies for both manned and unmanned aircraft represent a major challenge for current Verification and Validation techniques. A (missing) key step towards flight certification of adaptive flight control systems is the definition and development of analysis tools and methods to support Verification and Validation for nonlinear systems, similar to the procedures currently used for linear systems. In this paper, we describe and demonstrate the advantages of L(sub l) adaptive control architectures for closing some of the gaps in certification of adaptive flight control systems, which may facilitate the transition of adaptive control into military and commercial aerospace applications. As illustrative examples, we present the results of a piloted simulation evaluation on the NASA AirSTAR flight test vehicle, and results of an extensive flight test program conducted by the Naval Postgraduate School to demonstrate the advantages of L(sub l) adaptive control as a verifiable robust adaptive flight control system.

Nonlinear stability and control study of highly maneuverable high performance aircraft, phase 2

NASA Technical Reports Server (NTRS)

Mohler, R. R.

1992-01-01

Research leading to the development of new nonlinear methodologies for the adaptive control and stability analysis of high angle of attack aircraft such as the F-18 is discussed. The emphasis has been on nonlinear adaptive control, but associated model development, system identification, stability analysis, and simulation were studied in some detail as well. Studies indicated that nonlinear adaptive control can outperform linear adaptive control for rapid maneuvers with large changes in angle of attack. Included here are studies on nonlinear model algorithmic controller design and an analysis of nonlinear system stability using robust stability analysis for linear systems.

Predictor-Based Model Reference Adaptive Control

NASA Technical Reports Server (NTRS)

Lavretsky, Eugene; Gadient, Ross; Gregory, Irene M.

2009-01-01

This paper is devoted to robust, Predictor-based Model Reference Adaptive Control (PMRAC) design. The proposed adaptive system is compared with the now-classical Model Reference Adaptive Control (MRAC) architecture. Simulation examples are presented. Numerical evidence indicates that the proposed PMRAC tracking architecture has better than MRAC transient characteristics. In this paper, we presented a state-predictor based direct adaptive tracking design methodology for multi-input dynamical systems, with partially known dynamics. Efficiency of the design was demonstrated using short period dynamics of an aircraft. Formal proof of the reported PMRAC benefits constitute future research and will be reported elsewhere.

Speed tracking control of pneumatic motor servo systems using observation-based adaptive dynamic sliding-mode control

NASA Astrophysics Data System (ADS)

Chen, Syuan-Yi; Gong, Sheng-Sian

2017-09-01

This study aims to develop an adaptive high-precision control system for controlling the speed of a vane-type air motor (VAM) pneumatic servo system. In practice, the rotor speed of a VAM depends on the input mass air flow, which can be controlled by the effective orifice area (EOA) of an electronic throttle valve (ETV). As the control variable of a second-order pneumatic system is the integral of the EOA, an observation-based adaptive dynamic sliding-mode control (ADSMC) system is proposed to derive the differential of the control variable, namely, the EOA control signal. In the ADSMC system, a proportional-integral-derivative fuzzy neural network (PIDFNN) observer is used to achieve an ideal dynamic sliding-mode control (DSMC), and a supervisor compensator is designed to eliminate the approximation error. As a result, the ADSMC incorporates the robustness of a DSMC and the online learning ability of a PIDFNN. To ensure the convergence of the tracking error, a Lyapunov-based analytical method is employed to obtain the adaptive algorithms required to tune the control parameters of the online ADSMC system. Finally, our experimental results demonstrate the precision and robustness of the ADSMC system for highly nonlinear and time-varying VAM pneumatic servo systems.

Optimal Control Modification Adaptive Law for Time-Scale Separated Systems

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2010-01-01

Recently a new optimal control modification has been introduced that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. This modification is based on an optimal control formulation to minimize the L2 norm of the tracking error. The optimal control modification adaptive law results in a stable adaptation in the presence of a large adaptive gain. This study examines the optimal control modification adaptive law in the context of a system with a time scale separation resulting from a fast plant with a slow actuator. A singular perturbation analysis is performed to derive a modification to the adaptive law by transforming the original system into a reduced-order system in slow time. A model matching conditions in the transformed time coordinate results in an increase in the actuator command that effectively compensate for the slow actuator dynamics. Simulations demonstrate effectiveness of the method.

Robust Adaptive Flight Control Design of Air-breathing Hypersonic Vehicles

DTIC Science & Technology

2016-12-07

dynamic inversion controller design for a non -minimum phase hypersonic vehicle is derived by Kuipers et al. [2008]. Moreover, integrated guidance and...stabilization time for inner loop variables is lesser than the intermediate loop variables because of the three-loop-control design methodology . The control...adaptive design . Control Engineering Practice, 2016. Michael A Bolender and David B Doman. A non -linear model for the longitudinal dynamics of a

Dynamic modelling and adaptive robust tracking control of a space robot with two-link flexible manipulators under unknown disturbances

NASA Astrophysics Data System (ADS)

Yang, Xinxin; Ge, Shuzhi Sam; He, Wei

2018-04-01

In this paper, both the closed-form dynamics and adaptive robust tracking control of a space robot with two-link flexible manipulators under unknown disturbances are developed. The dynamic model of the system is described with assumed modes approach and Lagrangian method. The flexible manipulators are represented as Euler-Bernoulli beams. Based on singular perturbation technique, the displacements/joint angles and flexible modes are modelled as slow and fast variables, respectively. A sliding mode control is designed for trajectories tracking of the slow subsystem under unknown but bounded disturbances, and an adaptive sliding mode control is derived for slow subsystem under unknown slowly time-varying disturbances. An optimal linear quadratic regulator method is proposed for the fast subsystem to damp out the vibrations of the flexible manipulators. Theoretical analysis validates the stability of the proposed composite controller. Numerical simulation results demonstrate the performance of the closed-loop flexible space robot system.

Design and implementation of a new modified sliding mode controller for grid-connected inverter to controlling the voltage and frequency.

PubMed

Ghanbarian, Mohammad Mehdi; Nayeripour, Majid; Rajaei, Amirhossein; Mansouri, Mohammad Mahdi

2016-03-01

As the output power of a microgrid with renewable energy sources should be regulated based on the grid conditions, using robust controllers to share and balance the power in order to regulate the voltage and frequency of microgrid is critical. Therefore a proper control system is necessary for updating the reference signals and determining the proportion of each inverter in the microgrid control. This paper proposes a new adaptive method which is robust while the conditions are changing. This controller is based on a modified sliding mode controller which provides adapting conditions in linear and nonlinear loads. The performance of the proposed method is validated by representing the simulation results and experimental lab results. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Global adaptive control for uncertain nonaffine nonlinear hysteretic systems.

PubMed

Liu, Yong-Hua; Huang, Liangpei; Xiao, Dongming; Guo, Yong

2015-09-01

In this paper, the global output tracking is investigated for a class of uncertain nonlinear hysteretic systems with nonaffine structures. By combining the solution properties of the hysteresis model with the novel backstepping approach, a robust adaptive control algorithm is developed without constructing a hysteresis inverse. The proposed control scheme is further modified to tackle the bounded disturbances by adaptively estimating their bounds. It is rigorously proven that the designed adaptive controllers can guarantee global stability of the closed-loop system. Two numerical examples are provided to show the effectiveness of the proposed control schemes. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Robust adaptive relative position and attitude control for spacecraft autonomous proximity.

PubMed

Sun, Liang; Huo, Wei; Jiao, Zongxia

2016-07-01

This paper provides new results of the dynamical modeling and controller designing for autonomous close proximity phase during rendezvous and docking in the presence of kinematic couplings and model uncertainties. A globally defined relative motion mechanical model for close proximity operations is introduced firstly. Then, in spite of the kinematic couplings and thrust misalignment between relative rotation and relative translation, robust adaptive relative position and relative attitude controllers are designed successively. Finally, stability of the overall system is proved that the relative position and relative attitude are uniformly ultimately bounded, and the size of the ultimate bound can be regulated small enough by control system parameters. Performance of the controlled overall system is demonstrated via a representative numerical example. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal Control Modification for Time-Scale Separated Systems

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2012-01-01

Recently a new optimal control modification has been introduced that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. This modification is based on an optimal control formulation to minimize the L2 norm of the tracking error. The optimal control modification adaptive law results in a stable adaptation in the presence of a large adaptive gain. This study examines the optimal control modification adaptive law in the context of a system with a time scale separation resulting from a fast plant with a slow actuator. A singular perturbation analysis is performed to derive a modification to the adaptive law by transforming the original system into a reduced-order system in slow time. A model matching conditions in the transformed time coordinate results in an increase in the actuator command that effectively compensate for the slow actuator dynamics. Simulations demonstrate effectiveness of the method.

Adaptive control in the presence of unmodeled dynamics. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Rohrs, C. E.

1982-01-01

Stability and robustness properties of a wide class of adaptive control algorithms in the presence of unmodeled dynamics and output disturbances were investigated. The class of adaptive algorithms considered are those commonly referred to as model reference adaptive control algorithms, self-tuning controllers, and dead beat adaptive controllers, developed for both continuous-time systems and discrete-time systems. A unified analytical approach was developed to examine the class of existing adaptive algorithms. It was discovered that all existing algorithms contain an infinite gain operator in the dynamic system that defines command reference errors and parameter errors; it is argued that such an infinite gain operator appears to be generic to all adaptive algorithms, whether they exhibit explicit or implicit parameter identification. It is concluded that none of the adaptive algorithms considered can be used with confidence in a practical control system design, because instability will set in with a high probability.

On Motion Planning and Control of Multi-Link Lightweight Robotic Manipulators

NASA Technical Reports Server (NTRS)

Cetinkunt, Sabri

1987-01-01

A general gross and fine motion planning and control strategy is needed for lightweight robotic manipulator applications such as painting, welding, material handling, surface finishing, and spacecraft servicing. The control problem of lightweight manipulators is to perform fast, accurate, and robust motions despite the payload variations, structural flexibility, and other environmental disturbances. Performance of the rigid manipulator model based computed torque and decoupled joint control methods are determined and simulated for the counterpart flexible manipulators. A counterpart flexible manipulator is defined as a manipulator which has structural flexibility, in addition to having the same inertial, geometric, and actuation properties of a given rigid manipulator. An adaptive model following control (AMFC) algorithm is developed to improve the performance in speed, accuracy, and robustness. It is found that the AMFC improves the speed performance by a factor of two over the conventional non-adaptive control methods for given accuracy requirements while proving to be more robust with respect to payload variations. Yet there are clear limitations on the performance of AMFC alone as well, which are imposed by the arm flexibility. In the search to further improve speed performance while providing a desired accuracy and robustness, a combined control strategy is developed. Furthermore, the problem of switching from one control structure to another during the motion and implementation aspects of combined control are discussed.

Robust, Practical Adaptive Control for Launch Vehicles

NASA Technical Reports Server (NTRS)

Orr, Jeb. S.; VanZwieten, Tannen S.

2012-01-01

A modern mechanization of a classical adaptive control concept is presented with an application to launch vehicle attitude control systems. Due to a rigorous flight certification environment, many adaptive control concepts are infeasible when applied to high-risk aerospace systems; methods of stability analysis are either intractable for high complexity models or cannot be reconciled in light of classical requirements. Furthermore, many adaptive techniques appearing in the literature are not suitable for application to conditionally stable systems with complex flexible-body dynamics, as is often the case with launch vehicles. The present technique is a multiplicative forward loop gain adaptive law similar to that used for the NASA X-15 flight research vehicle. In digital implementation with several novel features, it is well-suited to application on aerodynamically unstable launch vehicles with thrust vector control via augmentation of the baseline attitude/attitude-rate feedback control scheme. The approach is compatible with standard design features of autopilots for launch vehicles, including phase stabilization of lateral bending and slosh via linear filters. In addition, the method of assessing flight control stability via classical gain and phase margins is not affected under reasonable assumptions. The algorithm s ability to recover from certain unstable operating regimes can in fact be understood in terms of frequency-domain criteria. Finally, simulation results are presented that confirm the ability of the algorithm to improve performance and robustness in realistic failure scenarios.

Robust output feedback stabilization for a flexible marine riser system.

PubMed

Zhao, Zhijia; Liu, Yu; Guo, Fang

2017-12-06

The aim of this paper is to develop a boundary control for the vibration reduction of a flexible marine riser system in the presence of parametric uncertainties and system states obtained inaccurately. To this end, an adaptive output feedback boundary control is proposed to suppress the riser's vibration fusing with observer-based backstepping, high-gain observers and robust adaptive control theory. In addition, the parameter adaptive laws are designed to compensate for the system parametric uncertainties, and the disturbance observer is introduced to mitigate the effects of external environmental disturbance. The uniformly bounded stability of the closed-loop system is achieved through rigorous Lyapunov analysis without any discretisation or simplification of the dynamics in the time and space, and the state observer error is ensured to exponentially converge to zero as time grows to infinity. In the end, the simulation and comparison studies are carried out to illustrate the performance of the proposed control under the proper choice of the design parameters. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Study on Fuzzy Adaptive Fractional Order PIλDμ Control for Maglev Guiding System

NASA Astrophysics Data System (ADS)

Hu, Qing; Hu, Yuwei

The mathematical model of the linear elevator maglev guiding system is analyzed in this paper. For the linear elevator needs strong stability and robustness to run, the integer order PID was expanded to the fractional order, in order to improve the steady state precision, rapidity and robustness of the system, enhance the accuracy of the parameter in fractional order PIλDμ controller, the fuzzy control is combined with the fractional order PIλDμ control, using the fuzzy logic achieves the parameters online adjustment. The simulations reveal that the system has faster response speed, higher tracking precision, and has stronger robustness to the disturbance.

Adaptive output feedback control of flexible-joint robots using neural networks: dynamic surface design approach.

PubMed

Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho

2008-10-01

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.

Adaptive nonlinear robust relative pose control of spacecraft autonomous rendezvous and proximity operations.

PubMed

Sun, Liang; Huo, Wei; Jiao, Zongxia

2017-03-01

This paper studies relative pose control for a rigid spacecraft with parametric uncertainties approaching to an unknown tumbling target in disturbed space environment. State feedback controllers for relative translation and relative rotation are designed in an adaptive nonlinear robust control framework. The element-wise and norm-wise adaptive laws are utilized to compensate the parametric uncertainties of chaser and target spacecraft, respectively. External disturbances acting on two spacecraft are treated as a lumped and bounded perturbation input for system. To achieve the prescribed disturbance attenuation performance index, feedback gains of controllers are designed by solving linear matrix inequality problems so that lumped disturbance attenuation with respect to the controlled output is ensured in the L 2 -gain sense. Moreover, in the absence of lumped disturbance input, asymptotical convergence of relative pose are proved by using the Lyapunov method. Numerical simulations are performed to show that position tracking and attitude synchronization are accomplished in spite of the presence of couplings and uncertainties. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive attitude control and momentum management for large-angle spacecraft maneuvers

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Sunkel, John W.

1992-01-01

The fully coupled equations of motion are systematically linearized around an equilibrium point of a gravity gradient stabilized spacecraft, controlled by momentum exchange devices. These equations are then used for attitude control system design of an early Space Station Freedom flight configuration, demonstrating the errors caused by the improper approximation of the spacecraft dynamics. A full state feedback controller, incorporating gain-scheduled adaptation of the attitude gains, is developed for use during spacecraft on-orbit assembly or operations characterized by significant mass properties variations. The feasibility of the gain adaptation is demonstrated via a Space Station Freedom assembly sequence case study. The attitude controller stability robustness and transient performance during gain adaptation appear satisfactory.

Robust partial integrated guidance and control for missiles via extended state observer.

PubMed

Wang, Qing; Ran, Maopeng; Dong, Chaoyang

2016-11-01

A novel extended state observer (ESO) based control is proposed for a class of nonlinear systems subject to multiple uncertainties, and then applied to partial integrated guidance and control (PIGC) design for a missile. The proposed control strategy incorporates both an ESO and an adaptive sliding mode control law. The multiple uncertainties are treated as an extended state of the plant, and then estimate them using the ESO and compensate for them in the control action, in real time. Based on the output of the ESO, the resulting adaptive sliding mode control law is inherently continuous and differentiable. Strict proof is given to show that the estimation error of the ESO can be arbitrarily small in a finite time. In addition, the adaptive sliding mode control law can achieve finite time convergence to a neighborhood of the origin, and the accurate expression of the convergent region is given. Finally, simulations are conducted on the planar missile-target engagement geometry. The effectiveness of the proposed control strategy in enhanced interception performance and improved robustness against multiple uncertainties are demonstrated. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Dual-thread parallel control strategy for ophthalmic adaptive optics.

PubMed

Yu, Yongxin; Zhang, Yuhua

To improve ophthalmic adaptive optics speed and compensate for ocular wavefront aberration of high temporal frequency, the adaptive optics wavefront correction has been implemented with a control scheme including 2 parallel threads; one is dedicated to wavefront detection and the other conducts wavefront reconstruction and compensation. With a custom Shack-Hartmann wavefront sensor that measures the ocular wave aberration with 193 subapertures across the pupil, adaptive optics has achieved a closed loop updating frequency up to 110 Hz, and demonstrated robust compensation for ocular wave aberration up to 50 Hz in an adaptive optics scanning laser ophthalmoscope.

Dual-thread parallel control strategy for ophthalmic adaptive optics

PubMed Central

Yu, Yongxin; Zhang, Yuhua

2015-01-01

To improve ophthalmic adaptive optics speed and compensate for ocular wavefront aberration of high temporal frequency, the adaptive optics wavefront correction has been implemented with a control scheme including 2 parallel threads; one is dedicated to wavefront detection and the other conducts wavefront reconstruction and compensation. With a custom Shack-Hartmann wavefront sensor that measures the ocular wave aberration with 193 subapertures across the pupil, adaptive optics has achieved a closed loop updating frequency up to 110 Hz, and demonstrated robust compensation for ocular wave aberration up to 50 Hz in an adaptive optics scanning laser ophthalmoscope. PMID:25866498

Autonomous Pointing Control of a Large Satellite Antenna Subject to Parametric Uncertainty

PubMed Central

Wu, Shunan; Liu, Yufei; Radice, Gianmarco; Tan, Shujun

2017-01-01

With the development of satellite mobile communications, large antennas are now widely used. The precise pointing of the antenna’s optical axis is essential for many space missions. This paper addresses the challenging problem of high-precision autonomous pointing control of a large satellite antenna. The pointing dynamics are firstly proposed. The proportional–derivative feedback and structural filter to perform pointing maneuvers and suppress antenna vibrations are then presented. An adaptive controller to estimate actual system frequencies in the presence of modal parameters uncertainty is proposed. In order to reduce periodic errors, the modified controllers, which include the proposed adaptive controller and an active disturbance rejection filter, are then developed. The system stability and robustness are analyzed and discussed in the frequency domain. Numerical results are finally provided, and the results have demonstrated that the proposed controllers have good autonomy and robustness. PMID:28287450

Robust adaptive fuzzy tracking control for pure-feedback stochastic nonlinear systems with input constraints.

PubMed

Wang, Huanqing; Chen, Bing; Liu, Xiaoping; Liu, Kefu; Lin, Chong

2013-12-01

This paper is concerned with the problem of adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with input saturation. To overcome the design difficulty from nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is first introduced to approximate the saturation function; then, an adaptive fuzzy tracking controller based on the mean-value theorem is constructed by using backstepping technique. The proposed adaptive fuzzy controller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.

Performance characteristics of an adaptive controller based on least-mean-square filters

NASA Technical Reports Server (NTRS)

Mehta, Rajiv S.; Merhav, Shmuel J.

1986-01-01

A closed loop, adaptive control scheme that uses a least mean square filter as the controller model is presented, along with simulation results that demonstrate the excellent robustness of this scheme. It is shown that the scheme adapts very well to unknown plants, even those that are marginally stable, responds appropriately to changes in plant parameters, and is not unduly affected by additive noise. A heuristic argument for the conditions necessary for convergence is presented. Potential applications and extensions of the scheme are also discussed.

Robust control algorithms for Mars aerobraking

NASA Technical Reports Server (NTRS)

Shipley, Buford W., Jr.; Ward, Donald T.

1992-01-01

Four atmospheric guidance concepts have been adapted to control an interplanetary vehicle aerobraking in the Martian atmosphere. The first two offer improvements to the Analytic Predictor Corrector (APC) to increase its robustness to density variations. The second two are variations of a new Liapunov tracking exit phase algorithm, developed to guide the vehicle along a reference trajectory. These four new controllers are tested using a six degree of freedom computer simulation to evaluate their robustness. MARSGRAM is used to develop realistic atmospheres for the study. When square wave density pulses perturb the atmosphere all four controllers are successful. The algorithms are tested against atmospheres where the inbound and outbound density functions are different. Square wave density pulses are again used, but only for the outbound leg of the trajectory. Additionally, sine waves are used to perturb the density function. The new algorithms are found to be more robust than any previously tested and a Liapunov controller is selected as the most robust control algorithm overall examined.

The NASA F-15 Intelligent Flight Control Systems: Generation II

NASA Technical Reports Server (NTRS)

Buschbacher, Mark; Bosworth, John

2006-01-01

The Second Generation (Gen II) control system for the F-15 Intelligent Flight Control System (IFCS) program implements direct adaptive neural networks to demonstrate robust tolerance to faults and failures. The direct adaptive tracking controller integrates learning neural networks (NNs) with a dynamic inversion control law. The term direct adaptive is used because the error between the reference model and the aircraft response is being compensated or directly adapted to minimize error without regard to knowing the cause of the error. No parameter estimation is needed for this direct adaptive control system. In the Gen II design, the feedback errors are regulated with a proportional-plus-integral (PI) compensator. This basic compensator is augmented with an online NN that changes the system gains via an error-based adaptation law to improve aircraft performance at all times, including normal flight, system failures, mispredicted behavior, or changes in behavior resulting from damage.

Fuzzy Behavior-Based Navigation for Planetary

NASA Technical Reports Server (NTRS)

Tunstel, Edward; Danny, Harrison; Lippincott, Tanya; Jamshidi, Mo

1997-01-01

Adaptive behavioral capabilities are necessary for robust rover navigation in unstructured and partially-mapped environments. A control approach is described which exploits the approximate reasoning capability of fuzzy logic to produce adaptive motion behavior. In particular, a behavior-based architecture for hierarchical fuzzy control of microrovers is presented. Its structure is described, as well as mechanisms of control decision-making which give rise to adaptive behavior. Control decisions for local navigation result from a consensus of recommendations offered only by behaviors that are applicable to current situations. Simulation predicts the navigation performance on a microrover in simplified Mars-analog terrain.

Adaptive Control for Autonomous Navigation of Mobile Robots Considering Time Delay and Uncertainty

NASA Astrophysics Data System (ADS)

Armah, Stephen Kofi

Autonomous control of mobile robots has attracted considerable attention of researchers in the areas of robotics and autonomous systems during the past decades. One of the goals in the field of mobile robotics is development of platforms that robustly operate in given, partially unknown, or unpredictable environments and offer desired services to humans. Autonomous mobile robots need to be equipped with effective, robust and/or adaptive, navigation control systems. In spite of enormous reported work on autonomous navigation control systems for mobile robots, achieving the goal above is still an open problem. Robustness and reliability of the controlled system can always be improved. The fundamental issues affecting the stability of the control systems include the undesired nonlinear effects introduced by actuator saturation, time delay in the controlled system, and uncertainty in the model. This research work develops robustly stabilizing control systems by investigating and addressing such nonlinear effects through analytical, simulations, and experiments. The control systems are designed to meet specified transient and steady-state specifications. The systems used for this research are ground (Dr Robot X80SV) and aerial (Parrot AR.Drone 2.0) mobile robots. Firstly, an effective autonomous navigation control system is developed for X80SV using logic control by combining 'go-to-goal', 'avoid-obstacle', and 'follow-wall' controllers. A MATLAB robot simulator is developed to implement this control algorithm and experiments are conducted in a typical office environment. The next stage of the research develops an autonomous position (x, y, and z) and attitude (roll, pitch, and yaw) controllers for a quadrotor, and PD-feedback control is used to achieve stabilization. The quadrotor's nonlinear dynamics and kinematics are implemented using MATLAB S-function to generate the state output. Secondly, the white-box and black-box approaches are used to obtain a linearized second-order altitude models for the quadrotor, AR.Drone 2.0. Proportional (P), pole placement or proportional plus velocity (PV), linear quadratic regulator (LQR), and model reference adaptive control (MRAC) controllers are designed and validated through simulations using MATLAB/Simulink. Control input saturation and time delay in the controlled systems are also studied. MATLAB graphical user interface (GUI) and Simulink programs are developed to implement the controllers on the drone. Thirdly, the time delay in the drone's control system is estimated using analytical and experimental methods. In the experimental approach, the transient properties of the experimental altitude responses are compared to those of simulated responses. The analytical approach makes use of the Lambert W function to obtain analytical solutions of scalar first-order delay differential equations (DDEs). A time-delayed P-feedback control system (retarded type) is used in estimating the time delay. Then an improved system performance is obtained by incorporating the estimated time delay in the design of the PV control system (neutral type) and PV-MRAC control system. Furthermore, the stability of a parametric perturbed linear time-invariant (LTI) retarded-type system is studied. This is done by analytically calculating the stability radius of the system. Simulation of the control system is conducted to confirm the stability. This robust control design and uncertainty analysis are conducted for first-order and second-order quadrotor models. Lastly, the robustly designed PV and PV-MRAC control systems are used to autonomously track multiple waypoints. Also, the robustness of the PV-MRAC controller is tested against a baseline PV controller using the payload capability of the drone. It is shown that the PV-MRAC offers several benefits over the fixed-gain approach of the PV controller. The adaptive control is found to offer enhanced robustness to the payload fluctuations.

Wire rope tension control of hoisting systems using a robust nonlinear adaptive backstepping control scheme.

PubMed

Zhu, Zhen-Cai; Li, Xiang; Shen, Gang; Zhu, Wei-Dong

2018-01-01

This paper concerns wire rope tension control of a double-rope winding hoisting system (DRWHS), which consists of a hoisting system employed to realize a transportation function and an electro-hydraulic servo system utilized to adjust wire rope tensions. A dynamic model of the DRWHS is developed in which parameter uncertainties and external disturbances are considered. A comparison between simulation results using the dynamic model and experimental results using a double-rope winding hoisting experimental system is given in order to demonstrate accuracy of the dynamic model. In order to improve the wire rope tension coordination control performance of the DRWHS, a robust nonlinear adaptive backstepping controller (RNABC) combined with a nonlinear disturbance observer (NDO) is proposed. Main features of the proposed combined controller are: (1) using the RNABC to adjust wire rope tensions with consideration of parameter uncertainties, whose parameters are designed online by adaptive laws derived from Lyapunov stability theory to guarantee the control performance and stability of the closed-loop system; and (2) introducing the NDO to deal with uncertain external disturbances. In order to demonstrate feasibility and effectiveness of the proposed controller, experimental studies have been conducted on the DRWHS controlled by an xPC rapid prototyping system. Experimental results verify that the proposed controller exhibits excellent performance on wire rope tension coordination control compared with a conventional proportional-integral (PI) controller and adaptive backstepping controller. Copyright © 2017 ISA. All rights reserved.

IECON '87: Industrial applications of control and simulation; Proceedings of the 1987 International Conference on Industrial Electronics, Control, and Instrumentation, Cambridge, MA, Nov. 3, 4, 1987

NASA Technical Reports Server (NTRS)

Hartley, Tom T. (Editor)

1987-01-01

Recent advances in control-system design and simulation are discussed in reviews and reports. Among the topics considered are fast algorithms for generating near-optimal binary decision programs, trajectory control of robot manipulators with compensation of load effects via a six-axis force sensor, matrix integrators for real-time simulation, a high-level control language for an autonomous land vehicle, and a practical engineering design method for stable model-reference adaptive systems. Also addressed are the identification and control of flexible-limb robots with unknown loads, adaptive control and robust adaptive control for manipulators with feedforward compensation, adaptive pole-placement controllers with predictive action, variable-structure strategies for motion control, and digital signal-processor-based variable-structure controls.

Flight Test Comparison of Different Adaptive Augmentations for Fault Tolerant Control Laws for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.; Hanson, Curtis E.; Lee, James A.; Kaneshige, John T.

2009-01-01

This report describes the improvements and enhancements to a neural network based approach for directly adapting to aerodynamic changes resulting from damage or failures. This research is a follow-on effort to flight tests performed on the NASA F-15 aircraft as part of the Intelligent Flight Control System research effort. Previous flight test results demonstrated the potential for performance improvement under destabilizing damage conditions. Little or no improvement was provided under simulated control surface failures, however, and the adaptive system was prone to pilot-induced oscillations. An improved controller was designed to reduce the occurrence of pilot-induced oscillations and increase robustness to failures in general. This report presents an analysis of the neural networks used in the previous flight test, the improved adaptive controller, and the baseline case with no adaptation. Flight test results demonstrate significant improvement in performance by using the new adaptive controller compared with the previous adaptive system and the baseline system for control surface failures.

Adaptive robust motion trajectory tracking control of pneumatic cylinders with LuGre model-based friction compensation

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Liu, Hao; Zhu, Xiaocong

2014-07-01

Friction compensation is particularly important for motion trajectory tracking control of pneumatic cylinders at low speed movement. However, most of the existing model-based friction compensation schemes use simple classical models, which are not enough to address applications with high-accuracy position requirements. Furthermore, the friction force in the cylinder is time-varying, and there exist rather severe unmodelled dynamics and unknown disturbances in the pneumatic system. To deal with these problems effectively, an adaptive robust controller with LuGre model-based dynamic friction compensation is constructed. The proposed controller employs on-line recursive least squares estimation (RLSE) to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. In addition, in order to realize LuGre model-based friction compensation, the modified dual-observer structure for estimating immeasurable friction internal state is developed. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology is applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Finally, the proposed controller is tested for tracking sinusoidal trajectories and smooth square trajectory under different loads and sudden disturbance. The testing results demonstrate that the achievable performance of the proposed controller is excellent and is much better than most other studies in literature. Especially when a 0.5 Hz sinusoidal trajectory is tracked, the maximum tracking error is 0.96 mm and the average tracking error is 0.45 mm. This paper constructs an adaptive robust controller which can compensate the friction force in the cylinder.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Basher, A.M.H.

Poor control of steam generator water level of a nuclear power plant may lead to frequent nuclear reactor shutdowns. These shutdowns are more common at low power where the plant exhibits strong non-minimum phase characteristics and flow measurements at low power are unreliable in many instances. There is need to investigate this problem and systematically design a controller for water level regulation. This work is concerned with the study and the design of a suitable controller for a U-Tube Steam Generator (UTSG) of a Pressurized Water Reactor (PWR) which has time varying dynamics. The controller should be suitable for themore » water level control of UTSG without manual operation from start-up to full load transient condition. Some preliminary simulation results are presented that demonstrate the effectiveness of the proposed controller. The development of the complete control algorithm includes components such as robust output tracking, and adaptively estimating both the system parameters and state variables simultaneously. At the present time all these components are not completed due to time constraints. A robust tracking component of the controller for water level control is developed and its effectiveness on the parameter variations is demonstrated in this study. The results appear encouraging and they are only preliminary. Additional work is warranted to resolve other issues such as robust adaptive estimation.« less

Adaptive relative pose control of spacecraft with model couplings and uncertainties

NASA Astrophysics Data System (ADS)

Sun, Liang; Zheng, Zewei

2018-02-01

The spacecraft pose tracking control problem for an uncertain pursuer approaching to a space target is researched in this paper. After modeling the nonlinearly coupled dynamics for relative translational and rotational motions between two spacecraft, position tracking and attitude synchronization controllers are developed independently by using a robust adaptive control approach. The unknown kinematic couplings, parametric uncertainties, and bounded external disturbances are handled with adaptive updating laws. It is proved via Lyapunov method that the pose tracking errors converge to zero asymptotically. Spacecraft close-range rendezvous and proximity operations are introduced as an example to validate the effectiveness of the proposed control approach.

A novel composite adaptive flap controller design by a high-efficient modified differential evolution identification approach.

PubMed

Li, Nailu; Mu, Anle; Yang, Xiyun; Magar, Kaman T; Liu, Chao

2018-05-01

The optimal tuning of adaptive flap controller can improve adaptive flap control performance on uncertain operating environments, but the optimization process is usually time-consuming and it is difficult to design proper optimal tuning strategy for the flap control system (FCS). To solve this problem, a novel adaptive flap controller is designed based on a high-efficient differential evolution (DE) identification technique and composite adaptive internal model control (CAIMC) strategy. The optimal tuning can be easily obtained by DE identified inverse of the FCS via CAIMC structure. To achieve fast tuning, a high-efficient modified adaptive DE algorithm is proposed with new mutant operator and varying range adaptive mechanism for the FCS identification. A tradeoff between optimized adaptive flap control and low computation cost is successfully achieved by proposed controller. Simulation results show the robustness of proposed method and its superiority to conventional adaptive IMC (AIMC) flap controller and the CAIMC flap controllers using other DE algorithms on various uncertain operating conditions. The high computation efficiency of proposed controller is also verified based on the computation time on those operating cases. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Incremental Adaptive Fuzzy Control for Sensorless Stroke Control of A Halbach-type Linear Oscillatory Motor

NASA Astrophysics Data System (ADS)

Lei, Meizhen; Wang, Liqiang

2018-01-01

The halbach-type linear oscillatory motor (HT-LOM) is multi-variable, highly coupled, nonlinear and uncertain, and difficult to get a satisfied result by conventional PID control. An incremental adaptive fuzzy controller (IAFC) for stroke tracking was presented, which combined the merits of PID control, the fuzzy inference mechanism and the adaptive algorithm. The integral-operation is added to the conventional fuzzy control algorithm. The fuzzy scale factor can be online tuned according to the load force and stroke command. The simulation results indicate that the proposed control scheme can achieve satisfied stroke tracking performance and is robust with respect to parameter variations and external disturbance.

Adaptive backstepping fault-tolerant control for flexible spacecraft with unknown bounded disturbances and actuator failures.

PubMed

Jiang, Ye; Hu, Qinglei; Ma, Guangfu

2010-01-01

In this paper, a robust adaptive fault-tolerant control approach to attitude tracking of flexible spacecraft is proposed for use in situations when there are reaction wheel/actuator failures, persistent bounded disturbances and unknown inertia parameter uncertainties. The controller is designed based on an adaptive backstepping sliding mode control scheme, and a sufficient condition under which this control law can render the system semi-globally input-to-state stable is also provided such that the closed-loop system is robust with respect to any disturbance within a quantifiable restriction on the amplitude, as well as the set of initial conditions, if the control gains are designed appropriately. Moreover, in the design, the control law does not need a fault detection and isolation mechanism even if the failure time instants, patterns and values on actuator failures are also unknown for the designers, as motivated from a practical spacecraft control application. In addition to detailed derivations of the new controller design and a rigorous sketch of all the associated stability and attitude error convergence proofs, illustrative simulation results of an application to flexible spacecraft show that high precise attitude control and vibration suppression are successfully achieved using various scenarios of controlling effective failures. 2009. Published by Elsevier Ltd.

Experimental study of adaptive pointing and tracking for large flexible space structures

NASA Technical Reports Server (NTRS)

Boussalis, D.; Bayard, D. S.; Ih, C.; Wang, S. J.; Ahmed, A.

1991-01-01

This paper describes an experimental study of adaptive pointing and tracking control for flexible spacecraft conducted on a complex ground experiment facility. The algorithm used in this study is based on a multivariable direct model reference adaptive control law. Several experimental validation studies were performed earlier using this algorithm for vibration damping and robust regulation, with excellent results. The current work extends previous studies by addressing the pointing and tracking problem. As is consistent with an adaptive control framework, the plant is assumed to be poorly known to the extent that only system level knowledge of its dynamics is available. Explicit bounds on the steady-state pointing error are derived as functions of the adaptive controller design parameters. It is shown that good tracking performance can be achieved in an experimental setting by adjusting adaptive controller design weightings according to the guidelines indicated by the analytical expressions for the error.

Identification and Control of Non-Linear Time-Varying Dynamical Systems Using Artificial Neural Networks

DTIC Science & Technology

1992-09-01

finding an inverse plant such as was done by Bertrand [BD91] and by Levin, Gewirtzman and Inbar in a binary type inverse controller [LGI91], to self tuning...gain robust control. 2) Self oscillating adaptive controller. 3) Gain scheduling. 4) Self tuning. 5) Model-reference adaptive systems. Although the...of multidimensional systems (CS881 as well as aircraft [HG90]. The self oscillating method is also a feedback based mechanism, utilizing a relay in the

Neural-Network-Based Robust Optimal Tracking Control for MIMO Discrete-Time Systems With Unknown Uncertainty Using Adaptive Critic Design.

PubMed

Liu, Lei; Wang, Zhanshan; Zhang, Huaguang

2018-04-01

This paper is concerned with the robust optimal tracking control strategy for a class of nonlinear multi-input multi-output discrete-time systems with unknown uncertainty via adaptive critic design (ACD) scheme. The main purpose is to establish an adaptive actor-critic control method, so that the cost function in the procedure of dealing with uncertainty is minimum and the closed-loop system is stable. Based on the neural network approximator, an action network is applied to generate the optimal control signal and a critic network is used to approximate the cost function, respectively. In contrast to the previous methods, the main features of this paper are: 1) the ACD scheme is integrated into the controllers to cope with the uncertainty and 2) a novel cost function, which is not in quadric form, is proposed so that the total cost in the design procedure is reduced. It is proved that the optimal control signals and the tracking errors are uniformly ultimately bounded even when the uncertainty exists. Finally, a numerical simulation is developed to show the effectiveness of the present approach.

High precision tracking of a piezoelectric nano-manipulator with parameterized hysteresis compensation

NASA Astrophysics Data System (ADS)

Yan, Peng; Zhang, Yangming

2018-06-01

High performance scanning of nano-manipulators is widely deployed in various precision engineering applications such as SPM (scanning probe microscope), where trajectory tracking of sophisticated reference signals is an challenging control problem. The situation is further complicated when rate dependent hysteresis of the piezoelectric actuators and the stress-stiffening induced nonlinear stiffness of the flexure mechanism are considered. In this paper, a novel control framework is proposed to achieve high precision tracking of a piezoelectric nano-manipulator subjected to hysteresis and stiffness nonlinearities. An adaptive parameterized rate-dependent Prandtl-Ishlinskii model is constructed and the corresponding adaptive inverse model based online compensation is derived. Meanwhile a robust adaptive control architecture is further introduced to improve the tracking accuracy and robustness of the compensated system, where the parametric uncertainties of the nonlinear dynamics can be well eliminated by on-line estimations. Comparative experimental studies of the proposed control algorithm are conducted on a PZT actuated nano-manipulating stage, where hysteresis modeling accuracy and excellent tracking performance are demonstrated in real-time implementations, with significant improvement over existing results.

Extended state observer based robust adaptive control on SE(3) for coupled spacecraft tracking maneuver with actuator saturation and misalignment

NASA Astrophysics Data System (ADS)

Zhang, Jianqiao; Ye, Dong; Sun, Zhaowei; Liu, Chuang

2018-02-01

This paper presents a robust adaptive controller integrated with an extended state observer (ESO) to solve coupled spacecraft tracking maneuver in the presence of model uncertainties, external disturbances, actuator uncertainties including magnitude deviation and misalignment, and even actuator saturation. More specifically, employing the exponential coordinates on the Lie group SE(3) to describe configuration tracking errors, the coupled six-degrees-of-freedom (6-DOF) dynamics are developed for spacecraft relative motion, in which a generic fully actuated thruster distribution is considered and the lumped disturbances are reconstructed by using anti-windup technique. Then, a novel ESO, developed via second order sliding mode (SOSM) technique and adding linear correction terms to improve the performance, is designed firstly to estimate the disturbances in finite time. Based on the estimated information, an adaptive fast terminal sliding mode (AFTSM) controller is developed to guarantee the almost global asymptotic stability of the resulting closed-loop system such that the trajectory can be tracked with all the aforementioned drawbacks addressed simultaneously. Finally, the effectiveness of the controller is illustrated through numerical examples.

Adaptive Robust Output Feedback Control for a Marine Dynamic Positioning System Based on a High-Gain Observer.

PubMed

Du, Jialu; Hu, Xin; Liu, Hongbo; Chen, C L Philip

2015-11-01

This paper develops an adaptive robust output feedback control scheme for dynamically positioned ships with unavailable velocities and unknown dynamic parameters in an unknown time-variant disturbance environment. The controller is designed by incorporating the high-gain observer and radial basis function (RBF) neural networks in vectorial backstepping method. The high-gain observer provides the estimations of the ship position and heading as well as velocities. The RBF neural networks are employed to compensate for the uncertainties of ship dynamics. The adaptive laws incorporating a leakage term are designed to estimate the weights of RBF neural networks and the bounds of unknown time-variant environmental disturbances. In contrast to the existing results of dynamic positioning (DP) controllers, the proposed control scheme relies only on the ship position and heading measurements and does not require a priori knowledge of the ship dynamics and external disturbances. By means of Lyapunov functions, it is theoretically proved that our output feedback controller can control a ship's position and heading to the arbitrarily small neighborhood of the desired target values while guaranteeing that all signals in the closed-loop DP control system are uniformly ultimately bounded. Finally, simulations involving two ships are carried out, and simulation results demonstrate the effectiveness of the proposed control scheme.

Hybrid passive/active damping for robust multivariable acoustic control in composite plates

NASA Astrophysics Data System (ADS)

Veeramani, Sudha; Wereley, Norman M.

1996-05-01

Noise transmission through a flexible kevlar-epoxy composite trim panel into an acoustic cavity or box is studied with the intent of controlling the interior sound fields. A hybrid noise attenuation technique is proposed which uses viscoelastic damping layers in the composite plate for passive attenuation of high frequency noise transmission, and uses piezo-electric patch actuators for active control in the low frequency range. An adaptive feedforward noise control strategy is applied. The passive structural damping augmentation incorporated in the composite plates is also intended to increase stability robustness of the active noise control strategy. A condenser microphone in the interior of the enclosure functions as the error sensor. Three composite plates were experimentally evaluated: one with no damping layer, the second with a 10 mil damping layer, and the third with a 15 mil damping layer. The damping layer was cocured in the kevlar-epoxy trim panels. Damping in the plates was increased from 1.6% for the plate with no damping layer, to 5.9% for the plate with a 15 mil damping layer. In experimental studies, the improved stability robustness of the controller was demonstrated by improved adaptive feedforward control algorithm convergence. A preliminary analytical model is presented that describes the dynamic behavior of a composite panel actuated by piezoelectric actuators bonded to its surface.

Adaptive PID formation control of nonholonomic robots without leader's velocity information.

PubMed

Shen, Dongbin; Sun, Weijie; Sun, Zhendong

2014-03-01

This paper proposes an adaptive proportional integral derivative (PID) algorithm to solve a formation control problem in the leader-follower framework where the leader robot's velocities are unknown for the follower robots. The main idea is first to design some proper ideal control law for the formation system to obtain a required performance, and then to propose the adaptive PID methodology to approach the ideal controller. As a result, the formation is achieved with much more enhanced robust formation performance. The stability of the closed-loop system is theoretically proved by Lyapunov method. Both numerical simulations and physical vehicle experiments are presented to verify the effectiveness of the proposed adaptive PID algorithm. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Fine Surface Control of Flexible Space Mirrors Using Adaptive Optics and Robust Control

DTIC Science & Technology

2009-03-01

an AO system not only increases complexity but also lends itself to coupling between actuators. Whereas historically, control laws treated AO...adaptive optic in large ground based AO systems is treated as a static system with no dynamics. In the case of a deformable mirror, it is assumed... astigmatism , and so on. As with any series expansion, the more terms used, the more accurate the approximation will be. For this research, 21 Zernike

Adaptive disturbance compensation finite control set optimal control for PMSM systems based on sliding mode extended state observer

NASA Astrophysics Data System (ADS)

Wu, Yun-jie; Li, Guo-fei

2018-01-01

Based on sliding mode extended state observer (SMESO) technique, an adaptive disturbance compensation finite control set optimal control (FCS-OC) strategy is proposed for permanent magnet synchronous motor (PMSM) system driven by voltage source inverter (VSI). So as to improve robustness of finite control set optimal control strategy, a SMESO is proposed to estimate the output-effect disturbance. The estimated value is fed back to finite control set optimal controller for implementing disturbance compensation. It is indicated through theoretical analysis that the designed SMESO could converge in finite time. The simulation results illustrate that the proposed adaptive disturbance compensation FCS-OC possesses better dynamical response behavior in the presence of disturbance.

Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics.

PubMed

Li, Yongming; Sui, Shuai; Tong, Shaocheng

2017-02-01

This paper deals with the problem of adaptive fuzzy output feedback control for a class of stochastic nonlinear switched systems. The controlled system in this paper possesses unmeasured states, completely unknown nonlinear system functions, unmodeled dynamics, and arbitrary switchings. A state observer which does not depend on the switching signal is constructed to tackle the unmeasured states. Fuzzy logic systems are employed to identify the completely unknown nonlinear system functions. Based on the common Lyapunov stability theory and stochastic small-gain theorem, a new robust adaptive fuzzy backstepping stabilization control strategy is developed. The stability of the closed-loop system on input-state-practically stable in probability is proved. The simulation results are given to verify the efficiency of the proposed fuzzy adaptive control scheme.

Robust Adaptive Synchronization of Ring Configured Uncertain Chaotic FitzHugh–Nagumo Neurons under Direction-Dependent Coupling

PubMed Central

Iqbal, Muhammad; Rehan, Muhammad; Hong, Keum-Shik

2018-01-01

This paper exploits the dynamical modeling, behavior analysis, and synchronization of a network of four different FitzHugh–Nagumo (FHN) neurons with unknown parameters linked in a ring configuration under direction-dependent coupling. The main purpose is to investigate a robust adaptive control law for the synchronization of uncertain and perturbed neurons, communicating in a medium of bidirectional coupling. The neurons are assumed to be different and interconnected in a ring structure. The strength of the gap junctions is taken to be different for each link in the network, owing to the inter-neuronal coupling medium properties. Robust adaptive control mechanism based on Lyapunov stability analysis is employed and theoretical criteria are derived to realize the synchronization of the network of four FHN neurons in a ring form with unknown parameters under direction-dependent coupling and disturbances. The proposed scheme for synchronization of dissimilar neurons, under external electrical stimuli, coupled in a ring communication topology, having all parameters unknown, and subject to directional coupling medium and perturbations, is addressed for the first time as per our knowledge. To demonstrate the efficacy of the proposed strategy, simulation results are provided. PMID:29535622

Robust adaptive cruise control of high speed trains.

PubMed

Faieghi, Mohammadreza; Jalali, Aliakbar; Mashhadi, Seyed Kamal-e-ddin Mousavi

2014-03-01

The cruise control problem of high speed trains in the presence of unknown parameters and external disturbances is considered. In particular a Lyapunov-based robust adaptive controller is presented to achieve asymptotic tracking and disturbance rejection. The system under consideration is nonlinear, MIMO and non-minimum phase. To deal with the limitations arising from the unstable zero-dynamics we do an output redefinition such that the zero-dynamics with respect to new outputs becomes stable. Rigorous stability analyses are presented which establish the boundedness of all the internal states and simultaneously asymptotic stability of the tracking error dynamics. The results are presented for two common configurations of high speed trains, i.e. the DD and PPD designs, based on the multi-body model and are verified by several numerical simulations. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Hexacopter trajectory control using a neural network

NASA Astrophysics Data System (ADS)

Artale, V.; Collotta, M.; Pau, G.; Ricciardello, A.

2013-10-01

The modern flight control systems are complex due to their non-linear nature. In fact, modern aerospace vehicles are expected to have non-conventional flight envelopes and, then, they must guarantee a high level of robustness and adaptability in order to operate in uncertain environments. Neural Networks (NN), with real-time learning capability, for flight control can be used in applications with manned or unmanned aerial vehicles. Indeed, using proven lower level control algorithms with adaptive elements that exhibit long term learning could help in achieving better adaptation performance while performing aggressive maneuvers. In this paper we show a mathematical modeling and a Neural Network for a hexacopter dynamics in order to develop proper methods for stabilization and trajectory control.

On adaptive robustness approach to Anti-Jam signal processing

NASA Astrophysics Data System (ADS)

Poberezhskiy, Y. S.; Poberezhskiy, G. Y.

An effective approach to exploiting statistical differences between desired and jamming signals named adaptive robustness is proposed and analyzed in this paper. It combines conventional Bayesian, adaptive, and robust approaches that are complementary to each other. This combining strengthens the advantages and mitigates the drawbacks of the conventional approaches. Adaptive robustness is equally applicable to both jammers and their victim systems. The capabilities required for realization of adaptive robustness in jammers and victim systems are determined. The employment of a specific nonlinear robust algorithm for anti-jam (AJ) processing is described and analyzed. Its effectiveness in practical situations has been proven analytically and confirmed by simulation. Since adaptive robustness can be used by both sides in electronic warfare, it is more advantageous for the fastest and most intelligent side. Many results obtained and discussed in this paper are also applicable to commercial applications such as communications in unregulated or poorly regulated frequency ranges and systems with cognitive capabilities.

Vibration control in statically indeterminate adaptive truss structures

NASA Technical Reports Server (NTRS)

Baycan, C. M.; Utku, Senol; Wada, Ben K.

1993-01-01

In this work vibration control of statically indeterminate adaptive truss structures is investigated. Here, the actuators (i.e., length adjusting devices) that are used for vibration control, work against the axial forces caused by the inertial forces. In statically determinate adaptive trusses no axial force is induced by the actuation. The control problem in statically indeterminate trusses may be dominated by the actuation-induced axial element forces. The creation of actuation-induced axial forces puts the system to a higher energy state, thus aggravates the controls. It is shown that by the usage of sufficient number of slave actuators in addition to the actual control actuators, the actuation-induced axial element forces can be nullified, and the control problem of the statically indeterminate adaptive truss problem is reduced to that of a statically determinate one. It is also shown that the usage of slave actuators saves a great amount of control energy and provides robustness for the controls.

IRAC Full-Scale Flight Testbed Capabilities

NASA Technical Reports Server (NTRS)

Lee, James A.; Pahle, Joseph; Cogan, Bruce R.; Hanson, Curtis E.; Bosworth, John T.

2009-01-01

Overview: Provide validation of adaptive control law concepts through full scale flight evaluation in a representative avionics architecture. Develop an understanding of aircraft dynamics of current vehicles in damaged and upset conditions Real-world conditions include: a) Turbulence, sensor noise, feedback biases; and b) Coupling between pilot and adaptive system. Simulated damage includes 1) "B" matrix (surface) failures; and 2) "A" matrix failures. Evaluate robustness of control systems to anticipated and unanticipated failures.

Design of sewage treatment system by applying fuzzy adaptive PID controller

NASA Astrophysics Data System (ADS)

Jin, Liang-Ping; Li, Hong-Chan

2013-03-01

In the sewage treatment system, the dissolved oxygen concentration control, due to its nonlinear, time-varying, large time delay and uncertainty, is difficult to establish the exact mathematical model. While the conventional PID controller only works with good linear not far from its operating point, it is difficult to realize the system control when the operating point far off. In order to solve the above problems, the paper proposed a method which combine fuzzy control with PID methods and designed a fuzzy adaptive PID controller based on S7-300 PLC .It employs fuzzy inference method to achieve the online tuning for PID parameters. The control algorithm by simulation and practical application show that the system has stronger robustness and better adaptability.

Limb Position Tolerant Pattern Recognition for Myoelectric Prosthesis Control with Adaptive Sparse Representations From Extreme Learning.

PubMed

Betthauser, Joseph L; Hunt, Christopher L; Osborn, Luke E; Masters, Matthew R; Levay, Gyorgy; Kaliki, Rahul R; Thakor, Nitish V

2018-04-01

Myoelectric signals can be used to predict the intended movements of an amputee for prosthesis control. However, untrained effects like limb position changes influence myoelectric signal characteristics, hindering the ability of pattern recognition algorithms to discriminate among motion classes. Despite frequent and long training sessions, these deleterious conditional influences may result in poor performance and device abandonment. We present a robust sparsity-based adaptive classification method that is significantly less sensitive to signal deviations resulting from untrained conditions. We compare this approach in the offline and online contexts of untrained upper-limb positions for amputee and able-bodied subjects to demonstrate its robustness compared against other myoelectric classification methods. We report significant performance improvements () in untrained limb positions across all subject groups. The robustness of our suggested approach helps to ensure better untrained condition performance from fewer training conditions. This method of prosthesis control has the potential to deliver real-world clinical benefits to amputees: better condition-tolerant performance, reduced training burden in terms of frequency and duration, and increased adoption of myoelectric prostheses.

Stability Assessment and Tuning of an Adaptively Augmented Classical Controller for Launch Vehicle Flight Control

NASA Technical Reports Server (NTRS)

VanZwieten, Tannen; Zhu, J. Jim; Adami, Tony; Berry, Kyle; Grammar, Alex; Orr, Jeb S.; Best, Eric A.

2014-01-01

Recently, a robust and practical adaptive control scheme for launch vehicles [ [1] has been introduced. It augments a classical controller with a real-time loop-gain adaptation, and it is therefore called Adaptive Augmentation Control (AAC). The loop-gain will be increased from the nominal design when the tracking error between the (filtered) output and the (filtered) command trajectory is large; whereas it will be decreased when excitation of flex or sloshing modes are detected. There is a need to determine the range and rate of the loop-gain adaptation in order to retain (exponential) stability, which is critical in vehicle operation, and to develop some theoretically based heuristic tuning methods for the adaptive law gain parameters. The classical launch vehicle flight controller design technics are based on gain-scheduling, whereby the launch vehicle dynamics model is linearized at selected operating points along the nominal tracking command trajectory, and Linear Time-Invariant (LTI) controller design techniques are employed to ensure asymptotic stability of the tracking error dynamics, typically by meeting some prescribed Gain Margin (GM) and Phase Margin (PM) specifications. The controller gains at the design points are then scheduled, tuned and sometimes interpolated to achieve good performance and stability robustness under external disturbances (e.g. winds) and structural perturbations (e.g. vehicle modeling errors). While the GM does give a bound for loop-gain variation without losing stability, it is for constant dispersions of the loop-gain because the GM is based on frequency-domain analysis, which is applicable only for LTI systems. The real-time adaptive loop-gain variation of the AAC effectively renders the closed-loop system a time-varying system, for which it is well-known that the LTI system stability criterion is neither necessary nor sufficient when applying to a Linear Time-Varying (LTV) system in a frozen-time fashion. Therefore, a generalized stability metric for time-varying loop=gain perturbations is needed for the AAC.

Adaptive control of 5 DOF upper-limb exoskeleton robot with improved safety.

PubMed

Kang, Hao-Bo; Wang, Jian-Hui

2013-11-01

This paper studies an adaptive control strategy for a class of 5 DOF upper-limb exoskeleton robot with a special safety consideration. The safety requirement plays a critical role in the clinical treatment when assisting patients with shoulder, elbow and wrist joint movements. With the objective of assuring the tracking performance of the pre-specified operations, the proposed adaptive controller is firstly designed to be robust to the model uncertainties. To further improve the safety and fault-tolerance in the presence of unknown large parameter variances or even actuator faults, the adaptive controller is on-line updated according to the information provided by an adaptive observer without additional sensors. An output tracking performance is well achieved with a tunable error bound. The experimental example also verifies the effectiveness of the proposed control scheme. © 2013 ISA. Published by ISA. All rights reserved.

Space Launch System Implementation of Adaptive Augmenting Control

NASA Technical Reports Server (NTRS)

VanZwieten, Tannen S.; Wall, John H.; Orr, Jeb S.

2014-01-01

Given the complex structural dynamics, challenging ascent performance requirements, and rigorous flight certification constraints owing to its manned capability, the NASA Space Launch System (SLS) launch vehicle requires a proven thrust vector control algorithm design with highly optimized parameters to robustly demonstrate stable and high performance flight. On its development path to preliminary design review (PDR), the stability of the SLS flight control system has been challenged by significant vehicle flexibility, aerodynamics, and sloshing propellant dynamics. While the design has been able to meet all robust stability criteria, it has done so with little excess margin. Through significant development work, an adaptive augmenting control (AAC) algorithm previously presented by Orr and VanZwieten, has been shown to extend the envelope of failures and flight anomalies for which the SLS control system can accommodate while maintaining a direct link to flight control stability criteria (e.g. gain & phase margin). In this paper, the work performed to mature the AAC algorithm as a baseline component of the SLS flight control system is presented. The progress to date has brought the algorithm design to the PDR level of maturity. The algorithm has been extended to augment the SLS digital 3-axis autopilot, including existing load-relief elements, and necessary steps for integration with the production flight software prototype have been implemented. Several updates to the adaptive algorithm to increase its performance, decrease its sensitivity to expected external commands, and safeguard against limitations in the digital implementation are discussed with illustrating results. Monte Carlo simulations and selected stressing case results are shown to demonstrate the algorithm's ability to increase the robustness of the integrated SLS flight control system.

Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network

PubMed Central

Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui

2012-01-01

This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production. PMID:22778587

Robust Wave-front Correction in a Small Scale Adaptive Optics System Using a Membrane Deformable Mirror

NASA Astrophysics Data System (ADS)

Choi, Y.; Park, S.; Baik, S.; Jung, J.; Lee, S.; Yoo, J.

A small scale laboratory adaptive optics system using a Shack-Hartmann wave-front sensor (WFS) and a membrane deformable mirror (DM) has been built for robust image acquisition. In this study, an adaptive limited control technique is adopted to maintain the long-term correction stability of an adaptive optics system. To prevent the waste of dynamic correction range for correcting small residual wave-front distortions which are inefficient to correct, the built system tries to limit wave-front correction when a similar small difference wave-front pattern is repeatedly generated. Also, the effect of mechanical distortion in an adaptive optics system is studied and a pre-recognition method for the distortion is devised to prevent low-performance system operation. A confirmation process for a balanced work assignment among deformable mirror (DM) actuators is adopted for the pre-recognition. The corrected experimental results obtained by using a built small scale adaptive optics system are described in this paper.

Adaptive Augmenting Control Flight Characterization Experiment on an F/A-18

NASA Technical Reports Server (NTRS)

VanZwieten, Tannen S.; Gilligan, Eric T.; Wall, John H.; Orr, Jeb S.; Miller, Christopher J.; Hanson, Curtis E.

2014-01-01

The NASA Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed an Adaptive Augmenting Control (AAC) algorithm for launch vehicles that improves robustness and performance by adapting an otherwise welltuned classical control algorithm to unexpected environments or variations in vehicle dynamics. This AAC algorithm is currently part of the baseline design for the SLS Flight Control System (FCS), but prior to this series of research flights it was the only component of the autopilot design that had not been flight tested. The Space Launch System (SLS) flight software prototype, including the adaptive component, was recently tested on a piloted aircraft at Dryden Flight Research Center (DFRC) which has the capability to achieve a high level of dynamic similarity to a launch vehicle. Scenarios for the flight test campaign were designed specifically to evaluate the AAC algorithm to ensure that it is able to achieve the expected performance improvements with no adverse impacts in nominal or nearnominal scenarios. Having completed the recent series of flight characterization experiments on DFRC's F/A-18, the AAC algorithm's capability, robustness, and reproducibility, have been successfully demonstrated. Thus, the entire SLS control architecture has been successfully flight tested in a relevant environment. This has increased NASA's confidence that the autopilot design is ready to fly on the SLS Block I vehicle and will exceed the performance of previous architectures.

Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

NASA Technical Reports Server (NTRS)

Jorgensen, Charles C.

1997-01-01

A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

An advanced robust method for speed control of switched reluctance motor

NASA Astrophysics Data System (ADS)

Zhang, Chao; Ming, Zhengfeng; Su, Zhanping; Cai, Zhuang

2018-05-01

This paper presents an advanced robust controller for the speed system of a switched reluctance motor (SRM) in the presence of nonlinearities, speed ripple, and external disturbances. It proposes that the adaptive fuzzy control is applied to regulate the motor speed in the outer loop, and the detector is used to obtain rotor detection in the inner loop. The new fuzzy logic tuning rules are achieved from the experience of the operator and the knowledge of the specialist. The fuzzy parameters are automatically adjusted online according to the error and its change of speed in the transient period. The designed detector can obtain the rotor's position accurately in each phase module. Furthermore, a series of contrastive simulations are completed between the proposed controller and proportion integration differentiation controller including low speed, medium speed, and high speed. Simulations show that the proposed robust controller enables the system reduced by at least 3% in overshoot, 6% in rise time, and 20% in setting time, respectively, and especially under external disturbances. Moreover, an actual SRM control system is constructed at 220 V 370 W. The experiment results further prove that the proposed robust controller has excellent dynamic performance and strong robustness.

Design of a Model Reference Adaptive Controller for an Unmanned Air Vehicle

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Matsutani, Megumi; Annaswamy, Anuradha M.

2010-01-01

This paper presents the "Adaptive Control Technology for Safe Flight (ACTS)" architecture, which consists of a non-adaptive controller that provides satisfactory performance under nominal flying conditions, and an adaptive controller that provides robustness under off nominal ones. The design and implementation procedures of both controllers are presented. The aim of these procedures, which encompass both theoretical and practical considerations, is to develop a controller suitable for flight. The ACTS architecture is applied to the Generic Transport Model developed by NASA-Langley Research Center. The GTM is a dynamically scaled test model of a transport aircraft for which a flight-test article and a high-fidelity simulation are available. The nominal controller at the core of the ACTS architecture has a multivariable LQR-PI structure while the adaptive one has a direct, model reference structure. The main control surfaces as well as the throttles are used as control inputs. The inclusion of the latter alleviates the pilot s workload by eliminating the need for cancelling the pitch coupling generated by changes in thrust. Furthermore, the independent usage of the throttles by the adaptive controller enables their use for attitude control. Advantages and potential drawbacks of adaptation are demonstrated by performing high fidelity simulations of a flight-validated controller and of its adaptive augmentation.

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights.

PubMed

Luo, Shaohua; Wu, Songli; Gao, Ruizhen

2015-07-01

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.

Adaptive process control using fuzzy logic and genetic algorithms

NASA Technical Reports Server (NTRS)

Karr, C. L.

1993-01-01

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

Adaptive Process Control with Fuzzy Logic and Genetic Algorithms

NASA Technical Reports Server (NTRS)

Karr, C. L.

1993-01-01

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

Genetic algorithms in adaptive fuzzy control

NASA Technical Reports Server (NTRS)

Karr, C. Lucas; Harper, Tony R.

1992-01-01

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Shaohua; Department of Mechanical Engineering, Chongqing Aerospace Polytechnic, Chongqing, 400021; Wu, Songli

2015-07-15

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in themore » closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.« less

Intelligent flight control systems

NASA Technical Reports Server (NTRS)

Stengel, Robert F.

1993-01-01

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms.

Adaptive mechanism-based congestion control for networked systems

NASA Astrophysics Data System (ADS)

Liu, Zhi; Zhang, Yun; Chen, C. L. Philip

2013-03-01

In order to assure the communication quality in network systems with heavy traffic and limited bandwidth, a new ATRED (adaptive thresholds random early detection) congestion control algorithm is proposed for the congestion avoidance and resource management of network systems. Different to the traditional AQM (active queue management) algorithms, the control parameters of ATRED are not configured statically, but dynamically adjusted by the adaptive mechanism. By integrating with the adaptive strategy, ATRED alleviates the tuning difficulty of RED (random early detection) and shows a better control on the queue management, and achieve a more robust performance than RED under varying network conditions. Furthermore, a dynamic transmission control protocol-AQM control system using ATRED controller is introduced for the systematic analysis. It is proved that the stability of the network system can be guaranteed when the adaptive mechanism is finely designed. Simulation studies show the proposed ATRED algorithm achieves a good performance in varying network environments, which is superior to the RED and Gentle-RED algorithm, and providing more reliable service under varying network conditions.

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

PubMed

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Adaptive fractional order sliding mode control for Boost converter in the Battery/Supercapacitor HESS.

PubMed

Wang, Jianlin; Xu, Dan; Zhou, Huan; Zhou, Tao

2018-01-01

In this paper, an adaptive fractional order sliding mode control (AFSMC) scheme is designed for the current tracking control of the Boost-type converter in a Battery/Supercapacitor hybrid energy storage system (HESS). In order to stabilize the current, the adaptation rules based on state-observer and Lyapunov function are being designed. A fractional order sliding surface function is defined based on the tracking current error and adaptive rules. Furthermore, through fractional order analysis, the stability of the fractional order control system is proven, and the value of the fractional order (λ) is being investigated. In addition, the effectiveness of the proposed AFSMC strategy is being verified by numerical simulations. The advantages of good transient response and robustness to uncertainty are being indicated by this design, when compared with a conventional integer order sliding mode control system.

Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.

PubMed

Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

2011-04-01

This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

An Adaptive B-Spline Neural Network and Its Application in Terminal Sliding Mode Control for a Mobile Satcom Antenna Inertially Stabilized Platform.

PubMed

Zhang, Xiaolei; Zhao, Yan; Guo, Kai; Li, Gaoliang; Deng, Nianmao

2017-04-28

The mobile satcom antenna (MSA) enables a moving vehicle to communicate with a geostationary Earth orbit satellite. To realize continuous communication, the MSA should be aligned with the satellite in both sight and polarization all the time. Because of coupling effects, unknown disturbances, sensor noises and unmodeled dynamics existing in the system, the control system should have a strong adaptability. The significant features of terminal sliding mode control method are robustness and finite time convergence, but the robustness is related to the large switching control gain which is determined by uncertain issues and can lead to chattering phenomena. Neural networks can reduce the chattering and approximate nonlinear issues. In this work, a novel B-spline curve-based B-spline neural network (BSNN) is developed. The improved BSNN has the capability of shape changing and self-adaption. In addition, the output of the proposed BSNN is applied to approximate the nonlinear function in the system. The results of simulations and experiments are also compared with those of PID method, non-singularity fast terminal sliding mode (NFTSM) control and radial basis function (RBF) neural network-based NFTSM. It is shown that the proposed method has the best performance, with reliable control precision.

Adaptive control of large space structures using recursive lattice filters

NASA Technical Reports Server (NTRS)

Sundararajan, N.; Goglia, G. L.

1985-01-01

The use of recursive lattice filters for identification and adaptive control of large space structures is studied. Lattice filters were used to identify the structural dynamics model of the flexible structures. This identification model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures is control engaged. This type of validation scheme prevents instability when the overall loop is closed. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods. The method uses the Linear Quadratic Guassian/Loop Transfer Recovery (LQG/LTR) approach to ensure stability against unmodeled higher frequency modes and achieves the desired performance.

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

PubMed

Mahmoudi, Babak; Pohlmeyer, Eric A; Prins, Noeline W; Geng, Shijia; Sanchez, Justin C

2013-12-01

Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance. Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks. The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance. By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.

Joint U.S./Japan Conference on Adaptive Structures, 1st, Maui, HI, Nov. 13-15, 1990, Proceedings

NASA Technical Reports Server (NTRS)

Wada, Ben K. (Editor); Fanson, James L. (Editor); Miura, Koryo (Editor)

1991-01-01

The present volume of adaptive structures discusses the development of control laws for an orbiting tethered antenna/reflector system test scale model, the sizing of active piezoelectric struts for vibration suppression on a space-based interferometer, the control design of a space station mobile transporter with multiple constraints, and optimum configuration control of an intelligent truss structure. Attention is given to the formulation of full state feedback for infinite order structural systems, robustness issues in the design of smart structures, passive piezoelectric vibration damping, shape control experiments with a functional model for large optical reflectors, and a mathematical basis for the design optimization of adaptive trusses in precision control. Topics addressed include approaches to the optimal adaptive geometries of intelligent truss structures, the design of an automated manufacturing system for tubular smart structures, the Sandia structural control experiments, and the zero-gravity dynamics of space structures in parabolic aircraft flight.

Joint U.S./Japan Conference on Adaptive Structures, 1st, Maui, HI, Nov. 13-15, 1990, Proceedings

NASA Astrophysics Data System (ADS)

Wada, Ben K.; Fanson, James L.; Miura, Koryo

1991-11-01

The present volume of adaptive structures discusses the development of control laws for an orbiting tethered antenna/reflector system test scale model, the sizing of active piezoelectric struts for vibration suppression on a space-based interferometer, the control design of a space station mobile transporter with multiple constraints, and optimum configuration control of an intelligent truss structure. Attention is given to the formulation of full state feedback for infinite order structural systems, robustness issues in the design of smart structures, passive piezoelectric vibration damping, shape control experiments with a functional model for large optical reflectors, and a mathematical basis for the design optimization of adaptive trusses in precision control. Topics addressed include approaches to the optimal adaptive geometries of intelligent truss structures, the design of an automated manufacturing system for tubular smart structures, the Sandia structural control experiments, and the zero-gravity dynamics of space structures in parabolic aircraft flight.

Neural network based adaptive control for nonlinear dynamic regimes

NASA Astrophysics Data System (ADS)

Shin, Yoonghyun

Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

An adaptive deep-coupled GNSS/INS navigation system with hybrid pre-filter processing

NASA Astrophysics Data System (ADS)

Wu, Mouyan; Ding, Jicheng; Zhao, Lin; Kang, Yingyao; Luo, Zhibin

2018-02-01

The deep-coupling of a global navigation satellite system (GNSS) with an inertial navigation system (INS) can provide accurate and reliable navigation information. There are several kinds of deeply-coupled structures. These can be divided mainly into coherent and non-coherent pre-filter based structures, which have their own strong advantages and disadvantages, especially in accuracy and robustness. In this paper, the existing pre-filters of the deeply-coupled structures are analyzed and modified to improve them firstly. Then, an adaptive GNSS/INS deeply-coupled algorithm with hybrid pre-filters processing is proposed to combine the advantages of coherent and non-coherent structures. An adaptive hysteresis controller is designed to implement the hybrid pre-filters processing strategy. The simulation and vehicle test results show that the adaptive deeply-coupled algorithm with hybrid pre-filters processing can effectively improve navigation accuracy and robustness, especially in a GNSS-challenged environment.

How simple autonomous decisions evolve into robust behaviours? A review from neurorobotics, cognitive, self-organized and artificial immune systems fields.

PubMed

Fernandez-Leon, Jose A; Acosta, Gerardo G; Rozenfeld, Alejandro

2014-10-01

Researchers in diverse fields, such as in neuroscience, systems biology and autonomous robotics, have been intrigued by the origin and mechanisms for biological robustness. Darwinian evolution, in general, has suggested that adaptive mechanisms as a way of reaching robustness, could evolve by natural selection acting successively on numerous heritable variations. However, is this understanding enough for realizing how biological systems remain robust during their interactions with the surroundings? Here, we describe selected studies of bio-inspired systems that show behavioral robustness. From neurorobotics, cognitive, self-organizing and artificial immune system perspectives, our discussions focus mainly on how robust behaviors evolve or emerge in these systems, having the capacity of interacting with their surroundings. These descriptions are twofold. Initially, we introduce examples from autonomous robotics to illustrate how the process of designing robust control can be idealized in complex environments for autonomous navigation in terrain and underwater vehicles. We also include descriptions of bio-inspired self-organizing systems. Then, we introduce other studies that contextualize experimental evolution with simulated organisms and physical robots to exemplify how the process of natural selection can lead to the evolution of robustness by means of adaptive behaviors. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Intelligent failure-tolerant control

NASA Technical Reports Server (NTRS)

Stengel, Robert F.

1991-01-01

An overview of failure-tolerant control is presented, beginning with robust control, progressing through parallel and analytical redundancy, and ending with rule-based systems and artificial neural networks. By design or implementation, failure-tolerant control systems are 'intelligent' systems. All failure-tolerant systems require some degrees of robustness to protect against catastrophic failure; failure tolerance often can be improved by adaptivity in decision-making and control, as well as by redundancy in measurement and actuation. Reliability, maintainability, and survivability can be enhanced by failure tolerance, although each objective poses different goals for control system design. Artificial intelligence concepts are helpful for integrating and codifying failure-tolerant control systems, not as alternatives but as adjuncts to conventional design methods.

Computations of Aerodynamic Performance Databases Using Output-Based Refinement

NASA Technical Reports Server (NTRS)

Nemec, Marian; Aftosmis, Michael J.

2009-01-01

Objectives: Handle complex geometry problems; Control discretization errors via solution-adaptive mesh refinement; Focus on aerodynamic databases of parametric and optimization studies: 1. Accuracy: satisfy prescribed error bounds 2. Robustness and speed: may require over 105 mesh generations 3. Automation: avoid user supervision Obtain "expert meshes" independent of user skill; and Run every case adaptively in production settings.

From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks

PubMed Central

Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

2016-01-01

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains. PMID:26972968

From Cellular Attractor Selection to Adaptive Signal Control for Traffic Networks.

PubMed

Tian, Daxin; Zhou, Jianshan; Sheng, Zhengguo; Wang, Yunpeng; Ma, Jianming

2016-03-14

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

Adaptive Control of a Utility-Scale Wind Turbine Operating in Region 3

NASA Technical Reports Server (NTRS)

Frost, Susan A.; Balas, Mark J.; Wright, Alan D.

2009-01-01

Adaptive control techniques are well suited to nonlinear applications, such as wind turbines, which are difficult to accurately model and which have effects from poorly known operating environments. The turbulent and unpredictable conditions in which wind turbines operate create many challenges for their operation. In this paper, we design an adaptive collective pitch controller for a high-fidelity simulation of a utility scale, variable-speed horizontal axis wind turbine. The objective of the adaptive pitch controller in Region 3 is to regulate generator speed and reject step disturbances. The control objective is accomplished by collectively pitching the turbine blades. We use an extension of the Direct Model Reference Adaptive Control (DMRAC) approach to track a reference point and to reject persistent disturbances. The turbine simulation models the Controls Advanced Research Turbine (CART) of the National Renewable Energy Laboratory in Golden, Colorado. The CART is a utility-scale wind turbine which has a well-developed and extensively verified simulator. The adaptive collective pitch controller for Region 3 was compared in simulations with a bas celliansesical Proportional Integrator (PI) collective pitch controller. In the simulations, the adaptive pitch controller showed improved speed regulation in Region 3 when compared with the baseline PI pitch controller and it demonstrated robustness to modeling errors.

A Flight Control System for Small Unmanned Aerial Vehicle

NASA Astrophysics Data System (ADS)

Tunik, A. A.; Nadsadnaya, O. I.

2018-03-01

The program adaptation of the controller for the flight control system (FCS) of an unmanned aerial vehicle (UAV) is considered. Linearized flight dynamic models depend mainly on the true airspeed of the UAV, which is measured by the onboard air data system. This enables its use for program adaptation of the FCS over the full range of altitudes and velocities, which define the flight operating range. FCS with program adaptation, based on static feedback (SF), is selected. The SF parameters for every sub-range of the true airspeed are determined using the linear matrix inequality approach in the case of discrete systems for synthesis of a suboptimal robust H ∞-controller. The use of the Lagrange interpolation between true airspeed sub-ranges provides continuous adaptation. The efficiency of the proposed approach is shown against an example of the heading stabilization system.

A Direct Adaptive Control Approach in the Presence of Model Mismatch

NASA Technical Reports Server (NTRS)

Joshi, Suresh M.; Tao, Gang; Khong, Thuan

2009-01-01

This paper considers the problem of direct model reference adaptive control when the plant-model matching conditions are violated due to abnormal changes in the plant or incorrect knowledge of the plant's mathematical structure. The approach consists of direct adaptation of state feedback gains for state tracking, and simultaneous estimation of the plant-model mismatch. Because of the mismatch, the plant can no longer track the state of the original reference model, but may be able to track a new reference model that still provides satisfactory performance. The reference model is updated if the estimated plant-model mismatch exceeds a bound that is determined via robust stability and/or performance criteria. The resulting controller is a hybrid direct-indirect adaptive controller that offers asymptotic state tracking in the presence of plant-model mismatch as well as parameter deviations.

Robust adaptive control for a hybrid solid oxide fuel cell system

NASA Astrophysics Data System (ADS)

Snyder, Steven

2011-12-01

Solid oxide fuel cells (SOFCs) are electrochemical energy conversion devices. They offer a number of advantages beyond those of most other fuel cells due to their high operating temperature (800-1000°C), such as internal reforming, heat as a byproduct, and faster reaction kinetics without precious metal catalysts. Mitigating fuel starvation and improving load-following capabilities of SOFC systems are conflicting control objectives. However, this can be resolved by the hybridization of the system with an energy storage device, such as an ultra-capacitor. In this thesis, a steady-state property of the SOFC is combined with an input-shaping method in order to address the issue of fuel starvation. Simultaneously, an overall adaptive system control strategy is employed to manage the energy sharing between the elements as well as to maintain the state-of-charge of the energy storage device. The adaptive control method is robust to errors in the fuel cell's fuel supply system and guarantees that the fuel cell current and ultra-capacitor state-of-charge approach their target values and remain uniformly, ultimately bounded about these target values. Parameter saturation is employed to guarantee boundedness of the parameters. The controller is validated through hardware-in-the-loop experiments as well as computer simulations.

Robust adaptive fault-tolerant control for leader-follower flocking of uncertain multi-agent systems with actuator failure.

PubMed

Yazdani, Sahar; Haeri, Mohammad

2017-11-01

In this work, we study the flocking problem of multi-agent systems with uncertain dynamics subject to actuator failure and external disturbances. By considering some standard assumptions, we propose a robust adaptive fault tolerant protocol for compensating of the actuator bias fault, the partial loss of actuator effectiveness fault, the model uncertainties, and external disturbances. Under the designed protocol, velocity convergence of agents to that of virtual leader is guaranteed while the connectivity preservation of network and collision avoidance among agents are ensured as well. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive fractional order sliding mode control for Boost converter in the Battery/Supercapacitor HESS

PubMed Central

Xu, Dan; Zhou, Huan; Zhou, Tao

2018-01-01

In this paper, an adaptive fractional order sliding mode control (AFSMC) scheme is designed for the current tracking control of the Boost-type converter in a Battery/Supercapacitor hybrid energy storage system (HESS). In order to stabilize the current, the adaptation rules based on state-observer and Lyapunov function are being designed. A fractional order sliding surface function is defined based on the tracking current error and adaptive rules. Furthermore, through fractional order analysis, the stability of the fractional order control system is proven, and the value of the fractional order (λ) is being investigated. In addition, the effectiveness of the proposed AFSMC strategy is being verified by numerical simulations. The advantages of good transient response and robustness to uncertainty are being indicated by this design, when compared with a conventional integer order sliding mode control system. PMID:29702696

How protein materials balance strength, robustness, and adaptability

PubMed Central

Buehler, Markus J.; Yung, Yu Ching

2010-01-01

Proteins form the basis of a wide range of biological materials such as hair, skin, bone, spider silk, or cells, which play an important role in providing key functions to biological systems. The focus of this article is to discuss how protein materials are capable of balancing multiple, seemingly incompatible properties such as strength, robustness, and adaptability. To illustrate this, we review bottom-up materiomics studies focused on the mechanical behavior of protein materials at multiple scales, from nano to macro. We focus on alpha-helix based intermediate filament proteins as a model system to explain why the utilization of hierarchical structural features is vital to their ability to combine strength, robustness, and adaptability. Experimental studies demonstrating the activation of angiogenesis, the growth of new blood vessels, are presented as an example of how adaptability of structure in biological tissue is achieved through changes in gene expression that result in an altered material structure. We analyze the concepts in light of the universality and diversity of the structural makeup of protein materials and discuss the findings in the context of potential fundamental evolutionary principles that control their nanoscale structure. We conclude with a discussion of multiscale science in biology and de novo materials design. PMID:20676305

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering.

PubMed

Wu, Dongjin; Xia, Linyuan; Geng, Jijun

2018-06-19

Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.

Adaptive Control of Small Outboard-Powered Boats for Survey Applications

NASA Technical Reports Server (NTRS)

VanZwieten, T.S.; VanZwieten, J.H.; Fisher, A.D.

2009-01-01

Four autopilot controllers have been developed in this work that can both hold a desired heading and follow a straight line. These PID, adaptive PID, neuro-adaptive, and adaptive augmenting control algorithms have all been implemented into a numerical simulation of a 33-foot center console vessel with wind, waves, and current disturbances acting in the perpendicular (across-track) direction of the boat s desired trajectory. Each controller is tested for its ability to follow a desired heading in the presence of these disturbances and then to follow a straight line at two different throttle settings for the same disturbances. These controllers were tuned for an input thrust of 2000 N and all four controllers showed good performance with none of the controllers significantly outperforming the others when holding a constant heading and following a straight line at this engine thrust. Each controller was then tested for a reduced engine thrust of 1200 N per engine where each of the three adaptive controllers reduced heading error and across-track error by approximately 50% after a 300 second tuning period when compared to the fixed gain PID, showing that significant robustness to changes in throttle setting was gained by using an adaptive algorithm.

On the role of model-based monitoring for adaptive planning under uncertainty

NASA Astrophysics Data System (ADS)

Raso, Luciano; Kwakkel, Jan; Timmermans, Jos; Haasnoot, Mariolijn

2016-04-01

Adaptive plans, designed to anticipate and respond to an unfolding uncertain future, have found a fertile application domain in the planning of deltas that are exposed to rapid socioeconomic development and climate change. Adaptive planning, under the moniker of adaptive delta management, is used in the Dutch Delta Program for developing a nation-wide plan to prepare for uncertain climate change and socio-economic developments. Scientifically, adaptive delta management relies heavily on Dynamic Adaptive Policy Pathways. Currently, in the Netherlands the focus is shifting towards implementing the adaptive delta plan. This shift is especially relevant because the efficacy of adaptive plans hinges on monitoring on-going developments and ensuring that actions are indeed taken if and when necessary. In the design of an effective monitoring system for an adaptive plan, three challenges have to be confronted: • Shadow of the past: The development of adaptive plans and the design of their monitoring system relies heavily on current knowledge of the system, and current beliefs about plausible future developments. A static monitoring system is therefore exposed to the exact same uncertainties one tries to address through adaptive planning. • Inhibition of learning: Recent applications of adaptive planning tend to overlook the importance of learning and new information, and fail to account for this explicitly in the design of adaptive plans. • Challenge of surprise: Adaptive policies are designed in light of the current foreseen uncertainties. However, developments that are not considered during the design phase as being plausible could still substantially affect the performance of adaptive policies. The shadow of the past, the inhibition of learning, and the challenge of surprise taken together suggest that there is a need for redesigning the concepts of monitoring and evaluation to support the implementation of adaptive plans. Innovations from control theory, triggered by the challenge of uncertainty in operational control, may offer solutions from which monitoring for adaptive planning can benefit. Specifically: (i) in control, observations are incorporated into the model through data assimilation, updating the present state, boundary conditions, and parameters based on new observations, diminishing the shadow of the past; (ii) adaptive control is a way to modify the characteristics of the internal model, incorporating new knowledge on the system, countervailing the inhibition of learning; and (iii) in closed-loop control, a continuous system update equips the controller with "inherent robustness", i.e. to capacity to adapts to new conditions even when these were not initially considered. We aim to explore how inherent robustness addresses the challenge of surprise. Innovations in model-based control might help to improve and adapt the models used to support adaptive delta management to new information (reducing uncertainty). Moreover, this would offer a starting point for using these models not only in the design of adaptive plans, but also as part of the monitoring. The proposed research requires multidisciplinary cooperation between control theory, the policy sciences, and integrated assessment modeling.

Adaptive Strategies for Controls of Flexible Arms. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Yuan, Bau-San

1989-01-01

An adaptive controller for a modern manipulator has been designed based on asymptotical stability via the Lyapunov criterion with the output error between the system and a reference model used as the actuating control signal. Computer simulations were carried out to test the design. The combination of the adaptive controller and a system vibration and mode shape estimator show that the flexible arm should move along a pre-defined trajectory with high-speed motion and fast vibration setting time. An existing computer-controlled prototype two link manipulator, RALF (Robotic Arm, Large Flexible), with a parallel mechanism driven by hydraulic actuators was used to verify the mathematical analysis. The experimental results illustrate that assumed modes found from finite element techniques can be used to derive the equations of motion with acceptable accuracy. The robust adaptive (modal) control is implemented to compensate for unmodelled modes and nonlinearities and is compared with the joint feedback control in additional experiments. Preliminary results show promise for the experimental control algorithm.

In-Flight Suppression of an Unstable F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

NASA Technical Reports Server (NTRS)

VanZwieten, Tannen S.; Gilligan, Eric T.; Wall, John H.; Miller, Christopher J.; Hanson, Curtis E.; Orr, Jeb S.

2015-01-01

NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off-nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using post-flight frequency-domain reconstruction, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

Adaptive Fuzzy Hysteresis Band Current Controller for Four-Wire Shunt Active Filter

NASA Astrophysics Data System (ADS)

Hamoudi, F.; Chaghi, A.; Amimeur, H.; Merabet, E.

2008-06-01

This paper presents an adaptive fuzzy hysteresis band current controller for four-wire shunt active power filters to eliminate harmonics and to compensate reactive power in distribution systems in order to keep currents at the point of common coupling sinusoidal and in phase with the corresponding voltage and the cancel neutral current. The conventional hysteresis band known for its robustness and its advantage in current controlled applications is adapted with a fuzzy logic controller to change the bandwidth according to the operating point in order to keep the frequency modulation at tolerable limits. The algorithm used to identify the reference currents is based on the synchronous reference frame theory (dqγ). Finally, simulation results using Matlab/Simulink are given to validate the proposed control.

Indirect adaptive soft computing based wavelet-embedded control paradigms for WT/PV/SOFC in a grid/charging station connected hybrid power system.

PubMed

Mumtaz, Sidra; Khan, Laiq; Ahmed, Saghir; Bader, Rabiah

2017-01-01

This paper focuses on the indirect adaptive tracking control of renewable energy sources in a grid-connected hybrid power system. The renewable energy systems have low efficiency and intermittent nature due to unpredictable meteorological conditions. The domestic load and the conventional charging stations behave in an uncertain manner. To operate the renewable energy sources efficiently for harvesting maximum power, instantaneous nonlinear dynamics should be captured online. A Chebyshev-wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control paradigm is proposed for variable speed wind turbine-permanent synchronous generator (VSWT-PMSG). A Hermite-wavelet incorporated NeuroFuzzy indirect adaptive MPPT control strategy for photovoltaic (PV) system to extract maximum power and indirect adaptive tracking control scheme for Solid Oxide Fuel Cell (SOFC) is developed. A comprehensive simulation test-bed for a grid-connected hybrid power system is developed in Matlab/Simulink. The robustness of the suggested indirect adaptive control paradigms are evaluated through simulation results in a grid-connected hybrid power system test-bed by comparison with conventional and intelligent control techniques. The simulation results validate the effectiveness of the proposed control paradigms.

Indirect adaptive soft computing based wavelet-embedded control paradigms for WT/PV/SOFC in a grid/charging station connected hybrid power system

PubMed Central

Khan, Laiq; Ahmed, Saghir; Bader, Rabiah

2017-01-01

This paper focuses on the indirect adaptive tracking control of renewable energy sources in a grid-connected hybrid power system. The renewable energy systems have low efficiency and intermittent nature due to unpredictable meteorological conditions. The domestic load and the conventional charging stations behave in an uncertain manner. To operate the renewable energy sources efficiently for harvesting maximum power, instantaneous nonlinear dynamics should be captured online. A Chebyshev-wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control paradigm is proposed for variable speed wind turbine-permanent synchronous generator (VSWT-PMSG). A Hermite-wavelet incorporated NeuroFuzzy indirect adaptive MPPT control strategy for photovoltaic (PV) system to extract maximum power and indirect adaptive tracking control scheme for Solid Oxide Fuel Cell (SOFC) is developed. A comprehensive simulation test-bed for a grid-connected hybrid power system is developed in Matlab/Simulink. The robustness of the suggested indirect adaptive control paradigms are evaluated through simulation results in a grid-connected hybrid power system test-bed by comparison with conventional and intelligent control techniques. The simulation results validate the effectiveness of the proposed control paradigms. PMID:28877191

Learning-based adaptive prescribed performance control of postcapture space robot-target combination without inertia identifications

NASA Astrophysics Data System (ADS)

Wei, Caisheng; Luo, Jianjun; Dai, Honghua; Bian, Zilin; Yuan, Jianping

2018-05-01

In this paper, a novel learning-based adaptive attitude takeover control method is investigated for the postcapture space robot-target combination with guaranteed prescribed performance in the presence of unknown inertial properties and external disturbance. First, a new static prescribed performance controller is developed to guarantee that all the involved attitude tracking errors are uniformly ultimately bounded by quantitatively characterizing the transient and steady-state performance of the combination. Then, a learning-based supplementary adaptive strategy based on adaptive dynamic programming is introduced to improve the tracking performance of static controller in terms of robustness and adaptiveness only utilizing the input/output data of the combination. Compared with the existing works, the prominent advantage is that the unknown inertial properties are not required to identify in the development of learning-based adaptive control law, which dramatically decreases the complexity and difficulty of the relevant controller design. Moreover, the transient and steady-state performance is guaranteed a priori by designer-specialized performance functions without resorting to repeated regulations of the controller parameters. Finally, the three groups of illustrative examples are employed to verify the effectiveness of the proposed control method.

Geometry Modeling and Adaptive Control of Air-Breathing Hypersonic Vehicles

NASA Astrophysics Data System (ADS)

Vick, Tyler Joseph

Air-breathing hypersonic vehicles have the potential to provide global reach and affordable access to space. Recent technological advancements have made scramjet-powered flight achievable, as evidenced by the successes of the X-43A and X-51A flight test programs over the last decade. Air-breathing hypersonic vehicles present unique modeling and control challenges in large part due to the fact that scramjet propulsion systems are highly integrated into the airframe, resulting in strongly coupled and often unstable dynamics. Additionally, the extreme flight conditions and inability to test fully integrated vehicle systems larger than X-51 before flight leads to inherent uncertainty in hypersonic flight. This thesis presents a means to design vehicle geometries, simulate vehicle dynamics, and develop and analyze control systems for hypersonic vehicles. First, a software tool for generating three-dimensional watertight vehicle surface meshes from simple design parameters is developed. These surface meshes are compatible with existing vehicle analysis tools, with which databases of aerodynamic and propulsive forces and moments can be constructed. A six-degree-of-freedom nonlinear dynamics simulation model which incorporates this data is presented. Inner-loop longitudinal and lateral control systems are designed and analyzed utilizing the simulation model. The first is an output feedback proportional-integral linear controller designed using linear quadratic regulator techniques. The second is a model reference adaptive controller (MRAC) which augments this baseline linear controller with an adaptive element. The performance and robustness of each controller are analyzed through simulated time responses to angle-of-attack and bank angle commands, while various uncertainties are introduced. The MRAC architecture enables the controller to adapt in a nonlinear fashion to deviations from the desired response, allowing for improved tracking performance, stability, and robustness.

Adaptive Tracking Control for Robots With an Interneural Computing Scheme.

PubMed

Tsai, Feng-Sheng; Hsu, Sheng-Yi; Shih, Mau-Hsiang

2018-04-01

Adaptive tracking control of mobile robots requires the ability to follow a trajectory generated by a moving target. The conventional analysis of adaptive tracking uses energy minimization to study the convergence and robustness of the tracking error when the mobile robot follows a desired trajectory. However, in the case that the moving target generates trajectories with uncertainties, a common Lyapunov-like function for energy minimization may be extremely difficult to determine. Here, to solve the adaptive tracking problem with uncertainties, we wish to implement an interneural computing scheme in the design of a mobile robot for behavior-based navigation. The behavior-based navigation adopts an adaptive plan of behavior patterns learning from the uncertainties of the environment. The characteristic feature of the interneural computing scheme is the use of neural path pruning with rewards and punishment interacting with the environment. On this basis, the mobile robot can be exploited to change its coupling weights in paths of neural connections systematically, which can then inhibit or enhance the effect of flow elimination in the dynamics of the evolutionary neural network. Such dynamical flow translation ultimately leads to robust sensory-to-motor transformations adapting to the uncertainties of the environment. A simulation result shows that the mobile robot with the interneural computing scheme can perform fault-tolerant behavior of tracking by maintaining suitable behavior patterns at high frequency levels.

Robust leader-follower formation tracking control of multiple underactuated surface vessels

NASA Astrophysics Data System (ADS)

Peng, Zhou-hua; Wang, Dan; Lan, Wei-yao; Sun, Gang

2012-09-01

This paper is concerned with the formation control problem of multiple underactuated surface vessels moving in a leader-follower formation. The formation is achieved by the follower to track a virtual target defined relative to the leader. A robust adaptive target tracking law is proposed by using neural network and backstepping techniques. The advantage of the proposed control scheme is that the uncertain nonlinear dynamics caused by Coriolis/centripetal forces, nonlinear damping, unmodeled hydrodynamics and disturbances from the environment can be compensated by on line learning. Based on Lyapunov analysis, the proposed controller guarantees the tracking errors converge to a small neighborhood of the origin. Simulation results demonstrate the effectiveness of the control strategy.

Self-tuning control of attitude and momentum management for the Space Station

NASA Technical Reports Server (NTRS)

Shieh, L. S.; Sunkel, J. W.; Yuan, Z. Z.; Zhao, X. M.

1992-01-01

This paper presents a hybrid state-space self-tuning design methodology using dual-rate sampling for suboptimal digital adaptive control of attitude and momentum management for the Space Station. This new hybrid adaptive control scheme combines an on-line recursive estimation algorithm for indirectly identifying the parameters of a continuous-time system from the available fast-rate sampled data of the inputs and states and a controller synthesis algorithm for indirectly finding the slow-rate suboptimal digital controller from the designed optimal analog controller. The proposed method enables the development of digitally implementable control algorithms for the robust control of Space Station Freedom with unknown environmental disturbances and slowly time-varying dynamics.

Simple robust control laws for robot manipulators. Part 2: Adaptive case

NASA Technical Reports Server (NTRS)

Bayard, D. S.; Wen, J. T.

1987-01-01

A new class of asymptotically stable adaptive control laws is introduced for application to the robotic manipulator. Unlike most applications of adaptive control theory to robotic manipulators, this analysis addresses the nonlinear dynamics directly without approximation, linearization, or ad hoc assumptions, and utilizes a parameterization based on physical (time-invariant) quantities. This approach is made possible by using energy-like Lyapunov functions which retain the nonlinear character and structure of the dynamics, rather than simple quadratic forms which are ubiquitous to the adaptive control literature, and which have bound the theory tightly to linear systems with unknown parameters. It is a unique feature of these results that the adaptive forms arise by straightforward certainty equivalence adaptation of their nonadaptive counterparts found in the companion to this paper (i.e., by replacing unknown quantities by their estimates) and that this simple approach leads to asymptotically stable closed-loop adaptive systems. Furthermore, it is emphasized that this approach does not require convergence of the parameter estimates (i.e., via persistent excitation), invertibility of the mass matrix estimate, or measurement of the joint accelerations.

Accounting for sequential trial effects in the flanker task: conflict adaptation or associative priming?

PubMed

Nieuwenhuis, Sander; Stins, John F; Posthuma, Danielle; Polderman, Tinca J C; Boomsma, Dorret I; de Geus, Eco J

2006-09-01

The conflict-control loop theory proposes that the detection of conflict in information processing triggers an increase in cognitive control, resulting in improved performance on the subsequent trial. This theory seems consistent with the robust finding that conflict susceptibility is reduced following correct trials associated with high conflict: the conflict adaptation effect. However, despite providing favorable conditions for eliciting and detecting conflict-triggered performance adjustments, none of the five experiments reported here provide unequivocal evidence of such adjustments. Instead, the results corroborate and extend earlier findings by demonstrating that the conflict adaptation effect, at least in the flanker task, is only present for a specific subset of trial sequences that is characterized by a response repetition. This pattern of results provides strong evidence that the conflict adaptation effect reflects associative stimulus-response priming instead of conflict-driven adaptations in cognitive control.

Adaptive fixed-time trajectory tracking control of a stratospheric airship.

PubMed

Zheng, Zewei; Feroskhan, Mir; Sun, Liang

2018-05-01

This paper addresses the fixed-time trajectory tracking control problem of a stratospheric airship. By extending the method of adding a power integrator to a novel adaptive fixed-time control method, the convergence of a stratospheric airship to its reference trajectory is guaranteed to be achieved within a fixed time. The control algorithm is firstly formulated without the consideration of external disturbances to establish the stability of the closed-loop system in fixed-time and demonstrate that the convergence time of the airship is essentially independent of its initial conditions. Subsequently, a smooth adaptive law is incorporated into the proposed fixed-time control framework to provide the system with robustness to external disturbances. Theoretical analyses demonstrate that under the adaptive fixed-time controller, the tracking errors will converge towards a residual set in fixed-time. The results of a comparative simulation study with other recent methods illustrate the remarkable performance and superiority of the proposed control method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Mechanisms for Robust Cognition.

PubMed

Walsh, Matthew M; Gluck, Kevin A

2015-08-01

To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness in cognitive systems. We identify three mechanisms that enhance robustness in biological and engineered systems: system control, redundancy, and adaptability. After surveying the psychological literature for evidence of these mechanisms, we provide simulations illustrating how each contributes to robust cognition in a different psychological domain: psychomotor vigilance, semantic memory, and strategy selection. These simulations highlight features of a mathematical approach for quantifying robustness, and they provide concrete examples of mechanisms for robust cognition. © 2014 Cognitive Science Society, Inc.

Design of robust adaptive controller and feedback error learning for rehabilitation in Parkinson's disease: a simulation study.

PubMed

Rouhollahi, Korosh; Emadi Andani, Mehran; Karbassi, Seyed Mahdi; Izadi, Iman

2017-02-01

Deep brain stimulation (DBS) is an efficient therapy to control movement disorders of Parkinson's tremor. Stimulation of one area of basal ganglia (BG) by DBS with no feedback is the prevalent opinion. Reduction of additional stimulatory signal delivered to the brain is the advantage of using feedback. This results in reduction of side effects caused by the excessive stimulation intensity. In fact, the stimulatory intensity of controllers is decreased proportional to reduction of hand tremor. The objective of this study is to design a new controller structure to decrease three indicators: (i) the hand tremor; (ii) the level of delivered stimulation in disease condition; and (iii) the ratio of the level of delivered stimulation in health condition to disease condition. For this purpose, the authors offer a new closed-loop control structure to stimulate two areas of BG simultaneously. One area (STN: subthalamic nucleus) is stimulated by an adaptive controller with feedback error learning. The other area (GPi: globus pallidus internal) is stimulated by a partial state feedback (PSF) controller. Considering the three indicators, the results show that, stimulating two areas simultaneously leads to better performance compared with stimulating one area only. It is shown that both PSF and adaptive controllers are robust regarding system parameter uncertainties. In addition, a method is proposed to update the parameters of the BG model in real time. As a result, the parameters of the controllers can be updated based on the new parameters of the BG model.

Robust tracking and distributed synchronization control of a multi-motor servomechanism with H-infinity performance.

PubMed

Wang, Minlin; Ren, Xuemei; Chen, Qiang

2018-01-01

The multi-motor servomechanism (MMS) is a multi-variable, high coupling and nonlinear system, which makes the controller design challenging. In this paper, an adaptive robust H-infinity control scheme is proposed to achieve both the load tracking and multi-motor synchronization of MMS. This control scheme consists of two parts: a robust tracking controller and a distributed synchronization controller. The robust tracking controller is constructed by incorporating a neural network (NN) K-filter observer into the dynamic surface control, while the distributed synchronization controller is designed by combining the mean deviation coupling control strategy with the distributed technique. The proposed control scheme has several merits: 1) by using the mean deviation coupling synchronization control strategy, the tracking controller and the synchronization controller can be designed individually without any coupling problem; 2) the immeasurable states and unknown nonlinearities are handled by a NN K-filter observer, where the number of NN weights is largely reduced by using the minimal learning parameter technique; 3) the H-infinity performances of tracking error and synchronization error are guaranteed by introducing a robust term into the tracking controller and the synchronization controller, respectively. The stabilities of the tracking and synchronization control systems are analyzed by the Lyapunov theory. Simulation and experimental results based on a four-motor servomechanism are conducted to demonstrate the effectiveness of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Computationally inexpensive approach for pitch control of offshore wind turbine on barge floating platform.

PubMed

Zuo, Shan; Song, Y D; Wang, Lei; Song, Qing-wang

2013-01-01

Offshore floating wind turbine (OFWT) has gained increasing attention during the past decade because of the offshore high-quality wind power and complex load environment. The control system is a tradeoff between power tracking and fatigue load reduction in the above-rated wind speed area. In allusion to the external disturbances and uncertain system parameters of OFWT due to the proximity to load centers and strong wave coupling, this paper proposes a computationally inexpensive robust adaptive control approach with memory-based compensation for blade pitch control. The method is tested and compared with a baseline controller and a conventional individual blade pitch controller with the "NREL offshore 5 MW baseline wind turbine" being mounted on a barge platform run on FAST and Matlab/Simulink, operating in the above-rated condition. It is shown that the advanced control approach is not only robust to complex wind and wave disturbances but adaptive to varying and uncertain system parameters as well. The simulation results demonstrate that the proposed method performs better in reducing power fluctuations, fatigue loads and platform vibration as compared to the conventional individual blade pitch control.

Robustly stable adaptive control of a tandem of master-slave robotic manipulators with force reflection by using a multiestimation scheme.

PubMed

Ibeas, Asier; de la Sen, Manuel

2006-10-01

The problem of controlling a tandem of robotic manipulators composing a teleoperation system with force reflection is addressed in this paper. The final objective of this paper is twofold: 1) to design a robust control law capable of ensuring closed-loop stability for robots with uncertainties and 2) to use the so-obtained control law to improve the tracking of each robot to its corresponding reference model in comparison with previously existing controllers when the slave is interacting with the obstacle. In this way, a multiestimation-based adaptive controller is proposed. Thus, the master robot is able to follow more accurately the constrained motion defined by the slave when interacting with an obstacle than when a single-estimation-based controller is used, improving the transparency property of the teleoperation scheme. The closed-loop stability is guaranteed if a minimum residence time, which might be updated online when unknown, between different controller parameterizations is respected. Furthermore, the analysis of the teleoperation and stability capabilities of the overall scheme is carried out. Finally, some simulation examples showing the working of the multiestimation scheme complete this paper.

Computationally Inexpensive Approach for Pitch Control of Offshore Wind Turbine on Barge Floating Platform

PubMed Central

Zuo, Shan; Song, Y. D.; Wang, Lei; Song, Qing-wang

2013-01-01

Offshore floating wind turbine (OFWT) has gained increasing attention during the past decade because of the offshore high-quality wind power and complex load environment. The control system is a tradeoff between power tracking and fatigue load reduction in the above-rated wind speed area. In allusion to the external disturbances and uncertain system parameters of OFWT due to the proximity to load centers and strong wave coupling, this paper proposes a computationally inexpensive robust adaptive control approach with memory-based compensation for blade pitch control. The method is tested and compared with a baseline controller and a conventional individual blade pitch controller with the “NREL offshore 5 MW baseline wind turbine” being mounted on a barge platform run on FAST and Matlab/Simulink, operating in the above-rated condition. It is shown that the advanced control approach is not only robust to complex wind and wave disturbances but adaptive to varying and uncertain system parameters as well. The simulation results demonstrate that the proposed method performs better in reducing power fluctuations, fatigue loads and platform vibration as compared to the conventional individual blade pitch control. PMID:24453834

Context-specific adjustment of cognitive control: Transfer of adaptive control sets.

PubMed

Surrey, Caroline; Dreisbach, Gesine; Fischer, Rico

2017-11-01

Cognitive control protects processing of relevant information from interference by irrelevant information. The level of this processing selectivity can be flexibly adjusted to different control demands (e.g., frequency of conflict) associated with a certain context, leading to the formation of specific context-control associations. In the present study we investigated the robustness and transferability of the acquired context-control demands to new situations. In three experiments, we used a version of the context-specific proportion congruence (CSPC) paradigm, in which each context (e.g., location) is associated with a specific conflict frequency, determining high and low control demands. In a learning phase, associations between context and control demands were established. In a subsequent transfer block, stimulus-response mappings, whole task sets, or context-control demands changed. Results showed an impressive robustness of context-control associations, as context-specific adjustments of control from the learning phase were virtually unaffected by new stimuli and tasks in the transfer block. Only a change of the context-control demand eliminated the context-specific adjustment of control. These findings suggest that context-control associations that have proven to be adaptive in the past are continuously applied despite major changes in the task structure as long as the context-control associations remain the same.

Lessons from applied ecology: cancer control using an evolutionary double bind.

PubMed

Gatenby, Robert A; Brown, Joel; Vincent, Thomas

2009-10-01

Because the metastatic cascade is largely governed by the ability of malignant cells to adapt and proliferate at the distant tissue site, we propose that disseminated cancers are analogous in many important ways to the evolutionary and ecological dynamics of exotic species. Although pests can be decimated through the application of chemical toxins, this strategy virtually never achieves robust control as evolution of resistant phenotypes typically permits population recovery to pretreatment levels. In general, biological strategies that introduce predators, parasitoids, or pathogens have achieved more durable control of pest populations even after emergence of resistant phenotypes. From this we propose that long term outcome from any treatment strategy for invasive pests, including cancer, is not limited by evolution of resistance, but rather by the phenotypic cost of that resistance. If a cancerous cell's adaptation to therapy is achieved by upregulating xenobiotic metabolism or a redundant signaling pathway, the required investment in resources is small, and the original malignant phenotype remains essentially intact. As a result, the cancer cells' initial high level of fitness is little changed and unconstrained proliferation will resume once resistance evolves. Robust population control is possible if resistance to therapy requires a substantial and costly phenotypic adaptation that also significantly reduces the organism's fitness in its original niche: an evolutionary double bind.

Robust current control-based generalized predictive control with sliding mode disturbance compensation for PMSM drives.

PubMed

Liu, Xudong; Zhang, Chenghui; Li, Ke; Zhang, Qi

2017-11-01

This paper addresses the current control of permanent magnet synchronous motor (PMSM) for electric drives with model uncertainties and disturbances. A generalized predictive current control method combined with sliding mode disturbance compensation is proposed to satisfy the requirement of fast response and strong robustness. Firstly, according to the generalized predictive control (GPC) theory based on the continuous time model, a predictive current control method is presented without considering the disturbance, which is convenient to be realized in the digital controller. In fact, it's difficult to derive the exact motor model and parameters in the practical system. Thus, a sliding mode disturbance compensation controller is studied to improve the adaptiveness and robustness of the control system. The designed controller attempts to combine the merits of both predictive control and sliding mode control, meanwhile, the controller parameters are easy to be adjusted. Lastly, the proposed controller is tested on an interior PMSM by simulation and experiment, and the results indicate that it has good performance in both current tracking and disturbance rejection. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Aerial robot intelligent control method based on back-stepping

NASA Astrophysics Data System (ADS)

Zhou, Jian; Xue, Qian

2018-05-01

The aerial robot is characterized as strong nonlinearity, high coupling and parameter uncertainty, a self-adaptive back-stepping control method based on neural network is proposed in this paper. The uncertain part of the aerial robot model is compensated online by the neural network of Cerebellum Model Articulation Controller and robust control items are designed to overcome the uncertainty error of the system during online learning. At the same time, particle swarm algorithm is used to optimize and fix parameters so as to improve the dynamic performance, and control law is obtained by the recursion of back-stepping regression. Simulation results show that the designed control law has desired attitude tracking performance and good robustness in case of uncertainties and large errors in the model parameters.

Hybrid Active/Passive Control of Sound Radiation from Panels with Constrained Layer Damping and Model Predictive Feedback Control

NASA Technical Reports Server (NTRS)

Cabell, Randolph H.; Gibbs, Gary P.

2000-01-01

There has been considerable interest over the past several years in applying feedback control methods to problems of structural acoustics. One problem of particular interest is the control of sound radiation from aircraft panels excited on one side by a turbulent boundary layer (TBL). TBL excitation appears as many uncorrelated sources acting on the panel, which makes it difficult to find a single reference signal that is coherent with the excitation. Feedback methods have no need for a reference signal, and are thus suited to this problem. Some important considerations for the structural acoustics problem include the fact that the required controller bandwidth can easily extend to several hundred Hertz, so a digital controller would have to operate at a few kilohertz. In addition, aircraft panel structures have a reasonably high modal density over this frequency range. A model based controller must therefore handle the modally dense system, or have some way to reduce the bandwidth of the problem. Further complicating the problem is the fact that the stiffness and dynamic properties of an aircraft panel can vary considerably during flight due to altitude changes resulting in significant resonant frequency shifts. These considerations make the tradeoff between robustness to changes in the system being controlled and controller performance especially important. Recent papers concerning the design and implementation of robust controllers for structural acoustic problems highlight the need to consider both performance and robustness when designing the controller. While robust control methods such as H1 can be used to balance performance and robustness, their implementation is not easy and requires assumptions about the types of uncertainties in the plant being controlled. Achieving a useful controller design may require many tradeoff studies of different types of parametric uncertainties in the system. Another approach to achieving robustness to plant changes is to make the controller adaptive. For example, a mathematical model of the plant could be periodically updated as the plant changes, and the feedback gains recomputed from the updated model. To be practical, this approach requires a simple plant model that can be updated quickly with reasonable computational requirements. A recent paper by the authors discussed one way to simplify a feedback controller, by reducing the number of actuators and sensors needed for good performance. The work was done on a tensioned aircraft-style panel excited on one side by TBL flow in a low speed wind tunnel. Actuation was provided by a piezoelectric (PZT) actuator mounted on the center of the panel. For sensing, the responses of four accelerometers, positioned to approximate the response of the first radiation mode of the panel, were summed and fed back through the controller. This single input-single output topology was found to have nearly the same noise reduction performance as a controller with fifteen accelerometers and three PZT patches. This paper extends the previous results by looking at how constrained layer damping (CLD) on a panel can be used to enhance the performance of the feedback controller thus providing a more robust and efficient hybrid active/passive system. The eventual goal is to use the CLD to reduce sound radiation at high frequencies, then implement a very simple, reduced order, low sample rate adaptive controller to attenuate sound radiation at low frequencies. Additionally this added damping smoothes phase transitions over the bandwidth which promotes robustness to natural frequency shifts. Experiments were conducted in a transmission loss facility on a clamped-clamped aluminum panel driven on one side by a loudspeaker. A generalized predictive control (GPC) algorithm, which is suited to online adaptation of its parameters, was used in single input-single output and multiple input-single output configurations. Because this was a preliminary look at the potential constrained layer damping for adaptive control, static feedback control with no online adaptation was used. Two configurations of CLD in addition to a bare panel configuration were studied. For each CLD configuration, two sensor arrangements for the feedback controller were compared. The first arrangement used fifteen accelerometers on the panel to estimate the responses of the first six radiation modes of the panel. The second sensor arrangement was simpler, using the summed responses of only four accelerometers to approximate the response of the first radiation mode of the panel. In all cases a PZT patch was mounted at the center of the panel for control input. The performance of the controller was quantified using the responses of the fifteen accelerometers on the panel to estimate radiated sound power. The paper begins with a brief discussion of the GPC algorithm and the experimental setup. The experimental results are discussed next, comparing the CLD and sensor configurations, followed by discussion and conclusions.

Neural-Network-Based Adaptive Decentralized Fault-Tolerant Control for a Class of Interconnected Nonlinear Systems.

PubMed

Li, Xiao-Jian; Yang, Guang-Hong

2018-01-01

This paper is concerned with the adaptive decentralized fault-tolerant tracking control problem for a class of uncertain interconnected nonlinear systems with unknown strong interconnections. An algebraic graph theory result is introduced to address the considered interconnections. In addition, to achieve the desirable tracking performance, a neural-network-based robust adaptive decentralized fault-tolerant control (FTC) scheme is given to compensate the actuator faults and system uncertainties. Furthermore, via the Lyapunov analysis method, it is proven that all the signals of the resulting closed-loop system are semiglobally bounded, and the tracking errors of each subsystem exponentially converge to a compact set, whose radius is adjustable by choosing different controller design parameters. Finally, the effectiveness and advantages of the proposed FTC approach are illustrated with two simulated examples.

Inspiration for wing design: how forelimb specialization enables active flight in modern vertebrates.

PubMed

Chin, Diana D; Matloff, Laura Y; Stowers, Amanda Kay; Tucci, Emily R; Lentink, David

2017-06-01

Harnessing flight strategies refined by millions of years of evolution can help expedite the design of more efficient, manoeuvrable and robust flying robots. This review synthesizes recent advances and highlights remaining gaps in our understanding of how bird and bat wing adaptations enable effective flight. Included in this discussion is an evaluation of how current robotic analogues measure up to their biological sources of inspiration. Studies of vertebrate wings have revealed skeletal systems well suited for enduring the loads required during flight, but the mechanisms that drive coordinated motions between bones and connected integuments remain ill-described. Similarly, vertebrate flight muscles have adapted to sustain increased wing loading, but a lack of in vivo studies limits our understanding of specific muscular functions. Forelimb adaptations diverge at the integument level, but both bird feathers and bat membranes yield aerodynamic surfaces with a level of robustness unparalleled by engineered wings. These morphological adaptations enable a diverse range of kinematics tuned for different flight speeds and manoeuvres. By integrating vertebrate flight specializations-particularly those that enable greater robustness and adaptability-into the design and control of robotic wings, engineers can begin narrowing the wide margin that currently exists between flying robots and vertebrates. In turn, these robotic wings can help biologists create experiments that would be impossible in vivo . © 2017 The Author(s).

Safety assurance of non-deterministic flight controllers in aircraft applications

NASA Astrophysics Data System (ADS)

Noriega, Alfonso

Loss of control is a serious problem in aviation that primarily affects General Aviation. Technological advancements can help mitigate the problem, but the FAA certification process makes certain solutions economically unfeasible. This investigation presents the design of a generic adaptive autopilot that could potentially lead to a single certification for use in several makes and models of aircraft. The autopilot consists of a conventional controller connected in series with a robust direct adaptive model reference controller. In this architecture, the conventional controller is tuned once to provide outer-loop guidance and navigation to a reference model. The adaptive controller makes unknown aircraft behave like the reference model, allowing the conventional controller to successfully provide navigation without the need for retuning. A strong theoretical foundation is presented as an argument for the safety and stability of the controller. The stability proof of direct adaptive controllers require that the plant being controlled has no unstable transmission zeros and has a nonzero high frequency gain. Because most conventional aircraft do not readily meet these requirements, a process known as sensor blending was used. Sensor blending consists of using a linear combination of the plant's outputs that has no unstable transmission zeros and has a nonzero high frequency gain to drive the adaptive controller. Although this method does not present a problem for regulators, it can lead to a steady state error in tracking applications. The sensor blending theory was expanded to take advantage of the system's dynamics to allow for zero steady state error tracking. This method does not need knowledge of the specific system's dynamics, but instead uses the structure of the A and B matrices to perform the blending for the general case. The generic adaptive autopilot was tested in two high-fidelity nonlinear simulators of two typical General Aviation aircraft. The results show that the autopilot was able to adapt appropriately to the different aircraft and was able to perform three-dimensional navigation and an ILS approach, without any modification to the controller. The autopilot was tested in moderate atmospheric turbulence, using consumer-grade sensors and actuators currently available in General Aviation aircraft. The generic adaptive autopilot was shown to be robust to atmospheric turbulence and sensor and actuator random noise. In both aircraft simulators, the autopilot adapted successfully to changes in airspeed, altitude, and configuration. This investigation proves the feasibility of a generic autopilot using direct adaptive controller. The autopilot does not need a priori information of the specific aircraft's dynamics to maintain its safety and stability arguments. Real-time parameter estimation of the aircraft dynamics are not needed. Recommendations for future work are provided.

In-Flight Suppression of a Destabilized F/A-18 Structural Mode Using the Space Launch System Adaptive Augmenting Control System

NASA Technical Reports Server (NTRS)

Wall, John H.; VanZwieten, Tannen S.; Gilligan, Eric T.; Miller, Christopher J.; Hanson, Curtis E.; Orr, Jeb S.

2015-01-01

NASA's Space Launch System (SLS) Flight Control System (FCS) includes an Adaptive Augmenting Control (AAC) component which employs a multiplicative gain update law to enhance the performance and robustness of the baseline control system for extreme off nominal scenarios. The SLS FCS algorithm including AAC has been flight tested utilizing a specially outfitted F/A-18 fighter jet in which the pitch axis control of the aircraft was performed by a Non-linear Dynamic Inversion (NDI) controller, SLS reference models, and the SLS flight software prototype. This paper describes test cases from the research flight campaign in which the fundamental F/A-18 airframe structural mode was identified using frequency-domain reconstruction of flight data, amplified to result in closed loop instability, and suppressed in-flight by the SLS adaptive control system.

Multi-model predictive control based on LMI: from the adaptation of the state-space model to the analytic description of the control law

NASA Astrophysics Data System (ADS)

Falugi, P.; Olaru, S.; Dumur, D.

2010-08-01

This article proposes an explicit robust predictive control solution based on linear matrix inequalities (LMIs). The considered predictive control strategy uses different local descriptions of the system dynamics and uncertainties and thus allows the handling of less conservative input constraints. The computed control law guarantees constraint satisfaction and asymptotic stability. The technique is effective for a class of nonlinear systems embedded into polytopic models. A detailed discussion of the procedures which adapt the partition of the state space is presented. For the practical implementation the construction of suitable (explicit) descriptions of the control law are described upon concrete algorithms.

Adaptive control of servo system based on LuGre model

NASA Astrophysics Data System (ADS)

Jin, Wang; Niancong, Liu; Jianlong, Chen; Weitao, Geng

2018-03-01

This paper established a mechanical model of feed system based on LuGre model. In order to solve the influence of nonlinear factors on the system running stability, a nonlinear single observer is designed to estimate the parameter z in the LuGre model and an adaptive friction compensation controller is designed. Simulink simulation results show that the control method can effectively suppress the adverse effects of friction and external disturbances. The simulation show that the adaptive parameter kz is between 0.11-0.13, and the value of gamma1 is between 1.9-2.1. Position tracking error reaches level 10-3 and is stabilized near 0 values within 0.3 seconds, the compensation method has better tracking accuracy and robustness.

Robust decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale MIMO nonlinear systems and its application to AHS.

PubMed

Huang, Yi-Shao; Liu, Wel-Ping; Wu, Min; Wang, Zheng-Wu

2014-09-01

This paper presents a novel observer-based decentralized hybrid adaptive fuzzy control scheme for a class of large-scale continuous-time multiple-input multiple-output (MIMO) uncertain nonlinear systems whose state variables are unmeasurable. The scheme integrates fuzzy logic systems, state observers, and strictly positive real conditions to deal with three issues in the control of a large-scale MIMO uncertain nonlinear system: algorithm design, controller singularity, and transient response. Then, the design of the hybrid adaptive fuzzy controller is extended to address a general large-scale uncertain nonlinear system. It is shown that the resultant closed-loop large-scale system keeps asymptotically stable and the tracking error converges to zero. The better characteristics of our scheme are demonstrated by simulations. Copyright © 2014. Published by Elsevier Ltd.

High speed, precision motion strategies for lightweight structures

NASA Technical Reports Server (NTRS)

Book, Wayne J.

1989-01-01

Research on space telerobotics is summarized. Adaptive control experiments on the Robotic Arm, Large and Flexible (RALF) were preformed and are documented, along with a joint controller design for the Small Articulated Manipulator (SAM), which is mounted on the RALF. A control algorithm is described as a robust decentralized adaptive control based on a bounded uncertainty approach. Dynamic interactions between SAM and RALF are examined. Unstability of the manipulator is studied from the perspective that the inertial forces generated could actually be used to more rapidly damp out the flexible manipulator's vibration. Currently being studied is the modeling of the constrained dynamics of flexible arms.

Adaptive radial basis function mesh deformation using data reduction

NASA Astrophysics Data System (ADS)

Gillebaart, T.; Blom, D. S.; van Zuijlen, A. H.; Bijl, H.

2016-09-01

Radial Basis Function (RBF) mesh deformation is one of the most robust mesh deformation methods available. Using the greedy (data reduction) method in combination with an explicit boundary correction, results in an efficient method as shown in literature. However, to ensure the method remains robust, two issues are addressed: 1) how to ensure that the set of control points remains an accurate representation of the geometry in time and 2) how to use/automate the explicit boundary correction, while ensuring a high mesh quality. In this paper, we propose an adaptive RBF mesh deformation method, which ensures the set of control points always represents the geometry/displacement up to a certain (user-specified) criteria, by keeping track of the boundary error throughout the simulation and re-selecting when needed. Opposed to the unit displacement and prescribed displacement selection methods, the adaptive method is more robust, user-independent and efficient, for the cases considered. Secondly, the analysis of a single high aspect ratio cell is used to formulate an equation for the correction radius needed, depending on the characteristics of the correction function used, maximum aspect ratio, minimum first cell height and boundary error. Based on the analysis two new radial basis correction functions are derived and proposed. This proposed automated procedure is verified while varying the correction function, Reynolds number (and thus first cell height and aspect ratio) and boundary error. Finally, the parallel efficiency is studied for the two adaptive methods, unit displacement and prescribed displacement for both the CPU as well as the memory formulation with a 2D oscillating and translating airfoil with oscillating flap, a 3D flexible locally deforming tube and deforming wind turbine blade. Generally, the memory formulation requires less work (due to the large amount of work required for evaluating RBF's), but the parallel efficiency reduces due to the limited bandwidth available between CPU and memory. In terms of parallel efficiency/scaling the different studied methods perform similarly, with the greedy algorithm being the bottleneck. In terms of absolute computational work the adaptive methods are better for the cases studied due to their more efficient selection of the control points. By automating most of the RBF mesh deformation, a robust, efficient and almost user-independent mesh deformation method is presented.

Optimal Micro-Scale Secondary Flow Control for the Management of High Cycle Fatigue and Distortion in Compact Inlet Diffusers

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Keller, Dennis J.

2002-01-01

The purpose of this study on micro-scale secondary flow control (MSFC) is to study the aerodynamic behavior of micro-vane effectors through their factor (i.e., the design variable) interactions and to demonstrate how these statistical interactions, when brought together in an optimal manner, determine design robustness. The term micro-scale indicates the vane effectors are small in comparison to the local boundary layer height. Robustness in this situation means that it is possible to design fixed MSFC robust installation (i.e.. open loop) which operates well over the range of mission variables and is only marginally different from adaptive (i.e., closed loop) installation design, which would require a control system. The inherent robustness of MSFC micro-vane effector installation designs comes about because of their natural aerodynamic characteristics and the manner in which these characteristics are brought together in an optimal manner through a structured Response Surface Methodology design process.

Inspiration for wing design: how forelimb specialization enables active flight in modern vertebrates

PubMed Central

2017-01-01

Harnessing flight strategies refined by millions of years of evolution can help expedite the design of more efficient, manoeuvrable and robust flying robots. This review synthesizes recent advances and highlights remaining gaps in our understanding of how bird and bat wing adaptations enable effective flight. Included in this discussion is an evaluation of how current robotic analogues measure up to their biological sources of inspiration. Studies of vertebrate wings have revealed skeletal systems well suited for enduring the loads required during flight, but the mechanisms that drive coordinated motions between bones and connected integuments remain ill-described. Similarly, vertebrate flight muscles have adapted to sustain increased wing loading, but a lack of in vivo studies limits our understanding of specific muscular functions. Forelimb adaptations diverge at the integument level, but both bird feathers and bat membranes yield aerodynamic surfaces with a level of robustness unparalleled by engineered wings. These morphological adaptations enable a diverse range of kinematics tuned for different flight speeds and manoeuvres. By integrating vertebrate flight specializations—particularly those that enable greater robustness and adaptability—into the design and control of robotic wings, engineers can begin narrowing the wide margin that currently exists between flying robots and vertebrates. In turn, these robotic wings can help biologists create experiments that would be impossible in vivo. PMID:28592663

Anfis Approach for Sssc Controller Design for the Improvement of Transient Stability Performance

NASA Astrophysics Data System (ADS)

Khuntia, Swasti R.; Panda, Sidhartha

2011-06-01

In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) method based on the Artificial Neural Network (ANN) is applied to design a Static Synchronous Series Compensator (SSSC)-based controller for improvement of transient stability. The proposed ANFIS controller combines the advantages of fuzzy controller and quick response and adaptability nature of ANN. The ANFIS structures were trained using the generated database by fuzzy controller of SSSC. It is observed that the proposed SSSC controller improves greatly the voltage profile of the system under severe disturbances. The results prove that the proposed SSSC-based ANFIS controller is found to be robust to fault location and change in operating conditions. Further, the results obtained are compared with the conventional lead-lag controllers for SSSC.

Anticipatory control: A software retrofit for current plant controllers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parthasarathy, S.; Parlos, A.G.; Atiya, A.F.

1993-01-01

The design and simulated testing of an artificial neural network (ANN)-based self-adapting controller for complex process systems are presented in this paper. The proposed controller employs concepts based on anticipatory systems, which have been widely used in the petroleum and chemical industries, and they are slowly finding their way into the power industry. In particular, model predictive control (MPC) is used for the systematic adaptation of the controller parameters to achieve desirable plant performance over the entire operating envelope. The versatile anticipatory control algorithm developed in this study is projected to enhance plant performance and lend robustness to drifts inmore » plant parameters and to modeling uncertainties. This novel technique of integrating recurrent ANNs with a conventional controller structure appears capable of controlling complex, nonlinear, and nonminimum phase process systems. The direct, on-line adaptive control algorithm presented in this paper considers the plant response over a finite time horizon, diminishing the need for manual control or process interruption for controller gain tuning.« less

Robust high-performance control for robotic manipulators

NASA Technical Reports Server (NTRS)

Seraji, Homayoun (Inventor)

1991-01-01

Model-based and performance-based control techniques are combined for an electrical robotic control system. Thus, two distinct and separate design philosophies have been merged into a single control system having a control law formulation including two distinct and separate components, each of which yields a respective signal component that is combined into a total command signal for the system. Those two separate system components include a feedforward controller and a feedback controller. The feedforward controller is model-based and contains any known part of the manipulator dynamics that can be used for on-line control to produce a nominal feedforward component of the system's control signal. The feedback controller is performance-based and consists of a simple adaptive PID controller which generates an adaptive control signal to complement the nominal feedforward signal.

Robust high-performance control for robotic manipulators

NASA Technical Reports Server (NTRS)

Seraji, Homayoun (Inventor)

1989-01-01

Model-based and performance-based control techniques are combined for an electrical robotic control system. Thus, two distinct and separate design philosophies were merged into a single control system having a control law formulation including two distinct and separate components, each of which yields a respective signal componet that is combined into a total command signal for the system. Those two separate system components include a feedforward controller and feedback controller. The feedforward controller is model-based and contains any known part of the manipulator dynamics that can be used for on-line control to produce a nominal feedforward component of the system's control signal. The feedback controller is performance-based and consists of a simple adaptive PID controller which generates an adaptive control signal to complement the nomical feedforward signal.

Adaptive Neural Control for a Class of Pure-Feedback Nonlinear Systems via Dynamic Surface Technique.

PubMed

Liu, Zongcheng; Dong, Xinmin; Xue, Jianping; Li, Hongbo; Chen, Yong

2016-09-01

This brief addresses the adaptive control problem for a class of pure-feedback systems with nonaffine functions possibly being nondifferentiable. Without using the mean value theorem, the difficulty of the control design for pure-feedback systems is overcome by modeling the nonaffine functions appropriately. With the help of neural network approximators, an adaptive neural controller is developed by combining the dynamic surface control (DSC) and minimal learning parameter (MLP) techniques. The key features of our approach are that, first, the restrictive assumptions on the partial derivative of nonaffine functions are removed, second, the DSC technique is used to avoid "the explosion of complexity" in the backstepping design, and the number of adaptive parameters is reduced significantly using the MLP technique, third, smooth robust compensators are employed to circumvent the influences of approximation errors and disturbances. Furthermore, it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, the simulation results are provided to demonstrate the effectiveness of the designed method.

Indoor Autonomous Control of a Two-Wheeled Inverted Pendulum Vehicle Using Ultra Wide Band Technology.

PubMed

Xia, Dunzhu; Yao, Yanhong; Cheng, Limei

2017-06-15

In this paper, we aimed to achieve the indoor tracking control of a two-wheeled inverted pendulum (TWIP) vehicle. The attitude data are acquired from a low cost micro inertial measurement unit (IMU), and the ultra-wideband (UWB) technology is utilized to obtain an accurate estimation of the TWIP's position. We propose a dual-loop control method to realize the simultaneous balance and trajectory tracking control for the TWIP vehicle. A robust adaptive second-order sliding mode control (2-RASMC) method based on an improved super-twisting (STW) algorithm is investigated to obtain the control laws, followed by several simulations to verify its robustness. The outer loop controller is designed using the idea of backstepping. Moreover, three typical trajectories, including a circle, a trifolium and a hexagon, have been designed to prove the adaptability of the control combinations. Six different combinations of inner and outer loop control algorithms have been compared, and the characteristics of inner and outer loop algorithm combinations have been analyzed. Simulation results demonstrate its tracking performance and thus verify the validity of the proposed control methods. Trajectory tracking experiments in a real indoor environment have been performed using our experimental vehicle to further validate the feasibility of the proposed algorithm in practice.

Adaptive control for solar energy based DC microgrid system development

NASA Astrophysics Data System (ADS)

Zhang, Qinhao

During the upgrading of current electric power grid, it is expected to develop smarter, more robust and more reliable power systems integrated with distributed generations. To realize these objectives, traditional control techniques are no longer effective in either stabilizing systems or delivering optimal and robust performances. Therefore, development of advanced control methods has received increasing attention in power engineering. This work addresses two specific problems in the control of solar panel based microgrid systems. First, a new control scheme is proposed for the microgrid systems to achieve optimal energy conversion ratio in the solar panels. The control system can optimize the efficiency of the maximum power point tracking (MPPT) algorithm by implementing two layers of adaptive control. Such a hierarchical control architecture has greatly improved the system performance, which is validated through both mathematical analysis and computer simulation. Second, in the development of the microgrid transmission system, the issues related to the tele-communication delay and constant power load (CPL)'s negative incremental impedance are investigated. A reference model based method is proposed for pole and zero placements that address the challenges of the time delay and CPL in closed-loop control. The effectiveness of the proposed modeling and control design methods are demonstrated in a simulation testbed. Practical aspects of the proposed methods for general microgrid systems are also discussed.

Indoor Autonomous Control of a Two-Wheeled Inverted Pendulum Vehicle Using Ultra Wide Band Technology

PubMed Central

Xia, Dunzhu; Yao, Yanhong; Cheng, Limei

2017-01-01

In this paper, we aimed to achieve the indoor tracking control of a two-wheeled inverted pendulum (TWIP) vehicle. The attitude data are acquired from a low cost micro inertial measurement unit (IMU), and the ultra-wideband (UWB) technology is utilized to obtain an accurate estimation of the TWIP’s position. We propose a dual-loop control method to realize the simultaneous balance and trajectory tracking control for the TWIP vehicle. A robust adaptive second-order sliding mode control (2-RASMC) method based on an improved super-twisting (STW) algorithm is investigated to obtain the control laws, followed by several simulations to verify its robustness. The outer loop controller is designed using the idea of backstepping. Moreover, three typical trajectories, including a circle, a trifolium and a hexagon, have been designed to prove the adaptability of the control combinations. Six different combinations of inner and outer loop control algorithms have been compared, and the characteristics of inner and outer loop algorithm combinations have been analyzed. Simulation results demonstrate its tracking performance and thus verify the validity of the proposed control methods. Trajectory tracking experiments in a real indoor environment have been performed using our experimental vehicle to further validate the feasibility of the proposed algorithm in practice. PMID:28617338

Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method.

PubMed

Zhang, Huaguang; Cui, Lili; Zhang, Xin; Luo, Yanhong

2011-12-01

In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.

Modal-space reference-model-tracking fuzzy control of earthquake excited structures

NASA Astrophysics Data System (ADS)

Park, Kwan-Soon; Ok, Seung-Yong

2015-01-01

This paper describes an adaptive modal-space reference-model-tracking fuzzy control technique for the vibration control of earthquake-excited structures. In the proposed approach, the fuzzy logic is introduced to update optimal control force so that the controlled structural response can track the desired response of a reference model. For easy and practical implementation, the reference model is constructed by assigning the target damping ratios to the first few dominant modes in modal space. The numerical simulation results demonstrate that the proposed approach successfully achieves not only the adaptive fault-tolerant control system against partial actuator failures but also the robust performance against the variations of the uncertain system properties by redistributing the feedback control forces to the available actuators.

Modeling, Control, and Estimation of Flexible, Aerodynamic Structures

NASA Astrophysics Data System (ADS)

Ray, Cody W.

Engineers have long been inspired by nature’s flyers. Such animals navigate complex environments gracefully and efficiently by using a variety of evolutionary adaptations for high-performance flight. Biologists have discovered a variety of sensory adaptations that provide flow state feedback and allow flying animals to feel their way through flight. A specialized skeletal wing structure and plethora of robust, adaptable sensory systems together allow nature’s flyers to adapt to myriad flight conditions and regimes. In this work, motivated by biology and the successes of bio-inspired, engineered aerial vehicles, linear quadratic control of a flexible, morphing wing design is investigated, helping to pave the way for truly autonomous, mission-adaptive craft. The proposed control algorithm is demonstrated to morph a wing into desired positions. Furthermore, motivated specifically by the sensory adaptations organisms possess, this work transitions to an investigation of aircraft wing load identification using structural response as measured by distributed sensors. A novel, recursive estimation algorithm is utilized to recursively solve the inverse problem of load identification, providing both wing structural and aerodynamic states for use in a feedback control, mission-adaptive framework. The recursive load identification algorithm is demonstrated to provide accurate load estimate in both simulation and experiment.

Robust ADP Design for Continuous-Time Nonlinear Systems With Output Constraints.

PubMed

Fan, Bo; Yang, Qinmin; Tang, Xiaoyu; Sun, Youxian

2018-06-01

In this paper, a novel robust adaptive dynamic programming (RADP)-based control strategy is presented for the optimal control of a class of output-constrained continuous-time unknown nonlinear systems. Our contribution includes a step forward beyond the usual optimal control result to show that the output of the plant is always within user-defined bounds. To achieve the new results, an error transformation technique is first established to generate an equivalent nonlinear system, whose asymptotic stability guarantees both the asymptotic stability and the satisfaction of the output restriction of the original system. Furthermore, RADP algorithms are developed to solve the transformed nonlinear optimal control problem with completely unknown dynamics as well as a robust design to guarantee the stability of the closed-loop systems in the presence of unavailable internal dynamic state. Via small-gain theorem, asymptotic stability of the original and transformed nonlinear system is theoretically guaranteed. Finally, comparison results demonstrate the merits of the proposed control policy.

Flight Testing of the Space Launch System (SLS) Adaptive Augmenting Control (AAC) Algorithm on an F/A-18

NASA Technical Reports Server (NTRS)

Dennehy, Cornelius J.; VanZwieten, Tannen S.; Hanson, Curtis E.; Wall, John H.; Miller, Chris J.; Gilligan, Eric T.; Orr, Jeb S.

2014-01-01

The Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed an adaptive augmenting control (AAC) algorithm for launch vehicles that improves robustness and performance on an as-needed basis by adapting a classical control algorithm to unexpected environments or variations in vehicle dynamics. This was baselined as part of the Space Launch System (SLS) flight control system. The NASA Engineering and Safety Center (NESC) was asked to partner with the SLS Program and the Space Technology Mission Directorate (STMD) Game Changing Development Program (GCDP) to flight test the AAC algorithm on a manned aircraft that can achieve a high level of dynamic similarity to a launch vehicle and raise the technology readiness of the algorithm early in the program. This document reports the outcome of the NESC assessment.

Self-adaptive robot training of stroke survivors for continuous tracking movements.

PubMed

Vergaro, Elena; Casadio, Maura; Squeri, Valentina; Giannoni, Psiche; Morasso, Pietro; Sanguineti, Vittorio

2010-03-15

Although robot therapy is progressively becoming an accepted method of treatment for stroke survivors, few studies have investigated how to adapt the robot/subject interaction forces in an automatic way. The paper is a feasibility study of a novel self-adaptive robot controller to be applied with continuous tracking movements. The haptic robot Braccio di Ferro is used, in relation with a tracking task. The proposed control architecture is based on three main modules: 1) a force field generator that combines a non linear attractive field and a viscous field; 2) a performance evaluation module; 3) an adaptive controller. The first module operates in a continuous time fashion; the other two modules operate in an intermittent way and are triggered at the end of the current block of trials. The controller progressively decreases the gain of the force field, within a session, but operates in a non monotonic way between sessions: it remembers the minimum gain achieved in a session and propagates it to the next one, which starts with a block whose gain is greater than the previous one. The initial assistance gains are chosen according to a minimal assistance strategy. The scheme can also be applied with closed eyes in order to enhance the role of proprioception in learning and control. The preliminary results with a small group of patients (10 chronic hemiplegic subjects) show that the scheme is robust and promotes a statistically significant improvement in performance indicators as well as a recalibration of the visual and proprioceptive channels. The results confirm that the minimally assistive, self-adaptive strategy is well tolerated by severely impaired subjects and is beneficial also for less severe patients. The experiments provide detailed information about the stability and robustness of the adaptive controller of robot assistance that could be quite relevant for the design of future large scale controlled clinical trials. Moreover, the study suggests that including continuous movement in the repertoire of training is acceptable also by rather severely impaired subjects and confirms the stabilizing effect of alternating vision/no vision trials already found in previous studies.

Based on interval type-2 fuzzy-neural network direct adaptive sliding mode control for SISO nonlinear systems

NASA Astrophysics Data System (ADS)

Lin, Tsung-Chih

2010-12-01

In this paper, a novel direct adaptive interval type-2 fuzzy-neural tracking control equipped with sliding mode and Lyapunov synthesis approach is proposed to handle the training data corrupted by noise or rule uncertainties for nonlinear SISO nonlinear systems involving external disturbances. By employing adaptive fuzzy-neural control theory, the update laws will be derived for approximating the uncertain nonlinear dynamical system. In the meantime, the sliding mode control method and the Lyapunov stability criterion are incorporated into the adaptive fuzzy-neural control scheme such that the derived controller is robust with respect to unmodeled dynamics, external disturbance and approximation errors. In comparison with conventional methods, the advocated approach not only guarantees closed-loop stability but also the output tracking error of the overall system will converge to zero asymptotically without prior knowledge on the upper bound of the lumped uncertainty. Furthermore, chattering effect of the control input will be substantially reduced by the proposed technique. To illustrate the performance of the proposed method, finally simulation example will be given.

Cooperative Adaptive Output Regulation for Second-Order Nonlinear Multiagent Systems With Jointly Connected Switching Networks.

PubMed

Liu, Wei; Huang, Jie

2018-03-01

This paper studies the cooperative global robust output regulation problem for a class of heterogeneous second-order nonlinear uncertain multiagent systems with jointly connected switching networks. The main contributions consist of the following three aspects. First, we generalize the result of the adaptive distributed observer from undirected jointly connected switching networks to directed jointly connected switching networks. Second, by performing a new coordinate and input transformation, we convert our problem into the cooperative global robust stabilization problem of a more complex augmented system via the distributed internal model principle. Third, we solve the stabilization problem by a distributed state feedback control law. Our result is illustrated by the leader-following consensus problem for a group of Van der Pol oscillators.

Space Launch System Implementation of Adaptive Augmenting Control

NASA Technical Reports Server (NTRS)

Wall, John H.; Orr, Jeb S.; VanZwieten, Tannen S.

2014-01-01

Given the complex structural dynamics, challenging ascent performance requirements, and rigorous flight certification constraints owing to its manned capability, the NASA Space Launch System (SLS) launch vehicle requires a proven thrust vector control algorithm design with highly optimized parameters to provide stable and high-performance flight. On its development path to Preliminary Design Review (PDR), the SLS flight control system has been challenged by significant vehicle flexibility, aerodynamics, and sloshing propellant. While the design has been able to meet all robust stability criteria, it has done so with little excess margin. Through significant development work, an Adaptive Augmenting Control (AAC) algorithm has been shown to extend the envelope of failures and flight anomalies the SLS control system can accommodate while maintaining a direct link to flight control stability criteria such as classical gain and phase margin. In this paper, the work performed to mature the AAC algorithm as a baseline component of the SLS flight control system is presented. The progress to date has brought the algorithm design to the PDR level of maturity. The algorithm has been extended to augment the full SLS digital 3-axis autopilot, including existing load-relief elements, and the necessary steps for integration with the production flight software prototype have been implemented. Several updates which have been made to the adaptive algorithm to increase its performance, decrease its sensitivity to expected external commands, and safeguard against limitations in the digital implementation are discussed with illustrating results. Monte Carlo simulations and selected stressing case results are also shown to demonstrate the algorithm's ability to increase the robustness of the integrated SLS flight control system.

Adaptive Modulation for DFIG and STATCOM With High-Voltage Direct Current Transmission.

PubMed

Tang, Yufei; He, Haibo; Ni, Zhen; Wen, Jinyu; Huang, Tingwen

2016-08-01

This paper develops an adaptive modulation approach for power system control based on the approximate/adaptive dynamic programming method, namely, the goal representation heuristic dynamic programming (GrHDP). In particular, we focus on the fault recovery problem of a doubly fed induction generator (DFIG)-based wind farm and a static synchronous compensator (STATCOM) with high-voltage direct current (HVDC) transmission. In this design, the online GrHDP-based controller provides three adaptive supplementary control signals to the DFIG controller, STATCOM controller, and HVDC rectifier controller, respectively. The mechanism is to observe the system states and their derivatives and then provides supplementary control to the plant according to the utility function. With the GrHDP design, the controller can adaptively develop an internal goal representation signal according to the observed power system states, therefore, to achieve more effective learning and modulating. Our control approach is validated on a wind power integrated benchmark system with two areas connected by HVDC transmission lines. Compared with the classical direct HDP and proportional integral control, our GrHDP approach demonstrates the improved transient stability under system faults. Moreover, experiments under different system operating conditions with signal transmission delays are also carried out to further verify the effectiveness and robustness of the proposed approach.

Complexity, Robustness, and Multistability in Network Systems with Switching Topologies: A Hierarchical Hybrid Control Approach

DTIC Science & Technology

2015-05-22

sensor networks for managing power levels of wireless networks ; air and ground transportation systems for air traffic control and payload transport and... network systems, large-scale systems, adaptive control, discontinuous systems 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF...cover a broad spectrum of ap- plications including cooperative control of unmanned air vehicles, autonomous underwater vehicles, distributed sensor

Robust model reference adaptive output feedback tracking for uncertain linear systems with actuator fault based on reinforced dead-zone modification.

PubMed

Bagherpoor, H M; Salmasi, Farzad R

2015-07-01

In this paper, robust model reference adaptive tracking controllers are considered for Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) linear systems containing modeling uncertainties, unknown additive disturbances and actuator fault. Two new lemmas are proposed for both SISO and MIMO, under which dead-zone modification rule is improved such that the tracking error for any reference signal tends to zero in such systems. In the conventional approach, adaption of the controller parameters is ceased inside the dead-zone region which results tracking error, while preserving the system stability. In the proposed scheme, control signal is reinforced with an additive term based on tracking error inside the dead-zone which results in full reference tracking. In addition, no Fault Detection and Diagnosis (FDD) unit is needed in the proposed approach. Closed loop system stability and zero tracking error are proved by considering a suitable Lyapunov functions candidate. It is shown that the proposed control approach can assure that all the signals of the close loop system are bounded in faulty conditions. Finally, validity and performance of the new schemes have been illustrated through numerical simulations of SISO and MIMO systems in the presence of actuator faults, modeling uncertainty and output disturbance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Focused cognitive control in dishonesty: Evidence for predominantly transient conflict adaptation.

PubMed

Foerster, Anna; Pfister, Roland; Schmidts, Constantin; Dignath, David; Wirth, Robert; Kunde, Wilfried

2018-04-01

Giving a dishonest response to a question entails cognitive conflict due to an initial activation of the truthful response. Following conflict monitoring theory, dishonest responding could therefore elicit transient and sustained control adaptation processes to mitigate such conflict, and the current experiments take on the scope and specificity of such conflict adaptation in dishonesty. Transient adaptation reduces differences between honest and dishonest responding following a recent dishonest response. Sustained adaptation has a similar behavioral signature but is driven by the overall frequency of dishonest responding. Both types of adaptation to recent and frequent dishonest responses have been separately documented, leaving open whether control processes in dishonest responding can flexibly adapt to transient and sustained conflict signals of dishonest and other actions. This was the goal of the present experiments which studied (dis)honest responding to autobiographical yes/no questions. Experiment 1 showed robust transient adaptation to recent dishonest responses whereas sustained control adaptation failed to exert an influence on behavior. It further revealed that transient effects may create a spurious impression of sustained adaptation in typical experimental settings. Experiments 2 and 3 examined whether dishonest responding can profit from transient and sustained adaption processes triggered by other behavioral conflicts. This was clearly not the case: Dishonest responding adapted markedly to recent (dis)honest responses but not to any context of other conflicts. These findings indicate that control adaptation in dishonest responding is strong but surprisingly focused and they point to a potential trade-off between transient and sustained adaptation. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Adaptive control of large space structures using recursive lattice filters

NASA Technical Reports Server (NTRS)

Goglia, G. L.

1985-01-01

The use of recursive lattice filters for identification and adaptive control of large space structures was studied. Lattice filters are used widely in the areas of speech and signal processing. Herein, they are used to identify the structural dynamics model of the flexible structures. This identified model is then used for adaptive control. Before the identified model and control laws are integrated, the identified model is passed through a series of validation procedures and only when the model passes these validation procedures control is engaged. This type of validation scheme prevents instability when the overall loop is closed. The results obtained from simulation were compared to those obtained from experiments. In this regard, the flexible beam and grid apparatus at the Aerospace Control Research Lab (ACRL) of NASA Langley Research Center were used as the principal candidates for carrying out the above tasks. Another important area of research, namely that of robust controller synthesis, was investigated using frequency domain multivariable controller synthesis methods.

Composite adaptive control of belt polishing force for aero-engine blade

NASA Astrophysics Data System (ADS)

Zhsao, Pengbing; Shi, Yaoyao

2013-09-01

The existing methods for blade polishing mainly focus on robot polishing and manual grinding. Due to the difficulty in high-precision control of the polishing force, the blade surface precision is very low in robot polishing, in particular, quality of the inlet and exhaust edges can not satisfy the processing requirements. Manual grinding has low efficiency, high labor intensity and unstable processing quality, moreover, the polished surface is vulnerable to burn, and the surface precision and integrity are difficult to ensure. In order to further improve the profile accuracy and surface quality, a pneumatic flexible polishing force-exerting mechanism is designed and a dual-mode switching composite adaptive control(DSCAC) strategy is proposed, which combines Bang-Bang control and model reference adaptive control based on fuzzy neural network(MRACFNN) together. By the mode decision-making mechanism, Bang-Bang control is used to track the control command signal quickly when the actual polishing force is far away from the target value, and MRACFNN is utilized in smaller error ranges to improve the system robustness and control precision. Based on the mathematical model of the force-exerting mechanism, simulation analysis is implemented on DSCAC. Simulation results show that the output polishing force can better track the given signal. Finally, the blade polishing experiments are carried out on the designed polishing equipment. Experimental results show that DSCAC can effectively mitigate the influence of gas compressibility, valve dead-time effect, valve nonlinear flow, cylinder friction, measurement noise and other interference on the control precision of polishing force, which has high control precision, strong robustness, strong anti-interference ability and other advantages compared with MRACFNN. The proposed research achieves high-precision control of the polishing force, effectively improves the blade machining precision and surface consistency, and significantly reduces the surface roughness.

Reinforcement-Learning-Based Robust Controller Design for Continuous-Time Uncertain Nonlinear Systems Subject to Input Constraints.

PubMed

Liu, Derong; Yang, Xiong; Wang, Ding; Wei, Qinglai

2015-07-01

The design of stabilizing controller for uncertain nonlinear systems with control constraints is a challenging problem. The constrained-input coupled with the inability to identify accurately the uncertainties motivates the design of stabilizing controller based on reinforcement-learning (RL) methods. In this paper, a novel RL-based robust adaptive control algorithm is developed for a class of continuous-time uncertain nonlinear systems subject to input constraints. The robust control problem is converted to the constrained optimal control problem with appropriately selecting value functions for the nominal system. Distinct from typical action-critic dual networks employed in RL, only one critic neural network (NN) is constructed to derive the approximate optimal control. Meanwhile, unlike initial stabilizing control often indispensable in RL, there is no special requirement imposed on the initial control. By utilizing Lyapunov's direct method, the closed-loop optimal control system and the estimated weights of the critic NN are proved to be uniformly ultimately bounded. In addition, the derived approximate optimal control is verified to guarantee the uncertain nonlinear system to be stable in the sense of uniform ultimate boundedness. Two simulation examples are provided to illustrate the effectiveness and applicability of the present approach.

Learning for intelligent mobile robots

NASA Astrophysics Data System (ADS)

Hall, Ernest L.; Liao, Xiaoqun; Alhaj Ali, Souma M.

2003-10-01

Unlike intelligent industrial robots which often work in a structured factory setting, intelligent mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths. However, such machines have many potential applications in medicine, defense, industry and even the home that make their study important. Sensors such as vision are needed. However, in many applications some form of learning is also required. The purpose of this paper is to present a discussion of recent technical advances in learning for intelligent mobile robots. During the past 20 years, the use of intelligent industrial robots that are equipped not only with motion control systems but also with sensors such as cameras, laser scanners, or tactile sensors that permit adaptation to a changing environment has increased dramatically. However, relatively little has been done concerning learning. Adaptive and robust control permits one to achieve point to point and controlled path operation in a changing environment. This problem can be solved with a learning control. In the unstructured environment, the terrain and consequently the load on the robot"s motors are constantly changing. Learning the parameters of a proportional, integral and derivative controller (PID) and artificial neural network provides an adaptive and robust control. Learning may also be used for path following. Simulations that include learning may be conducted to see if a robot can learn its way through a cluttered array of obstacles. If a situation is performed repetitively, then learning can also be used in the actual application. To reach an even higher degree of autonomous operation, a new level of learning is required. Recently learning theories such as the adaptive critic have been proposed. In this type of learning a critic provides a grade to the controller of an action module such as a robot. The creative control process is used that is "beyond the adaptive critic." A mathematical model of the creative control process is presented that illustrates the use for mobile robots. Examples from a variety of intelligent mobile robot applications are also presented. The significance of this work is in providing a greater understanding of the applications of learning to mobile robots that could lead to many applications.

A robust adaptive load frequency control for micro-grids.

PubMed

Khooban, Mohammad-Hassan; Niknam, Taher; Blaabjerg, Frede; Davari, Pooya; Dragicevic, Tomislav

2016-11-01

The goal of this study is to introduce a novel robust load frequency control (LFC) strategy for micro-grid(s) (MG(s)) in islanded mode operation. Admittedly, power generators in MG(s) cannot supply steady electric power output and sometimes cause unbalance between supply and demand. Battery energy storage system (BESS) is one of the effective solutions to these problems. Due to the high cost of the BESS, a new idea of Vehicle-to-Grid (V2G) is that a battery of Electric-Vehicle (EV) can be applied as a tantamount large-scale BESS in MG(s). As a result, a new robust control strategy for an islanded micro-grid (MG) is introduced that can consider electric vehicles׳ (EV(s)) effect. Moreover, in this paper, a new combination of the General Type II Fuzzy Logic Sets (GT2FLS) and the Modified Harmony Search Algorithm (MHSA) technique is applied for adaptive tuning of proportional-integral (PI) controller. Implementing General Type II Fuzzy Systems is computationally expensive. However, using a recently introduced α-plane representation, GT2FLS can be seen as a composition of several Interval Type II Fuzzy Logic Systems (IT2FLS) with a corresponding level of α for each. Real-data from an offshore wind farm in Sweden and solar radiation data in Aberdeen (United Kingdom) was used in order to examine the performance of the proposed novel controller. A comparison is made between the achieved results of Optimal Fuzzy-PI (OFPI) controller and those of Optimal Interval Type II Fuzzy-PI (IT2FPI) controller, which are of most recent advances in the area at hand. The Simulation results prove the successfulness and effectiveness of the proposed controller. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Robust adaptive control modeling of human arm movements subject to altered gravity and mechanical loads

NASA Astrophysics Data System (ADS)

Tryfonidis, Michail

It has been observed that during orbital spaceflight the absence of gravitation related sensory inputs causes incongruence between the expected and the actual sensory feedback resulting from voluntary movements. This incongruence results in a reinterpretation or neglect of gravity-induced sensory input signals. Over time, new internal models develop, gradually compensating for the loss of spatial reference. The study of adaptation of goal-directed movements is the main focus of this thesis. The hypothesis is that during the adaptive learning process the neural connections behave in ways that can be described by an adaptive control method. The investigation presented in this thesis includes two different sets of experiments. A series of dart throwing experiments took place onboard the space station Mir. Experiments also took place at the Biomechanics lab at MIT, where the subjects performed a series of continuous trajectory tracking movements while a planar robotic manipulandum exerted external torques on the subjects' moving arms. The experimental hypothesis for both experiments is that during the first few trials the subjects will perform poorly trying to follow a prescribed trajectory, or trying to hit a target. A theoretical framework is developed that is a modification of the sliding control method used in robotics. The new control framework is an attempt to explain the adaptive behavior of the subjects. Numerical simulations of the proposed framework are compared with experimental results and predictions from competitive models. The proposed control methodology extends the results of the sliding mode theory to human motor control. The resulting adaptive control model of the motor system is robust to external dynamics, even those of negative gain, uses only position and velocity feedback, and achieves bounded steady-state error without explicit knowledge of the system's nonlinearities. In addition, the experimental and modeling results demonstrate that visuomotor learning is important not only for error correction through internal model adaptation on ground or in microgravity, but also for the minimization of the total mean-square error in the presence of random variability. Thus human intelligent decision displays certain attributes that seem to conform to Bayesian statistical games. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Shaohua

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaosmore » of PMSM and show the effectiveness and robustness of the proposed method.« less

Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain.

PubMed

Luo, Shaohua

2014-09-01

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.

The ALICE-HMPID Detector Control System: Its evolution towards an expert and adaptive system

NASA Astrophysics Data System (ADS)

De Cataldo, G.; Franco, A.; Pastore, C.; Sgura, I.; Volpe, G.

2011-05-01

The High Momentum Particle IDentification (HMPID) detector is a proximity focusing Ring Imaging Cherenkov (RICH) for charged hadron identification. The HMPID is based on liquid C 6F 14 as the radiator medium and on a 10 m 2 CsI coated, pad segmented photocathode of MWPCs for UV Cherenkov photon detection. To ensure full remote control, the HMPID is equipped with a detector control system (DCS) responding to industrial standards for robustness and reliability. It has been implemented using PVSS as Slow Control And Data Acquisition (SCADA) environment, Programmable Logic Controller as control devices and Finite State Machines for modular and automatic command execution. In the perspective of reducing human presence at the experiment site, this paper focuses on DCS evolution towards an expert and adaptive control system, providing, respectively, automatic error recovery and stable detector performance. HAL9000, the first prototype of the HMPID expert system, is then presented. Finally an analysis of the possible application of the adaptive features is provided.

Modeling and simulation of permanent magnet synchronous motor based on neural network control strategy

NASA Astrophysics Data System (ADS)

Luo, Bingyang; Chi, Shangjie; Fang, Man; Li, Mengchao

2017-03-01

Permanent magnet synchronous motor is used widely in industry, the performance requirements wouldn't be met by adopting traditional PID control in some of the occasions with high requirements. In this paper, a hybrid control strategy - nonlinear neural network PID and traditional PID parallel control are adopted. The high stability and reliability of traditional PID was combined with the strong adaptive ability and robustness of neural network. The permanent magnet synchronous motor will get better control performance when switch different working modes according to different controlled object conditions. As the results showed, the speed response adopting the composite control strategy in this paper was faster than the single control strategy. And in the case of sudden disturbance, the recovery time adopting the composite control strategy designed in this paper was shorter, the recovery ability and the robustness were stronger.

Does a crouched leg posture enhance running stability and robustness?

PubMed

Blum, Yvonne; Birn-Jeffery, Aleksandra; Daley, Monica A; Seyfarth, Andre

2011-07-21

Humans and birds both walk and run bipedally on compliant legs. However, differences in leg architecture may result in species-specific leg control strategies as indicated by the observed gait patterns. In this work, control strategies for stable running are derived based on a conceptual model and compared with experimental data on running humans and pheasants (Phasianus colchicus). From a model perspective, running with compliant legs can be represented by the planar spring mass model and stabilized by applying swing leg control. Here, linear adaptations of the three leg parameters, leg angle, leg length and leg stiffness during late swing phase are assumed. Experimentally observed kinematic control parameters (leg rotation and leg length change) of human and avian running are compared, and interpreted within the context of this model, with specific focus on stability and robustness characteristics. The results suggest differences in stability characteristics and applied control strategies of human and avian running, which may relate to differences in leg posture (straight leg posture in humans, and crouched leg posture in birds). It has been suggested that crouched leg postures may improve stability. However, as the system of control strategies is overdetermined, our model findings suggest that a crouched leg posture does not necessarily enhance running stability. The model also predicts different leg stiffness adaptation rates for human and avian running, and suggests that a crouched avian leg posture, which is capable of both leg shortening and lengthening, allows for stable running without adjusting leg stiffness. In contrast, in straight-legged human running, the preparation of the ground contact seems to be more critical, requiring leg stiffness adjustment to remain stable. Finally, analysis of a simple robustness measure, the normalized maximum drop, suggests that the crouched leg posture may provide greater robustness to changes in terrain height. Copyright © 2011 Elsevier Ltd. All rights reserved.

A robust high-order lattice adaptive notch filter and its application to narrowband noise cancellation

NASA Astrophysics Data System (ADS)

Kim, Seong-woo; Park, Young-cheol; Seo, Young-soo; Youn, Dae Hee

2014-12-01

In this paper, we propose a high-order lattice adaptive notch filter (LANF) that can robustly track multiple sinusoids. Unlike the conventional cascade structure, the proposed high-order LANF has robust tracking characteristics regardless of the frequencies of reference sinusoids and initial notch frequencies. The proposed high-order LANF is applied to a narrowband adaptive noise cancellation (ANC) to mitigate the effect of the broadband disturbance in the reference signal. By utilizing the gradient adaptive lattice (GAL) ANC algorithm and approximately combining it with the proposed high-order LANF, a computationally efficient narrowband ANC system is obtained. Experimental results demonstrate the robustness of the proposed high-order LANF and the effectiveness of the obtained narrowband ANC system.

Robust Stability of Scaled-Four-Channel Teleoperation with Internet Time-Varying Delays

PubMed Central

Delgado, Emma; Barreiro, Antonio; Falcón, Pablo; Díaz-Cacho, Miguel

2016-01-01

We describe the application of a generic stability framework for a teleoperation system under time-varying delay conditions, as addressed in a previous work, to a scaled-four-channel (γ-4C) control scheme. Described is how varying delays are dealt with by means of dynamic encapsulation, giving rise to mu-test conditions for robust stability and offering an appealing frequency technique to deal with the stability robustness of the architecture. We discuss ideal transparency problems and we adapt classical solutions so that controllers are proper, without single or double differentiators, and thus avoid the negative effects of noise. The control scheme was fine-tuned and tested for complete stability to zero of the whole state, while seeking a practical solution to the trade-off between stability and transparency in the Internet-based teleoperation. These ideas were tested on an Internet-based application with two Omni devices at remote laboratory locations via simulations and real remote experiments that achieved robust stability, while performing well in terms of position synchronization and force transparency. PMID:27128914

Adaptive quantum computation in changing environments using projective simulation

NASA Astrophysics Data System (ADS)

Tiersch, M.; Ganahl, E. J.; Briegel, H. J.

2015-08-01

Quantum information processing devices need to be robust and stable against external noise and internal imperfections to ensure correct operation. In a setting of measurement-based quantum computation, we explore how an intelligent agent endowed with a projective simulator can act as controller to adapt measurement directions to an external stray field of unknown magnitude in a fixed direction. We assess the agent’s learning behavior in static and time-varying fields and explore composition strategies in the projective simulator to improve the agent’s performance. We demonstrate the applicability by correcting for stray fields in a measurement-based algorithm for Grover’s search. Thereby, we lay out a path for adaptive controllers based on intelligent agents for quantum information tasks.

Adaptive quantum computation in changing environments using projective simulation

PubMed Central

Tiersch, M.; Ganahl, E. J.; Briegel, H. J.

2015-01-01

Quantum information processing devices need to be robust and stable against external noise and internal imperfections to ensure correct operation. In a setting of measurement-based quantum computation, we explore how an intelligent agent endowed with a projective simulator can act as controller to adapt measurement directions to an external stray field of unknown magnitude in a fixed direction. We assess the agent’s learning behavior in static and time-varying fields and explore composition strategies in the projective simulator to improve the agent’s performance. We demonstrate the applicability by correcting for stray fields in a measurement-based algorithm for Grover’s search. Thereby, we lay out a path for adaptive controllers based on intelligent agents for quantum information tasks. PMID:26260263

Fault-tolerant nonlinear adaptive flight control using sliding mode online learning.

PubMed

Krüger, Thomas; Schnetter, Philipp; Placzek, Robin; Vörsmann, Peter

2012-08-01

An expanded nonlinear model inversion flight control strategy using sliding mode online learning for neural networks is presented. The proposed control strategy is implemented for a small unmanned aircraft system (UAS). This class of aircraft is very susceptible towards nonlinearities like atmospheric turbulence, model uncertainties and of course system failures. Therefore, these systems mark a sensible testbed to evaluate fault-tolerant, adaptive flight control strategies. Within this work the concept of feedback linearization is combined with feed forward neural networks to compensate for inversion errors and other nonlinear effects. Backpropagation-based adaption laws of the network weights are used for online training. Within these adaption laws the standard gradient descent backpropagation algorithm is augmented with the concept of sliding mode control (SMC). Implemented as a learning algorithm, this nonlinear control strategy treats the neural network as a controlled system and allows a stable, dynamic calculation of the learning rates. While considering the system's stability, this robust online learning method therefore offers a higher speed of convergence, especially in the presence of external disturbances. The SMC-based flight controller is tested and compared with the standard gradient descent backpropagation algorithm in the presence of system failures. Copyright © 2012 Elsevier Ltd. All rights reserved.

Adaptive Wavelet Coding Applied in a Wireless Control System.

PubMed

Gama, Felipe O S; Silveira, Luiz F Q; Salazar, Andrés O

2017-12-13

Wireless control systems can sense, control and act on the information exchanged between the wireless sensor nodes in a control loop. However, the exchanged information becomes susceptible to the degenerative effects produced by the multipath propagation. In order to minimize the destructive effects characteristic of wireless channels, several techniques have been investigated recently. Among them, wavelet coding is a good alternative for wireless communications for its robustness to the effects of multipath and its low computational complexity. This work proposes an adaptive wavelet coding whose parameters of code rate and signal constellation can vary according to the fading level and evaluates the use of this transmission system in a control loop implemented by wireless sensor nodes. The performance of the adaptive system was evaluated in terms of bit error rate (BER) versus E b / N 0 and spectral efficiency, considering a time-varying channel with flat Rayleigh fading, and in terms of processing overhead on a control system with wireless communication. The results obtained through computational simulations and experimental tests show performance gains obtained by insertion of the adaptive wavelet coding in a control loop with nodes interconnected by wireless link. These results enable the use of this technique in a wireless link control loop.

Robust and Adaptive Guidance and Control Laws for Missile Systems

DTIC Science & Technology

1994-06-26

Dynamic Noncooperative Game The- + I61 + R-IBTH 6xI5 dt a 0 ory. New York: Academic, 1982. 1241 A. E. Bryson and Y. C. Ho, Applied Optimal Control. New York...M[HorV- tro - -eI,,BoR - • ornl ,]M + rlr"" 0 (21) ment feedback. By use of the uncertainty modeling of Eq. (3), system (1) By using the controller

Adaptive sensor-fault tolerant control for a class of multivariable uncertain nonlinear systems.

PubMed

Khebbache, Hicham; Tadjine, Mohamed; Labiod, Salim; Boulkroune, Abdesselem

2015-03-01

This paper deals with the active fault tolerant control (AFTC) problem for a class of multiple-input multiple-output (MIMO) uncertain nonlinear systems subject to sensor faults and external disturbances. The proposed AFTC method can tolerate three additive (bias, drift and loss of accuracy) and one multiplicative (loss of effectiveness) sensor faults. By employing backstepping technique, a novel adaptive backstepping-based AFTC scheme is developed using the fact that sensor faults and system uncertainties (including external disturbances and unexpected nonlinear functions caused by sensor faults) can be on-line estimated and compensated via robust adaptive schemes. The stability analysis of the closed-loop system is rigorously proven using a Lyapunov approach. The effectiveness of the proposed controller is illustrated by two simulation examples. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

WITHDRAWN: A novel robust adaptive sliding mode control using fuzzy self-tuning PID controller for 3 DOF planar robot manipulators.

PubMed

Amer, Ahmed Foad; Sallam, Elsayed Abdelhameed; Elawady, Wael Mohammed

2011-02-22

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy. Copyright © 2011. Published by Elsevier Ltd.. All rights reserved.

Adaptive control paradigm for photovoltaic and solid oxide fuel cell in a grid-integrated hybrid renewable energy system.

PubMed

Mumtaz, Sidra; Khan, Laiq

2017-01-01

The hybrid power system (HPS) is an emerging power generation scheme due to the plentiful availability of renewable energy sources. Renewable energy sources are characterized as highly intermittent in nature due to meteorological conditions, while the domestic load also behaves in a quite uncertain manner. In this scenario, to maintain the balance between generation and load, the development of an intelligent and adaptive control algorithm has preoccupied power engineers and researchers. This paper proposes a Hermite wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control of photovoltaic (PV) systems to extract maximum power and a Hermite wavelet incorporated NeuroFuzzy indirect adaptive control of Solid Oxide Fuel Cells (SOFC) to obtain a swift response in a grid-connected hybrid power system. A comprehensive simulation testbed for a grid-connected hybrid power system (wind turbine, PV cells, SOFC, electrolyzer, battery storage system, supercapacitor (SC), micro-turbine (MT) and domestic load) is developed in Matlab/Simulink. The robustness and superiority of the proposed indirect adaptive control paradigm are evaluated through simulation results in a grid-connected hybrid power system testbed by comparison with a conventional PI (proportional and integral) control system. The simulation results verify the effectiveness of the proposed control paradigm.

Adaptive control paradigm for photovoltaic and solid oxide fuel cell in a grid-integrated hybrid renewable energy system

PubMed Central

Khan, Laiq

2017-01-01

The hybrid power system (HPS) is an emerging power generation scheme due to the plentiful availability of renewable energy sources. Renewable energy sources are characterized as highly intermittent in nature due to meteorological conditions, while the domestic load also behaves in a quite uncertain manner. In this scenario, to maintain the balance between generation and load, the development of an intelligent and adaptive control algorithm has preoccupied power engineers and researchers. This paper proposes a Hermite wavelet embedded NeuroFuzzy indirect adaptive MPPT (maximum power point tracking) control of photovoltaic (PV) systems to extract maximum power and a Hermite wavelet incorporated NeuroFuzzy indirect adaptive control of Solid Oxide Fuel Cells (SOFC) to obtain a swift response in a grid-connected hybrid power system. A comprehensive simulation testbed for a grid-connected hybrid power system (wind turbine, PV cells, SOFC, electrolyzer, battery storage system, supercapacitor (SC), micro-turbine (MT) and domestic load) is developed in Matlab/Simulink. The robustness and superiority of the proposed indirect adaptive control paradigm are evaluated through simulation results in a grid-connected hybrid power system testbed by comparison with a conventional PI (proportional and integral) control system. The simulation results verify the effectiveness of the proposed control paradigm. PMID:28329015

Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.

PubMed

Lin, Chuan-Kai

2005-04-01

A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot.

Guidance and Control Architecture Design and Demonstration for Low Ballistic Coefficient Atmospheric Entry

NASA Technical Reports Server (NTRS)

Swei, Sean

2014-01-01

We propose to develop a robust guidance and control system for the ADEPT (Adaptable Deployable Entry and Placement Technology) entry vehicle. A control-centric model of ADEPT will be developed to quantify the performance of candidate guidance and control architectures for both aerocapture and precision landing missions. The evaluation will be based on recent breakthroughs in constrained controllability/reachability analysis of control systems and constrained-based energy-minimum trajectory optimization for guidance development operating in complex environments.

Quality Scalability Aware Watermarking for Visual Content.

PubMed

Bhowmik, Deepayan; Abhayaratne, Charith

2016-11-01

Scalable coding-based content adaptation poses serious challenges to traditional watermarking algorithms, which do not consider the scalable coding structure and hence cannot guarantee correct watermark extraction in media consumption chain. In this paper, we propose a novel concept of scalable blind watermarking that ensures more robust watermark extraction at various compression ratios while not effecting the visual quality of host media. The proposed algorithm generates scalable and robust watermarked image code-stream that allows the user to constrain embedding distortion for target content adaptations. The watermarked image code-stream consists of hierarchically nested joint distortion-robustness coding atoms. The code-stream is generated by proposing a new wavelet domain blind watermarking algorithm guided by a quantization based binary tree. The code-stream can be truncated at any distortion-robustness atom to generate the watermarked image with the desired distortion-robustness requirements. A blind extractor is capable of extracting watermark data from the watermarked images. The algorithm is further extended to incorporate a bit-plane discarding-based quantization model used in scalable coding-based content adaptation, e.g., JPEG2000. This improves the robustness against quality scalability of JPEG2000 compression. The simulation results verify the feasibility of the proposed concept, its applications, and its improved robustness against quality scalable content adaptation. Our proposed algorithm also outperforms existing methods showing 35% improvement. In terms of robustness to quality scalable video content adaptation using Motion JPEG2000 and wavelet-based scalable video coding, the proposed method shows major improvement for video watermarking.

Finite-Time Attitude Tracking Control for Spacecraft Using Terminal Sliding Mode and Chebyshev Neural Network.

PubMed

An-Min Zou; Kumar, K D; Zeng-Guang Hou; Xi Liu

2011-08-01

A finite-time attitude tracking control scheme is proposed for spacecraft using terminal sliding mode and Chebyshev neural network (NN) (CNN). The four-parameter representations (quaternion) are used to describe the spacecraft attitude for global representation without singularities. The attitude state (i.e., attitude and velocity) error dynamics is transformed to a double integrator dynamics with a constraint on the spacecraft attitude. With consideration of this constraint, a novel terminal sliding manifold is proposed for the spacecraft. In order to guarantee that the output of the NN used in the controller is bounded by the corresponding bound of the approximated unknown function, a switch function is applied to generate a switching between the adaptive NN control and the robust controller. Meanwhile, a CNN, whose basis functions are implemented using only desired signals, is introduced to approximate the desired nonlinear function and bounded external disturbances online, and the robust term based on the hyperbolic tangent function is applied to counteract NN approximation errors in the adaptive neural control scheme. Most importantly, the finite-time stability in both the reaching phase and the sliding phase can be guaranteed by a Lyapunov-based approach. Finally, numerical simulations on the attitude tracking control of spacecraft in the presence of an unknown mass moment of inertia matrix, bounded external disturbances, and control input constraints are presented to demonstrate the performance of the proposed controller.

Development of Control Models and a Robust Multivariable Controller for Surface Shape Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Winters, Scott Eric

2003-06-18

Surface shape control techniques are applied to many diverse disciplines, such as adaptive optics, noise control, aircraft flutter control and satellites, with an objective to achieve a desirable shape for an elastic body by the application of distributed control forces. Achieving the desirable shape is influenced by many factors, such as, actuator locations, sensor locations, surface precision and controller performance. Building prototypes to complete design optimizations or controller development can be costly or impractical. This shortfall, puts significant value in developing accurate modeling and control simulation approaches. This thesis focuses on the field of adaptive optics, although these developments havemore » the potential for application in many other fields. A static finite element model is developed and validated using a large aperture interferometer system. This model is then integrated into a control model using a linear least squares algorithm and Shack-Hartmann sensor. The model is successfully exercised showing functionality for various wavefront aberrations. Utilizing a verified model shows significant value in simulating static surface shape control problems with quantifiable uncertainties. A new dynamic model for a seven actuator deformable mirror is presented and its accuracy is proven through experiment. Bond graph techniques are used to generate the state space model of the multi-actuator deformable mirror including piezo-electric actuator dynamics. Using this verified model, a robust multi-input multi-output (MIMO) H ∞ controller is designed and implemented. This controller proved superior performance as compared to a standard proportional-integral controller (PI) design.« less

Adaptive robust fault-tolerant control for linear MIMO systems with unmatched uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Kangkang; Jiang, Bin; Yan, Xing-Gang; Mao, Zehui

2017-10-01

In this paper, two novel fault-tolerant control design approaches are proposed for linear MIMO systems with actuator additive faults, multiplicative faults and unmatched uncertainties. For time-varying multiplicative and additive faults, new adaptive laws and additive compensation functions are proposed. A set of conditions is developed such that the unmatched uncertainties are compensated by actuators in control. On the other hand, for unmatched uncertainties with their projection in unmatched space being not zero, based on a (vector) relative degree condition, additive functions are designed to compensate for the uncertainties from output channels in the presence of actuator faults. The developed fault-tolerant control schemes are applied to two aircraft systems to demonstrate the efficiency of the proposed approaches.

Adaptive integral dynamic surface control of a hypersonic flight vehicle

NASA Astrophysics Data System (ADS)

Aslam Butt, Waseem; Yan, Lin; Amezquita S., Kendrick

2015-07-01

In this article, non-linear adaptive dynamic surface air speed and flight path angle control designs are presented for the longitudinal dynamics of a flexible hypersonic flight vehicle. The tracking performance of the control design is enhanced by introducing a novel integral term that caters to avoiding a large initial control signal. To ensure feasibility, the design scheme incorporates magnitude and rate constraints on the actuator commands. The uncertain non-linear functions are approximated by an efficient use of the neural networks to reduce the computational load. A detailed stability analysis shows that all closed-loop signals are uniformly ultimately bounded and the ? tracking performance is guaranteed. The robustness of the design scheme is verified through numerical simulations of the flexible flight vehicle model.

Sliding mode control of direct coupled interleaved boost converter for fuel cell

NASA Astrophysics Data System (ADS)

Wang, W. Y.; Ding, Y. H.; Ke, X.; Ma, X.

2017-12-01

A three phase direct coupled interleaved boost converter (TP-DIBC) was recommended in this paper. This converter has a small unbalance current sharing among the branches of TP-DIBC. An adaptive control law sliding mode control (SMC) is designed for the TP-DIBC. The aim is to 1) reduce ripple output voltage, inductor current and regulate output voltage tightly 2) The total current carried by direct coupled interleaved boost converter (DIBC) must be equally shared between different parallel branches. The efficacy and robustness of the proposed TP-DIBC and adaptive SMC is confirmed via computer simulations using Matlab SimPower System Tools. The simulation result is in line with the expectation.

Genetic adaptation to historical pathogen burdens.

PubMed

Fedderke, Johannes W; Klitgaard, Robert E; Napolioni, Valerio

2017-10-01

Historical pathogen burdens are examined as possible triggers for genetic adaptation. Evidence of adaptation emerges for the acid phosphatase locus 1 (ACP1), interleukin-6 (IL6), interleukin-10 (IL10 ), human leukocyte antigen (HLA) polymorphisms, along with a measure of heterozygosity over 783 alleles. Results are robust to controlling for the physical and historical environment humans faced, and to endogeneity of the historical pathogen burden measure. The present study represents a proof-of-concept which may pave the way to the analysis of future aggregate measures coming from whole-genome sequencing/genotyping data. Copyright © 2017 Elsevier B.V. All rights reserved.

Iterative learning-based decentralized adaptive tracker for large-scale systems: a digital redesign approach.

PubMed

Tsai, Jason Sheng-Hong; Du, Yan-Yi; Huang, Pei-Hsiang; Guo, Shu-Mei; Shieh, Leang-San; Chen, Yuhua

2011-07-01

In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Chaos Suppression in Fractional order Permanent Magnet Synchronous Generator in Wind Turbine Systems

NASA Astrophysics Data System (ADS)

Rajagopal, Karthikeyan; Karthikeyan, Anitha; Duraisamy, Prakash

2017-06-01

In this paper we investigate the control of three-dimensional non-autonomous fractional-order uncertain model of a permanent magnet synchronous generator (PMSG) via a adaptive control technique. We derive a dimensionless fractional order model of the PMSM from the integer order presented in the literatures. Various dynamic properties of the fractional order model like eigen values, Lyapunov exponents, bifurcation and bicoherence are investigated. The system chaotic behavior for various orders of fractional calculus are presented. An adaptive controller is derived to suppress the chaotic oscillations of the fractional order model. As the direct Lyapunov stability analysis of the robust controller is difficult for a fractional order first derivative, we have derived a new lemma to analyze the stability of the system. Numerical simulations of the proposed chaos suppression methodology are given to prove the analytical results derived through which we show that for the derived adaptive controller and the parameter update law, the origin of the system for any bounded initial conditions is asymptotically stable.

Mean deviation coupling synchronous control for multiple motors via second-order adaptive sliding mode control.

PubMed

Li, Lebao; Sun, Lingling; Zhang, Shengzhou

2016-05-01

A new mean deviation coupling synchronization control strategy is developed for multiple motor control systems, which can guarantee the synchronization performance of multiple motor control systems and reduce complexity of the control structure with the increasing number of motors. The mean deviation coupling synchronization control architecture combining second-order adaptive sliding mode control (SOASMC) approach is proposed, which can improve synchronization control precision of multiple motor control systems and make speed tracking errors, mean speed errors of each motor and speed synchronization errors converge to zero rapidly. The proposed control scheme is robustness to parameter variations and random external disturbances and can alleviate the chattering phenomena. Moreover, an adaptive law is employed to estimate the unknown bound of uncertainty, which is obtained in the sense of Lyapunov stability theorem to minimize the control effort. Performance comparisons with master-slave control, relative coupling control, ring coupling control, conventional PI control and SMC are investigated on a four-motor synchronization control system. Extensive comparative results are given to shown the good performance of the proposed control scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A self-adaptive-grid method with application to airfoil flow

NASA Technical Reports Server (NTRS)

Nakahashi, K.; Deiwert, G. S.

1985-01-01

A self-adaptive-grid method is described that is suitable for multidimensional steady and unsteady computations. Based on variational principles, a spring analogy is used to redistribute grid points in an optimal sense to reduce the overall solution error. User-specified parameters, denoting both maximum and minimum permissible grid spacings, are used to define the all-important constants, thereby minimizing the empiricism and making the method self-adaptive. Operator splitting and one-sided controls for orthogonality and smoothness are used to make the method practical, robust, and efficient. Examples are included for both steady and unsteady viscous flow computations about airfoils in two dimensions, as well as for a steady inviscid flow computation and a one-dimensional case. These examples illustrate the precise control the user has with the self-adaptive method and demonstrate a significant improvement in accuracy and quality of the solutions.

Adaptive AOA-aided TOA self-positioning for mobile wireless sensor networks.

PubMed

Wen, Chih-Yu; Chan, Fu-Kai

2010-01-01

Location-awareness is crucial and becoming increasingly important to many applications in wireless sensor networks. This paper presents a network-based positioning system and outlines recent work in which we have developed an efficient principled approach to localize a mobile sensor using time of arrival (TOA) and angle of arrival (AOA) information employing multiple seeds in the line-of-sight scenario. By receiving the periodic broadcasts from the seeds, the mobile target sensors can obtain adequate observations and localize themselves automatically. The proposed positioning scheme performs location estimation in three phases: (I) AOA-aided TOA measurement, (II) Geometrical positioning with particle filter, and (III) Adaptive fuzzy control. Based on the distance measurements and the initial position estimate, adaptive fuzzy control scheme is applied to solve the localization adjustment problem. The simulations show that the proposed approach provides adaptive flexibility and robust improvement in position estimation.

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

NASA Technical Reports Server (NTRS)

Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

2003-01-01

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

TU-H-CAMPUS-JeP3-01: Towards Robust Adaptive Radiation Therapy Strategies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boeck, M; KTH Royal Institute of Technology, Stockholm; Eriksson, K

Purpose: To set up a framework combining robust treatment planning with adaptive reoptimization in order to maintain high treatment quality, to respond to interfractional variations and to identify those patients who will benefit the most from an adaptive fractionation schedule. Methods: We propose adaptive strategies based on stochastic minimax optimization for a series of simulated treatments on a one-dimensional patient phantom. The plan should be able to handle anticipated systematic and random errors and is applied during the first fractions. Information on the individual geometric variations is gathered at each fraction. At scheduled fractions, the impact of the measured errorsmore » on the delivered dose distribution is evaluated. For a patient that receives a dose that does not satisfy specified plan quality criteria, the plan is reoptimized based on these individual measurements using one of three different adaptive strategies. The reoptimized plan is then applied during future fractions until a new scheduled adaptation becomes necessary. In the first adaptive strategy the measured systematic and random error scenarios and their assigned probabilities are updated to guide the robust reoptimization. The focus of the second strategy lies on variation of the fraction of the worst scenarios taken into account during robust reoptimization. In the third strategy the uncertainty margins around the target are recalculated with the measured errors. Results: By studying the effect of the three adaptive strategies combined with various adaptation schedules on the same patient population, the group which benefits from adaptation is identified together with the most suitable strategy and schedule. Preliminary computational results indicate when and how best to adapt for the three different strategies. Conclusion: A workflow is presented that provides robust adaptation of the treatment plan throughout the course of treatment and useful measures to identify patients in need for an adaptive treatment strategy.« less

Contributions of COMT Val[superscript 158]Met to Cognitive Stability and Flexibility in Infancy

ERIC Educational Resources Information Center

Markant, Julie; Cicchetti, Dante; Hetzel, Susan; Thomas, Kathleen M.

2014-01-01

Adaptive behavior requires focusing on relevant tasks while remaining sensitive to novel information. In adult studies of cognitive control, cognitive stability involves maintaining robust cognitive representations while cognitive flexibility involves updating of representations in response to novel information. Previous adult research has shown…

Robustness of non-interdependent and interdependent networks against dependent and adaptive attacks

NASA Astrophysics Data System (ADS)

Tyra, Adam; Li, Jingtao; Shang, Yilun; Jiang, Shuo; Zhao, Yanjun; Xu, Shouhuai

2017-09-01

Robustness of complex networks has been extensively studied via the notion of site percolation, which typically models independent and non-adaptive attacks (or disruptions). However, real-life attacks are often dependent and/or adaptive. This motivates us to characterize the robustness of complex networks, including non-interdependent and interdependent ones, against dependent and adaptive attacks. For this purpose, dependent attacks are accommodated by L-hop percolation where the nodes within some L-hop (L ≥ 0) distance of a chosen node are all deleted during one attack (with L = 0 degenerating to site percolation). Whereas, adaptive attacks are launched by attackers who can make node-selection decisions based on the network state in the beginning of each attack. The resulting characterization enriches the body of knowledge with new insights, such as: (i) the Achilles' Heel phenomenon is only valid for independent attacks, but not for dependent attacks; (ii) powerful attack strategies (e.g., targeted attacks and dependent attacks, dependent attacks and adaptive attacks) are not compatible and cannot help the attacker when used collectively. Our results shed some light on the design of robust complex networks.

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

PubMed Central

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-01-01

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361

A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation.

PubMed

Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

2016-12-19

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.

Robust Software Architecture for Robots

NASA Technical Reports Server (NTRS)

Aghazanian, Hrand; Baumgartner, Eric; Garrett, Michael

2009-01-01

Robust Real-Time Reconfigurable Robotics Software Architecture (R4SA) is the name of both a software architecture and software that embodies the architecture. The architecture was conceived in the spirit of current practice in designing modular, hard, realtime aerospace systems. The architecture facilitates the integration of new sensory, motor, and control software modules into the software of a given robotic system. R4SA was developed for initial application aboard exploratory mobile robots on Mars, but is adaptable to terrestrial robotic systems, real-time embedded computing systems in general, and robotic toys.

Environmental Noise, Genetic Diversity and the Evolution of Evolvability and Robustness in Model Gene Networks

PubMed Central

Steiner, Christopher F.

2012-01-01

The ability of organisms to adapt and persist in the face of environmental change is accepted as a fundamental feature of natural systems. More contentious is whether the capacity of organisms to adapt (or “evolvability”) can itself evolve and the mechanisms underlying such responses. Using model gene networks, I provide evidence that evolvability emerges more readily when populations experience positively autocorrelated environmental noise (red noise) compared to populations in stable or randomly varying (white noise) environments. Evolvability was correlated with increasing genetic robustness to effects on network viability and decreasing robustness to effects on phenotypic expression; populations whose networks displayed greater viability robustness and lower phenotypic robustness produced more additive genetic variation and adapted more rapidly in novel environments. Patterns of selection for robustness varied antagonistically with epistatic effects of mutations on viability and phenotypic expression, suggesting that trade-offs between these properties may constrain their evolutionary responses. Evolution of evolvability and robustness was stronger in sexual populations compared to asexual populations indicating that enhanced genetic variation under fluctuating selection combined with recombination load is a primary driver of the emergence of evolvability. These results provide insight into the mechanisms potentially underlying rapid adaptation as well as the environmental conditions that drive the evolution of genetic interactions. PMID:23284934

Robust Feedback Control of Flow Induced Structural Radiation of Sound

NASA Technical Reports Server (NTRS)

Heatwole, Craig M.; Bernhard, Robert J.; Franchek, Matthew A.

1997-01-01

A significant component of the interior noise of aircraft and automobiles is a result of turbulent boundary layer excitation of the vehicular structure. In this work, active robust feedback control of the noise due to this non-predictable excitation is investigated. Both an analytical model and experimental investigations are used to determine the characteristics of the flow induced structural sound radiation problem. The problem is shown to be broadband in nature with large system uncertainties associated with the various operating conditions. Furthermore the delay associated with sound propagation is shown to restrict the use of microphone feedback. The state of the art control methodologies, IL synthesis and adaptive feedback control, are evaluated and shown to have limited success for solving this problem. A robust frequency domain controller design methodology is developed for the problem of sound radiated from turbulent flow driven plates. The control design methodology uses frequency domain sequential loop shaping techniques. System uncertainty, sound pressure level reduction performance, and actuator constraints are included in the design process. Using this design method, phase lag was added using non-minimum phase zeros such that the beneficial plant dynamics could be used. This general control approach has application to lightly damped vibration and sound radiation problems where there are high bandwidth control objectives requiring a low controller DC gain and controller order.

Extended observer based on adaptive second order sliding mode control for a fixed wing UAV.

PubMed

Castañeda, Herman; Salas-Peña, Oscar S; León-Morales, Jesús de

2017-01-01

This paper addresses the design of attitude and airspeed controllers for a fixed wing unmanned aerial vehicle. An adaptive second order sliding mode control is proposed for improving performance under different operating conditions and is robust in presence of external disturbances. Moreover, this control does not require the knowledge of disturbance bounds and avoids overestimation of the control gains. Furthermore, in order to implement this controller, an extended observer is designed to estimate unmeasurable states as well as external disturbances. Additionally, sufficient conditions are given to guarantee the closed-loop stability of the observer based control. Finally, using a full 6 degree of freedom model, simulation results are obtained where the performance of the proposed method is compared against active disturbance rejection based on sliding mode control. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Limits to ground control in autonomous spacecraft

NASA Technical Reports Server (NTRS)

Wan, Alfred D. M.; Braspenning, Peter J.; Vreeswijk, Gerrard A. W.

1995-01-01

In this paper the autonomy concept used by ESA and NASA is critically evaluated. Moreover, a more proper ground control/spacecraft organizational structure is proposed on the basis of a new, more elaborated concept of autonomy. In an extended theoretical discussion its definitional properties and functionalities are established. The rather basic property of adaptivity leads to the categorization of behaviour into the modes of satisfaction and avoidance behaviour. However, the autonomy property with the most profound consequences is goal-robustness. The mechanism that implements goal-robustness tests newly generated goals and externally received goals on consistency with high-level goals. If goals appear not to be good instantiations or more acceptable replacements of existing goals, they are rejected. This means that ground control has to cooperate with the spacecraft instead of (intermittently) commanding it.

Robust passive control for a class of uncertain neutral systems based on sliding mode observer.

PubMed

Liu, Zhen; Zhao, Lin; Kao, Yonggui; Gao, Cunchen

2017-01-01

The passivity-based sliding mode control (SMC) problem for a class of uncertain neutral systems with unmeasured states is investigated. Firstly, a particular non-fragile state observer is designed to generate the estimations of the system states, based upon which a novel integral-type sliding surface function is established for the control process. Secondly, a new sufficient condition for robust asymptotic stability and passivity of the resultant sliding mode dynamics (SMDs) is obtained in terms of linear matrix inequalities (LMIs). Thirdly, the finite-time reachability of the predesigned sliding surface is ensured by resorting to a novel adaptive SMC law. Finally, the validity and superiority of the scheme are justified via several examples. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Robust design of a 2-DOF GMV controller: a direct self-tuning and fuzzy scheduling approach.

PubMed

Silveira, Antonio S; Rodríguez, Jaime E N; Coelho, Antonio A R

2012-01-01

This paper presents a study on self-tuning control strategies with generalized minimum variance control in a fixed two degree of freedom structure-or simply GMV2DOF-within two adaptive perspectives. One, from the process model point of view, using a recursive least squares estimator algorithm for direct self-tuning design, and another, using a Mamdani fuzzy GMV2DOF parameters scheduling technique based on analytical and physical interpretations from robustness analysis of the system. Both strategies are assessed by simulation and real plants experimentation environments composed of a damped pendulum and an under development wind tunnel from the Department of Automation and Systems of the Federal University of Santa Catarina. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Flight control with adaptive critic neural network

NASA Astrophysics Data System (ADS)

Han, Dongchen

2001-10-01

In this dissertation, the adaptive critic neural network technique is applied to solve complex nonlinear system control problems. Based on dynamic programming, the adaptive critic neural network can embed the optimal solution into a neural network. Though trained off-line, the neural network forms a real-time feedback controller. Because of its general interpolation properties, the neurocontroller has inherit robustness. The problems solved here are an agile missile control for U.S. Air Force and a midcourse guidance law for U.S. Navy. In the first three papers, the neural network was used to control an air-to-air agile missile to implement a minimum-time heading-reverse in a vertical plane corresponding to following conditions: a system without constraint, a system with control inequality constraint, and a system with state inequality constraint. While the agile missile is a one-dimensional problem, the midcourse guidance law is the first test-bed for multiple-dimensional problem. In the fourth paper, the neurocontroller is synthesized to guide a surface-to-air missile to a fixed final condition, and to a flexible final condition from a variable initial condition. In order to evaluate the adaptive critic neural network approach, the numerical solutions for these cases are also obtained by solving two-point boundary value problem with a shooting method. All of the results showed that the adaptive critic neural network could solve complex nonlinear system control problems.

Control design based on dead-zone and leakage adaptive laws for artificial swarm mechanical systems

NASA Astrophysics Data System (ADS)

Zhao, Xiaomin; Chen, Y. H.; Zhao, Han

2017-05-01

We consider the control design of artificial swarm systems with emphasis on four characteristics. First, the agent is made of mechanical components. As a result, the motion of each agent is subject to physical laws that govern mechanical systems. Second, both nonlinearity and uncertainty of the mechanical system are taken into consideration. Third, the ideal agent kinematic performance is treated as a desired d'Alembert constraint. This in turn suggests a creative way of embedding the constraint into the control design. Fourth, two types of adaptive robust control schemes are designed. They both contain leakage and dead-zone. However, one design suggests a trade-off between the amount of leakage and the size of dead-zone, in exchange for a simplified dead-zone structure.

Adaptive Fractional-order Control for Synchronization of Two Coupled Neurons in the External Electrical Stimulation

PubMed Central

Mehdiabadi, M. R. Rahmani; Rouhani, E.; Mashhadi, S. K. Mousavi; Jalali, A. A.

2014-01-01

This paper addresses synchronizing two coupled chaotic FitzHugh–Nagumo (FHN) neurons with weakly gap junction under external electrical stimulation (EES). To transmit information among coupled neurons, by generalization of the integer-order FHN equations of the coupled system into the fractional-order in frequency domain using Crone approach, the behavior of each coupled neuron relies on its past behavior and the memorized system can be a better fit for the neuron response. An adaptive fractional-order controller based on the Lyaponuv stability theory was designed to synchronize two neurons electrically coupled with gap junction in EES. The proposed controller is also robust to the inevitable random noise such as disturbances of ionic channels. The simulation results demonstrate the effectiveness of the control scheme. PMID:25337373

Robust Control Analysis of Hydraulic Turbine Speed

NASA Astrophysics Data System (ADS)

Jekan, P.; Subramani, C.

2018-04-01

An effective control strategy for the hydro-turbine governor in time scenario is adjective for this paper. Considering the complex dynamic characteristic and the uncertainty of the hydro-turbine governor model and taking the static and dynamic performance of the governing system as the ultimate goal, the designed logic combined the classical PID control theory with artificial intelligence used to obtain the desired output. The used controller will be a variable control techniques, therefore, its parameters can be adaptively adjusted according to the information about the control error signal.

Robustness and management adaptability in tropical rangelands: a viability-based assessment under the non-equilibrium paradigm.

PubMed

Accatino, F; Sabatier, R; De Michele, C; Ward, D; Wiegand, K; Meyer, K M

2014-08-01

Rangelands provide the main forage resource for livestock in many parts of the world, but maintaining long-term productivity and providing sufficient income for the rancher remains a challenge. One key issue is to maintain the rangeland in conditions where the rancher has the greatest possibility to adapt his/her management choices to a highly fluctuating and uncertain environment. In this study, we address management robustness and adaptability, which increase the resilience of a rangeland. After reviewing how the concept of resilience evolved in parallel to modelling views on rangelands, we present a dynamic model of rangelands to which we applied the mathematical framework of viability theory to quantify the management adaptability of the system in a stochastic environment. This quantification is based on an index that combines the robustness of the system to rainfall variability and the ability of the rancher to adjust his/her management through time. We evaluated the adaptability for four possible scenarios combining two rainfall regimes (high or low) with two herding strategies (grazers only or mixed herd). Results show that pure grazing is viable only for high-rainfall regimes, and that the use of mixed-feeder herds increases the adaptability of the management. The management is the most adaptive with mixed herds and in rangelands composed of an intermediate density of trees and grasses. In such situations, grass provides high quantities of biomass and woody plants ensure robustness to droughts. Beyond the implications for management, our results illustrate the relevance of viability theory for addressing the issue of robustness and adaptability in non-equilibrium environments.

A Study on Application of Fuzzy Adaptive Unscented Kalman Filter to Nonlinear Turbojet Engine Control

NASA Astrophysics Data System (ADS)

Han, Dongju

2018-05-01

Safe and efficient flight powered by an aircraft turbojet engine relies on the performance of the engine controller preventing compressor surge with robustness from noises or disturbances. This paper proposes the effective nonlinear controller associated with the nonlinear filter for the real turbojet engine with highly nonlinear dynamics. For the feasible controller study the nonlinearity of the engine dynamics was investigated by comparing the step responses from the linearized model with the original nonlinear dynamics. The fuzzy-based PID control logic is introduced to control the engine efficiently and FAUKF is applied for robustness from noises. The simulation results prove the effectiveness of FAUKF applied to the proposed controller such that the control performances are superior over the conventional controller and the filer performance using FAUKF indicates the satisfactory results such as clearing the defects by reducing the distortions without compressor surge, whereas the conventional UKF is not fully effective as occurring some distortions with compressor surge due to a process noise.

Adaptive super twisting vibration control of a flexible spacecraft with state rate estimation

NASA Astrophysics Data System (ADS)

Malekzadeh, Maryam; Karimpour, Hossein

2018-05-01

The robust attitude and vibration control of a flexible spacecraft trying to perform accurate maneuvers in spite of various sources of uncertainty is addressed here. Difficulties for achieving precise and stable pointing arise from noisy onboard sensors, parameters indeterminacy, outer disturbances as well as un-modeled or hidden dynamics interactions. Based on high-order sliding-mode methods, the non-minimum phase nature of the problem is dealt with through output redefinition. An adaptive super-twisting algorithm (ASTA) is incorporated with its observer counterpart on the system under consideration to get reliable attitude and vibration control in the presence of sensor noise and momentum coupling. The closed-loop efficiency is verified through simulations under various indeterminate situations and got compared to other methods.

Adaptive torque estimation of robot joint with harmonic drive transmission

NASA Astrophysics Data System (ADS)

Shi, Zhiguo; Li, Yuankai; Liu, Guangjun

2017-11-01

Robot joint torque estimation using input and output position measurements is a promising technique, but the result may be affected by the load variation of the joint. In this paper, a torque estimation method with adaptive robustness and optimality adjustment according to load variation is proposed for robot joint with harmonic drive transmission. Based on a harmonic drive model and a redundant adaptive robust Kalman filter (RARKF), the proposed approach can adapt torque estimation filtering optimality and robustness to the load variation by self-tuning the filtering gain and self-switching the filtering mode between optimal and robust. The redundant factor of RARKF is designed as a function of the motor current for tolerating the modeling error and load-dependent filtering mode switching. The proposed joint torque estimation method has been experimentally studied in comparison with a commercial torque sensor and two representative filtering methods. The results have demonstrated the effectiveness of the proposed torque estimation technique.

Adaptive learning and control for MIMO system based on adaptive dynamic programming.

PubMed

Fu, Jian; He, Haibo; Zhou, Xinmin

2011-07-01

Adaptive dynamic programming (ADP) is a promising research field for design of intelligent controllers, which can both learn on-the-fly and exhibit optimal behavior. Over the past decades, several generations of ADP design have been proposed in the literature, which have demonstrated many successful applications in various benchmarks and industrial applications. While many of the existing researches focus on multiple-inputs-single-output system with steepest descent search, in this paper we investigate a generalized multiple-input-multiple-output (GMIMO) ADP design for online learning and control, which is more applicable to a wide range of practical real-world applications. Furthermore, an improved weight-updating algorithm based on recursive Levenberg-Marquardt methods is presented and embodied in the GMIMO approach to improve its performance. Finally, we test the performance of this approach based on a practical complex system, namely, the learning and control of the tension and height of the looper system in a hot strip mill. Experimental results demonstrate that the proposed approach can achieve effective and robust performance.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alvarez-Ramirez, J.; Aguilar, R.; Lopez-Isunza, F.

FCC processes involve complex interactive dynamics which are difficult to operate and control as well as poorly known reaction kinetics. This work concerns the synthesis of temperature controllers for FCC units. The problem is addressed first for the case where perfect knowledge of the reaction kinetics is assumed, leading to an input-output linearizing state feedback. However, in most industrial FCC units, perfect knowledge of reaction kinetics and composition measurements is not available. To address the problem of robustness against uncertainties in the reaction kinetics, an adaptive model-based nonlinear controller with simplified reaction models is presented. The adaptive strategy makes usemore » of estimates of uncertainties derived from calorimetric (energy) balances. The resulting controller is similar in form to standard input-output linearizing controllers and can be tuned analogously. Alternatively, the controller can be tuned using a single gain parameter and is computationally efficient. The performance of the closed-loop system and the controller design procedure are shown with simulations.« less

Decentralized robust nonlinear model predictive controller for unmanned aerial systems

NASA Astrophysics Data System (ADS)

Garcia Garreton, Gonzalo A.

The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1. A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2. A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3. An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4. A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible.

Tracking control of air-breathing hypersonic vehicles with non-affine dynamics via improved neural back-stepping design.

PubMed

Bu, Xiangwei; He, Guangjun; Wang, Ke

2018-04-01

This study considers the design of a new back-stepping control approach for air-breathing hypersonic vehicle (AHV) non-affine models via neural approximation. The AHV's non-affine dynamics is decomposed into velocity subsystem and altitude subsystem to be controlled separately, and robust adaptive tracking control laws are developed using improved back-stepping designs. Neural networks are applied to estimate the unknown non-affine dynamics, which guarantees the addressed controllers with satisfactory robustness against uncertainties. In comparison with the existing control methodologies, the special contributions are that the non-affine issue is handled by constructing two low-pass filters based on model transformations, and virtual controllers are treated as intermediate variables such that they aren't needed for back-stepping designs any more. Lyapunov techniques are employed to show the uniformly ultimately boundedness of all closed-loop signals. Finally, simulation results are presented to verify the tracking performance and superiorities of the investigated control strategy. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Integrated multiple-model adaptive fault identification and reconfigurable fault-tolerant control for Lead-Wing close formation systems

NASA Astrophysics Data System (ADS)

Liu, Chun; Jiang, Bin; Zhang, Ke

2018-03-01

This paper investigates the attitude and position tracking control problem for Lead-Wing close formation systems in the presence of loss of effectiveness and lock-in-place or hardover failure. In close formation flight, Wing unmanned aerial vehicle movements are influenced by vortex effects of the neighbouring Lead unmanned aerial vehicle. This situation allows modelling of aerodynamic coupling vortex-effects and linearisation based on optimal close formation geometry. Linearised Lead-Wing close formation model is transformed into nominal robust H-infinity models with respect to Mach hold, Heading hold, and Altitude hold autopilots; static feedback H-infinity controller is designed to guarantee effective tracking of attitude and position while manoeuvring Lead unmanned aerial vehicle. Based on H-infinity control design, an integrated multiple-model adaptive fault identification and reconfigurable fault-tolerant control scheme is developed to guarantee asymptotic stability of close-loop systems, error signal boundedness, and attitude and position tracking properties. Simulation results for Lead-Wing close formation systems validate the efficiency of the proposed integrated multiple-model adaptive control algorithm.

A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem

NASA Astrophysics Data System (ADS)

Chen, Xianshun; Feng, Liang; Ong, Yew Soon

2012-07-01

In this article, we proposed a self-adaptive memeplex robust search (SAMRS) for finding robust and reliable solutions that are less sensitive to stochastic behaviours of customer demands and have low probability of route failures, respectively, in vehicle routing problem with stochastic demands (VRPSD). In particular, the contribution of this article is three-fold. First, the proposed SAMRS employs the robust solution search scheme (RS 3) as an approximation of the computationally intensive Monte Carlo simulation, thus reducing the computation cost of fitness evaluation in VRPSD, while directing the search towards robust and reliable solutions. Furthermore, a self-adaptive individual learning based on the conceptual modelling of memeplex is introduced in the SAMRS. Finally, SAMRS incorporates a gene-meme co-evolution model with genetic and memetic representation to effectively manage the search for solutions in VRPSD. Extensive experimental results are then presented for benchmark problems to demonstrate that the proposed SAMRS serves as an efficable means of generating high-quality robust and reliable solutions in VRPSD.

Real-time tracking control of electro-hydraulic force servo systems using offline feedback control and adaptive control.

PubMed

Shen, Gang; Zhu, Zhencai; Zhao, Jinsong; Zhu, Weidong; Tang, Yu; Li, Xiang

2017-03-01

This paper focuses on an application of an electro-hydraulic force tracking controller combined with an offline designed feedback controller (ODFC) and an online adaptive compensator in order to improve force tracking performance of an electro-hydraulic force servo system (EHFS). A proportional-integral controller has been employed and a parameter-based force closed-loop transfer function of the EHFS is identified by a continuous system identification algorithm. By taking the identified system model as a nominal plant model, an H ∞ offline design method is employed to establish an optimized feedback controller with consideration of the performance, control efforts, and robustness of the EHFS. In order to overcome the disadvantage of the offline designed controller and cope with the varying dynamics of the EHFS, an online adaptive compensator with a normalized least-mean-square algorithm is cascaded to the force closed-loop system of the EHFS compensated by the ODFC. Some comparative experiments are carried out on a real-time EHFS using an xPC rapid prototype technology, and the proposed controller yields a better force tracking performance improvement. Copyright © 2016. Published by Elsevier Ltd.

Quantum Transduction with Adaptive Control

NASA Astrophysics Data System (ADS)

Zhang, Mengzhen; Zou, Chang-Ling; Jiang, Liang

2018-01-01

Quantum transducers play a crucial role in hybrid quantum networks. A good quantum transducer can faithfully convert quantum signals from one mode to another with minimum decoherence. Most investigations of quantum transduction are based on the protocol of direct mode conversion. However, the direct protocol requires the matching condition, which in practice is not always feasible. Here we propose an adaptive protocol for quantum transducers, which can convert quantum signals without requiring the matching condition. The adaptive protocol only consists of Gaussian operations, feasible in various physical platforms. Moreover, we show that the adaptive protocol can be robust against imperfections associated with finite squeezing, thermal noise, and homodyne detection, and it can be implemented to realize quantum state transfer between microwave and optical modes.

Quantum Transduction with Adaptive Control.

PubMed

Zhang, Mengzhen; Zou, Chang-Ling; Jiang, Liang

2018-01-12

Quantum transducers play a crucial role in hybrid quantum networks. A good quantum transducer can faithfully convert quantum signals from one mode to another with minimum decoherence. Most investigations of quantum transduction are based on the protocol of direct mode conversion. However, the direct protocol requires the matching condition, which in practice is not always feasible. Here we propose an adaptive protocol for quantum transducers, which can convert quantum signals without requiring the matching condition. The adaptive protocol only consists of Gaussian operations, feasible in various physical platforms. Moreover, we show that the adaptive protocol can be robust against imperfections associated with finite squeezing, thermal noise, and homodyne detection, and it can be implemented to realize quantum state transfer between microwave and optical modes.

Active control strategy for the running attitude of high-speed train under strong crosswind condition

NASA Astrophysics Data System (ADS)

Li, Decang; Meng, Jianjun; Bai, Huan; Xu, Ruxun

2018-07-01

This paper focuses on the safety of high-speed trains under strong crosswind conditions. A new active control strategy is proposed based on the adaptive predictive control theory. The new control strategy aims at adjusting the attitudes of a train by controlling the new-type intelligent giant magnetostrictive actuator (GMA). It combined adaptive control with dynamic matrix control; parameters of predictive controller was real-time adjusted by online distinguishing to enhance the robustness of the control algorithm. On this basis, a correction control algorithm is also designed to regulate the parameters of predictive controller based on the step response of a controlled objective. Finally, the simulation results show that the proposed control strategy can adjust the running attitudes of high-speed trains under strong crosswind conditions; they also indicate that the new active control strategy is effective and applicable in improving the safety performance of a train based on a host-target computer technology provided by Matlab/Simulink.

Experimental Verification of Electric Drive Technologies Based on Artificial Intelligence Tools

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed; Ricketts, Daniel; Kotaru, Raj; Thomas, Robert; Noga, Donald F. (Technical Monitor); Kankam, Mark D. (Technical Monitor)

2000-01-01

In this report, a fully integrated prototype of a flight servo control system is successfully developed and implemented using brushless dc motors. The control system is developed by the fuzzy logic theory, and implemented with a multilayer neural network. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. The network structure and the parameter learning are performed simultaneously and online in the fuzzy-neural network system. The structure learning is based on the partition of input space. The parameter learning is based on the supervised gradient decent method, using a delta adaptation law. Using experimental setup, the performance of the proposed control system is evaluated under various operating conditions. Test results are presented and discussed in the report. The proposed learning control system has several advantages, namely, simple structure and learning capability, robustness and high tracking performance and few nodes at hidden layers. In comparison with the PI controller, the proposed fuzzy-neural network system can yield a better dynamic performance with shorter settling time, and without overshoot. Experimental results have shown that the proposed control system is adaptive and robust in responding to a wide range of operating conditions. In summary, the goal of this study is to design and implement-advanced servosystems to actuate control surfaces for flight vehicles, namely, aircraft and helicopters, missiles and interceptors, and mini- and micro-air vehicles.

Analysis and Synthesis of Robust Data Structures

DTIC Science & Technology

1990-08-01

1.3.2 Multiversion Software. .. .. .. .. .. .... .. ... .. ...... 5 1.3.3 Robust Data Structure .. .. .. .. .. .. .. .. .. ... .. ..... 6 1.4...context are 0 multiversion software, which is an adaptation oi N-modulo redundancy (NMR) tech- nique. * recovery blocks, which is an adaptation of...implementations using these features for such a hybrid approach. 1.3.2 Multiversion Software Avizienis [AC77] was the first to adapt NMR technique into

Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

PubMed Central

Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin

2013-01-01

Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines. PMID:23408775

The topological requirements for robust perfect adaptation in networks of any size.

PubMed

Araujo, Robyn P; Liotta, Lance A

2018-05-01

Robustness, and the ability to function and thrive amid changing and unfavorable environments, is a fundamental requirement for living systems. Until now it has been an open question how large and complex biological networks can exhibit robust behaviors, such as perfect adaptation to a variable stimulus, since complexity is generally associated with fragility. Here we report that all networks that exhibit robust perfect adaptation (RPA) to a persistent change in stimulus are decomposable into well-defined modules, of which there exist two distinct classes. These two modular classes represent a topological basis for all RPA-capable networks, and generate the full set of topological realizations of the internal model principle for RPA in complex, self-organizing, evolvable bionetworks. This unexpected result supports the notion that evolutionary processes are empowered by simple and scalable modular design principles that promote robust performance no matter how large or complex the underlying networks become.

The Researching on Evaluation of Automatic Voltage Control Based on Improved Zoning Methodology

NASA Astrophysics Data System (ADS)

Xiao-jun, ZHU; Ang, FU; Guang-de, DONG; Rui-miao, WANG; De-fen, ZHU

2018-03-01

According to the present serious phenomenon of increasing size and structure of power system, hierarchically structured automatic voltage control(AVC) has been the researching spot. In the paper, the reduced control model is built and the adaptive reduced control model is researched to improve the voltage control effect. The theories of HCSD, HCVS, SKC and FCM are introduced and the effect on coordinated voltage regulation caused by different zoning methodologies is also researched. The generic framework for evaluating performance of coordinated voltage regulation is built. Finally, the IEEE-96 stsyem is used to divide the network. The 2383-bus Polish system is built to verify that the selection of a zoning methodology affects not only the coordinated voltage regulation operation, but also its robustness to erroneous data and proposes a comprehensive generic framework for evaluating its performance. The New England 39-bus network is used to verify the adaptive reduced control models’ performance.

H.264/AVC digital fingerprinting based on spatio-temporal just noticeable distortion

NASA Astrophysics Data System (ADS)

Ait Saadi, Karima; Bouridane, Ahmed; Guessoum, Abderrezak

2014-01-01

This paper presents a robust adaptive embedding scheme using a modified Spatio-Temporal noticeable distortion (JND) model that is designed for tracing the distribution of the H.264/AVC video content and protecting them from unauthorized redistribution. The Embedding process is performed during coding process in selected macroblocks type Intra 4x4 within I-Frame. The method uses spread-spectrum technique in order to obtain robustness against collusion attacks and the JND model to dynamically adjust the embedding strength and control the energy of the embedded fingerprints so as to ensure their imperceptibility. Linear and non linear collusion attacks are performed to show the robustness of the proposed technique against collusion attacks while maintaining visual quality unchanged.

Identification of robust adaptation gene regulatory network parameters using an improved particle swarm optimization algorithm.

PubMed

Huang, X N; Ren, H P

2016-05-13

Robust adaptation is a critical ability of gene regulatory network (GRN) to survive in a fluctuating environment, which represents the system responding to an input stimulus rapidly and then returning to its pre-stimulus steady state timely. In this paper, the GRN is modeled using the Michaelis-Menten rate equations, which are highly nonlinear differential equations containing 12 undetermined parameters. The robust adaption is quantitatively described by two conflicting indices. To identify the parameter sets in order to confer the GRNs with robust adaptation is a multi-variable, multi-objective, and multi-peak optimization problem, which is difficult to acquire satisfactory solutions especially high-quality solutions. A new best-neighbor particle swarm optimization algorithm is proposed to implement this task. The proposed algorithm employs a Latin hypercube sampling method to generate the initial population. The particle crossover operation and elitist preservation strategy are also used in the proposed algorithm. The simulation results revealed that the proposed algorithm could identify multiple solutions in one time running. Moreover, it demonstrated a superior performance as compared to the previous methods in the sense of detecting more high-quality solutions within an acceptable time. The proposed methodology, owing to its universality and simplicity, is useful for providing the guidance to design GRN with superior robust adaptation.

Adaptive Control Model Reveals Systematic Feedback and Key Molecules in Metabolic Pathway Regulation

PubMed Central

Moffitt, Richard A.; Merrill, Alfred H.; Wang, May D.

2011-01-01

Abstract Robust behavior in metabolic pathways resembles stabilized performance in systems under autonomous control. This suggests we can apply control theory to study existing regulation in these cellular networks. Here, we use model-reference adaptive control (MRAC) to investigate the dynamics of de novo sphingolipid synthesis regulation in a combined theoretical and experimental case study. The effects of serine palmitoyltransferase over-expression on this pathway are studied in vitro using human embryonic kidney cells. We report two key results from comparing numerical simulations with observed data. First, MRAC simulations of pathway dynamics are comparable to simulations from a standard model using mass action kinetics. The root-sum-square (RSS) between data and simulations in both cases differ by less than 5%. Second, MRAC simulations suggest systematic pathway regulation in terms of adaptive feedback from individual molecules. In response to increased metabolite levels available for de novo sphingolipid synthesis, feedback from molecules along the main artery of the pathway is regulated more frequently and with greater amplitude than from other molecules along the branches. These biological insights are consistent with current knowledge while being new that they may guide future research in sphingolipid biology. In summary, we report a novel approach to study regulation in cellular networks by applying control theory in the context of robust metabolic pathways. We do this to uncover potential insight into the dynamics of regulation and the reverse engineering of cellular networks for systems biology. This new modeling approach and the implementation routines designed for this case study may be extended to other systems. Supplementary Material is available at www.liebertonline.com/cmb. PMID:21314456

Robustness via Run-Time Adaptation of Contingent Plans

NASA Technical Reports Server (NTRS)

Bresina, John L.; Washington, Richard; Norvig, Peter (Technical Monitor)

2000-01-01

In this paper, we discuss our approach to making the behavior of planetary rovers more robust for the purpose of increased productivity. Due to the inherent uncertainty in rover exploration, the traditional approach to rover control is conservative, limiting the autonomous operation of the rover and sacrificing performance for safety. Our objective is to increase the science productivity possible within a single uplink by allowing the rover's behavior to be specified with flexible, contingent plans and by employing dynamic plan adaptation during execution. We have deployed a system exhibiting flexible, contingent execution; this paper concentrates on our ongoing efforts on plan adaptation, Plans can be revised in two ways: plan steps may be deleted, with execution continuing with the plan suffix; and the current plan may be merged with an "alternate plan" from an on-board library. The plan revision action is chosen to maximize the expected utility of the plan. Plan merging and action deletion constitute a more conservative general-purpose planning system; in return, our approach is more efficient and more easily verified, two important criteria for deployed rovers.

Precision pointing of scientific instruments on space station: The LFGGREC perspective

NASA Technical Reports Server (NTRS)

Blackwell, C. C.; Sirlin, S. W.; Laskin, R. A.

1988-01-01

An application of Lyapunov function-gradient-generated robustness-enhancing control (LFGGREC) is explored. The attention is directed to a reduced-complexity representation of the pointing problem presented by the system composed of the Space Infrared Telescope Facility gimbaled to a space station configuration. Uncertainties include disturbance forces applied in the crew compartment area and control moments applied to adjacent scientific payloads (modeled as disturbance moments). Also included are uncertainties in gimbal friction and in the structural component of the system, as reflected in the inertia matrix, the damping matrix, and the stiffness matrix, and the effect of the ignored vibrational dynamics of the structure. The emphasis is on the adaptation of LFGGREC to this particular configuration and on the robustness analysis.

A novel adaptive control method for induction motor based on Backstepping approach using dSpace DS 1104 control board

NASA Astrophysics Data System (ADS)

Ben Regaya, Chiheb; Farhani, Fethi; Zaafouri, Abderrahmen; Chaari, Abdelkader

2018-02-01

This paper presents a new adaptive Backstepping technique to handle the induction motor (IM) rotor resistance tracking problem. The proposed solution leads to improve the robustness of the control system. Given the presence of static error when estimating the rotor resistance with classical methods, and the sensitivity to the load torque variation at low speed, a new Backstepping observer enhanced with an integral action of the tracking errors is presented, which can be established in two steps. The first one consists to estimate the rotor flux using a Backstepping observer. The second step, defines the adaptation mechanism of the rotor resistance based on the estimated rotor-flux. The asymptotic stability of the observer is proven by Lyapunov theory. To validate the proposed solution, a simulation and experimental benchmarking of a 3 kW induction motor are presented and analyzed. The obtained results show the effectiveness of the proposed solution compared to the model reference adaptive system (MRAS) rotor resistance observer presented in other recent works.

Epigenetic regulation of serotype expression antagonizes transcriptome dynamics in Paramecium tetraurelia

PubMed Central

Cheaib, Miriam; Dehghani Amirabad, Azim; Nordström, Karl J. V.; Schulz, Marcel H.; Simon, Martin

2015-01-01

Phenotypic variation of a single genotype is achieved by alterations in gene expression patterns. Regulation of such alterations depends on their time scale, where short-time adaptations differ from permanently established gene expression patterns maintained by epigenetic mechanisms. In the ciliate Paramecium, serotypes were described for an epigenetically controlled gene expression pattern of an individual multigene family. Paradoxically, individual serotypes can be triggered in Paramecium by alternating environments but are then stabilized by epigenetic mechanisms, thus raising the question to which extend their expression follows environmental stimuli. To characterize environmental adaptation in the context of epigenetically controlled serotype expression, we used RNA-seq to characterize transcriptomes of serotype pure cultures. The resulting vegetative transcriptome resource is first analysed for genes involved in the adaptive response to the altered environment. Secondly, we identified groups of genes that do not follow the adaptive response but show co-regulation with the epigenetically controlled serotype system, suggesting that their gene expression pattern becomes manifested by similar mechanisms. In our experimental set-up, serotype expression and the entire group of co-regulated genes were stable among environmental changes and only heat-shock genes altered expression of these gene groups. The data suggest that the maintenance of these gene expression patterns in a lineage represents epigenetically controlled robustness counteracting short-time adaptation processes. PMID:26231545

Relevance of phenotypic noise to adaptation and evolution.

PubMed

Kaneko, K; Furusawa, C

2008-09-01

Biological processes are inherently noisy, as highlighted in recent measurements of stochasticity in gene expression. Here, the authors show that such phenotypic noise is essential to the adaptation of organisms to a variety of environments and also to the evolution of robustness against mutations. First, the authors show that for any growing cell showing stochastic gene expression, the adaptive cellular state is inevitably selected by noise, without the use of a specific signal transduction network. In general, changes in any protein concentration in a cell are products of its synthesis minus dilution and degradation, both of which are proportional to the rate of cell growth. In an adaptive state, both the synthesis and dilution terms of proteins are large, and so the adaptive state is less affected by stochasticity in gene expression, whereas for a non-adaptive state, both terms are smaller, and so cells are easily knocked out of their original state by noise. This leads to a novel, generic mechanism for the selection of adaptive states. The authors have confirmed this selection by model simulations. Secondly, the authors consider the evolution of gene networks to acquire robustness of the phenotype against noise and mutation. Through simulations using a simple stochastic gene expression network that undergoes mutation and selection, the authors show that a threshold level of noise in gene expression is required for the network to acquire both types of robustness. The results reveal how the noise that cells encounter during growth and development shapes any network's robustness, not only to noise but also to mutations. The authors also establish a relationship between developmental and mutational robustness.

Parameters Identification for Motorcycle Simulator's Platform Characterization

NASA Astrophysics Data System (ADS)

Nehaoua, L.; Arioui, H.

2008-06-01

This paper presents the dynamics modeling and parameters identification of a motorcycle simulator's platform. This model begins with some suppositions which consider that the leg dynamics can be neglected with respect to the mobile platform one. The objectif is to synthesis a simplified control scheme, adapted to driving simulation application, minimising dealys and without loss of tracking performance. Electronic system of platform actuation is described. It's based on a CAN BUS communication which offers a large transmission robustness and error handling. Despite some disadvanteges, we adapted a control solution which overcome these inconvenients and preserve the quality of tracking trajectory. A bref description of the simulator's platform is given and results are shown and justified according to our specifications.

Suppression of the noise-induced effects in an electrostatic micro-plate using an adaptive back-stepping sliding mode control.

PubMed

Nwagoum Tuwa, Peguy Roussel; Woafo, P

2018-01-01

In this work, an adaptive backstepping sliding mode control approach is applied through the piezoelectric layer in order to control and to stabilize an electrostatic micro-plate. The mathematical model of the system by taking into account the small fluctuations in the gap considered as bounded noise is carried out. The accuracy of the proposed modal equation is proven using the method of lines. By using both approaches, the effects of noise are presented. It is found that they lead to pull-in instability as well as to random chaos. A suitable backstepping approach to improve the tracking performance is integrated to the adaptive sliding mode control in order to eliminate chattering phenomena and reinforce the robustness of the system in presence of uncertainties and external random disturbances. It is proved that all the variables of the closed-loop system are bounded and the system can follow the given reference signals as close as possible. Numerical simulations are provided to show the effectiveness of proposed controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Accelerated optimization and automated discovery with covariance matrix adaptation for experimental quantum control

NASA Astrophysics Data System (ADS)

Roslund, Jonathan; Shir, Ofer M.; Bäck, Thomas; Rabitz, Herschel

2009-10-01

Optimization of quantum systems by closed-loop adaptive pulse shaping offers a rich domain for the development and application of specialized evolutionary algorithms. Derandomized evolution strategies (DESs) are presented here as a robust class of optimizers for experimental quantum control. The combination of stochastic and quasi-local search embodied by these algorithms is especially amenable to the inherent topology of quantum control landscapes. Implementation of DES in the laboratory results in efficiency gains of up to ˜9 times that of the standard genetic algorithm, and thus is a promising tool for optimization of unstable or fragile systems. The statistical learning upon which these algorithms are predicated also provide the means for obtaining a control problem’s Hessian matrix with no additional experimental overhead. The forced optimal covariance adaptive learning (FOCAL) method is introduced to enable retrieval of the Hessian matrix, which can reveal information about the landscape’s local structure and dynamic mechanism. Exploitation of such algorithms in quantum control experiments should enhance their efficiency and provide additional fundamental insights.

Active fault tolerant control based on interval type-2 fuzzy sliding mode controller and non linear adaptive observer for 3-DOF laboratory helicopter.

PubMed

Zeghlache, Samir; Benslimane, Tarak; Bouguerra, Abderrahmen

2017-11-01

In this paper, a robust controller for a three degree of freedom (3 DOF) helicopter control is proposed in presence of actuator and sensor faults. For this purpose, Interval type-2 fuzzy logic control approach (IT2FLC) and sliding mode control (SMC) technique are used to design a controller, named active fault tolerant interval type-2 Fuzzy Sliding mode controller (AFTIT2FSMC) based on non-linear adaptive observer to estimate and detect the system faults for each subsystem of the 3-DOF helicopter. The proposed control scheme allows avoiding difficult modeling, attenuating the chattering effect of the SMC, reducing the rules number of the fuzzy controller. Exponential stability of the closed loop is guaranteed by using the Lyapunov method. The simulation results show that the AFTIT2FSMC can greatly alleviate the chattering effect, providing good tracking performance, even in presence of actuator and sensor faults. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A new composite adaptive controller featuring the neural network and prescribed sliding surface with application to vibration control

NASA Astrophysics Data System (ADS)

Phu, Do Xuan; Huy, Ta Duc; Mien, Van; Choi, Seung-Bok

2018-07-01

This work proposes a novel composite adaptive controller based on the prescribed performance of the sliding surface and applies it to vibration control of a semi-active vehicle seat suspension system subjected to severe external disturbances. As a first step, the online fast interval type 2 fuzzy neural network system is adopted to establish a model and two sliding surfaces are used; conventional surface and prescribed surface. Then, an equivalent control is determined by assuming the derivative of the prescribed surface is zero, followed by the design of a controller which can guarantee both stability and robustness. Then, two controllers are combined and integrated with adaptation laws using the projection algorithm. The effectiveness of the proposed composite controller is validated through both simulation and experiment by undertaking vibration control of a semi-active seat suspension system equipped with a magneto-rheological (MR) damper. It is shown from both simulation and experimental realization that excellent vibration control performances are achieved with a small tracking error between the proposed and prescribed objectives. In addition, the control superiority of the proposed controller to conventional sliding mode controller featuring one sliding surface and proportional-integral-derivative (PID) controllers are demonstrated through a comparative work.

Type-2 fuzzy logic control based MRAS speed estimator for speed sensorless direct torque and flux control of an induction motor drive.

PubMed

Ramesh, Tejavathu; Kumar Panda, Anup; Shiva Kumar, S

2015-07-01

In this research study, a model reference adaptive system (MRAS) speed estimator for speed sensorless direct torque and flux control (DTFC) of an induction motor drive (IMD) using two adaptation mechanism schemes are proposed to replace the conventional proportional integral controller (PIC). The first adaptation mechanism scheme is based on Type-1 fuzzy logic controller (T1FLC), which is used to achieve high performance sensorless drive in both transient as well as steady state conditions. However, the Type-1 fuzzy sets are certain and unable to work effectively when higher degree of uncertainties presents in the system which can be caused by sudden change in speed or different load disturbances, process noise etc. Therefore, a new Type-2 fuzzy logic controller (T2FLC) based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties and improves the performance and also robust to various load torque and sudden change in speed conditions, respectively. The detailed performances of various adaptation mechanism schemes are carried out in a MATLAB/Simulink environment with a speed sensor and speed sensorless modes of operation when an IMD is operating under different operating conditions, such as, no-load, load and sudden change in speed, respectively. To validate the different control approaches, the system also implemented on real-time system and adequate results are reported for its validation. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Robust design of configurations and parameters of adaptable products

NASA Astrophysics Data System (ADS)

Zhang, Jian; Chen, Yongliang; Xue, Deyi; Gu, Peihua

2014-03-01

An adaptable product can satisfy different customer requirements by changing its configuration and parameter values during the operation stage. Design of adaptable products aims at reducing the environment impact through replacement of multiple different products with single adaptable ones. Due to the complex architecture, multiple functional requirements, and changes of product configurations and parameter values in operation, impact of uncertainties to the functional performance measures needs to be considered in design of adaptable products. In this paper, a robust design approach is introduced to identify the optimal design configuration and parameters of an adaptable product whose functional performance measures are the least sensitive to uncertainties. An adaptable product in this paper is modeled by both configurations and parameters. At the configuration level, methods to model different product configuration candidates in design and different product configuration states in operation to satisfy design requirements are introduced. At the parameter level, four types of product/operating parameters and relations among these parameters are discussed. A two-level optimization approach is developed to identify the optimal design configuration and its parameter values of the adaptable product. A case study is implemented to illustrate the effectiveness of the newly developed robust adaptable design method.

Direct model reference adaptive control of robotic arms

NASA Technical Reports Server (NTRS)

Kaufman, Howard; Swift, David C.; Cummings, Steven T.; Shankey, Jeffrey R.

1993-01-01

The results of controlling A PUMA 560 Robotic Manipulator and the NASA shuttle Remote Manipulator System (RMS) using a Command Generator Tracker (CGT) based Model Reference Adaptive Controller (DMRAC) are presented. Initially, the DMRAC algorithm was run in simulation using a detailed dynamic model of the PUMA 560. The algorithm was tuned on the simulation and then used to control the manipulator using minimum jerk trajectories as the desired reference inputs. The ability to track a trajectory in the presence of load changes was also investigated in the simulation. Satisfactory performance was achieved in both simulation and on the actual robot. The obtained responses showed that the algorithm was robust in the presence of sudden load changes. Because these results indicate that the DMRAC algorithm can indeed be successfully applied to the control of robotic manipulators, additional testing was performed to validate the applicability of DMRAC to simulated dynamics of the shuttle RMS.

Indirect adaptive fuzzy fault-tolerant tracking control for MIMO nonlinear systems with actuator and sensor failures.

PubMed

Bounemeur, Abdelhamid; Chemachema, Mohamed; Essounbouli, Najib

2018-05-10

In this paper, an active fuzzy fault tolerant tracking control (AFFTTC) scheme is developed for a class of multi-input multi-output (MIMO) unknown nonlinear systems in the presence of unknown actuator faults, sensor failures and external disturbance. The developed control scheme deals with four kinds of faults for both sensors and actuators. The bias, drift, and loss of accuracy additive faults are considered along with the loss of effectiveness multiplicative fault. A fuzzy adaptive controller based on back-stepping design is developed to deal with actuator failures and unknown system dynamics. However, an additional robust control term is added to deal with sensor faults, approximation errors, and external disturbances. Lyapunov theory is used to prove the stability of the closed loop system. Numerical simulations on a quadrotor are presented to show the effectiveness of the proposed approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Comparison of adaptive critic-based and classical wide-area controllers for power systems.

PubMed

Ray, Swakshar; Venayagamoorthy, Ganesh Kumar; Chaudhuri, Balarko; Majumder, Rajat

2008-08-01

An adaptive critic design (ACD)-based damping controller is developed for a thyristor-controlled series capacitor (TCSC) installed in a power system with multiple poorly damped interarea modes. The performance of this ACD computational intelligence-based method is compared with two classical techniques, which are observer-based state-feedback (SF) control and linear matrix inequality LMI-H(infinity) robust control. Remote measurements are used as feedback signals to the wide-area damping controller for modulating the compensation of the TCSC. The classical methods use a linearized model of the system whereas the ACD method is purely measurement-based, leading to a nonlinear controller with fixed parameters. A comparative analysis of the controllers' performances is carried out under different disturbance scenarios. The ACD-based design has shown promising performance with very little knowledge of the system compared to classical model-based controllers. This paper also discusses the advantages and disadvantages of ACDs, SF, and LMI-H(infinity).

Intelligent nonsingular terminal sliding-mode control using MIMO Elman neural network for piezo-flexural nanopositioning stage.

PubMed

Lin, Faa-Jeng; Lee, Shih-Yang; Chou, Po-Huan

2012-12-01

The objective of this study is to develop an intelligent nonsingular terminal sliding-mode control (INTSMC) system using an Elman neural network (ENN) for the threedimensional motion control of a piezo-flexural nanopositioning stage (PFNS). First, the dynamic model of the PFNS is derived in detail. Then, to achieve robust, accurate trajectory-tracking performance, a nonsingular terminal sliding-mode control (NTSMC) system is proposed for the tracking of the reference contours. The steady-state response of the control system can be improved effectively because of the addition of the nonsingularity in the NTSMC. Moreover, to relax the requirements of the bounds and discard the switching function in NTSMC, an INTSMC system using a multi-input-multioutput (MIMO) ENN estimator is proposed to improve the control performance and robustness of the PFNS. The ENN estimator is proposed to estimate the hysteresis phenomenon and lumped uncertainty, including the system parameters and external disturbance of the PFNS online. Furthermore, the adaptive learning algorithms for the training of the parameters of the ENN online are derived using the Lyapunov stability theorem. In addition, two robust compensators are proposed to confront the minimum reconstructed errors in INTSMC. Finally, some experimental results for the tracking of various contours are given to demonstrate the validity of the proposed INTSMC system for PFNS.

Reservoir adaptive operating rules based on both of historical streamflow and future projections

NASA Astrophysics Data System (ADS)

Zhang, Wei; Liu, Pan; Wang, Hao; Chen, Jie; Lei, Xiaohui; Feng, Maoyuan

2017-10-01

Climate change is affecting hydrological variables and consequently is impacting water resources management. Historical strategies are no longer applicable under climate change. Therefore, adaptive management, especially adaptive operating rules for reservoirs, has been developed to mitigate the possible adverse effects of climate change. However, to date, adaptive operating rules are generally based on future projections involving uncertainties under climate change, yet ignoring historical information. To address this, we propose an approach for deriving adaptive operating rules considering both historical information and future projections, namely historical and future operating rules (HAFOR). A robustness index was developed by comparing benefits from HAFOR with benefits from conventional operating rules (COR). For both historical and future streamflow series, maximizations of both average benefits and the robustness index were employed as objectives, and four trade-offs were implemented to solve the multi-objective problem. Based on the integrated objective, the simulation-based optimization method was used to optimize the parameters of HAFOR. Using the Dongwushi Reservoir in China as a case study, HAFOR was demonstrated to be an effective and robust method for developing adaptive operating rules under the uncertain changing environment. Compared with historical or projected future operating rules (HOR or FPOR), HAFOR can reduce the uncertainty and increase the robustness for future projections, especially regarding results of reservoir releases and volumes. HAFOR, therefore, facilitates adaptive management in the context that climate change is difficult to predict accurately.

Transducer placement for robustness to variations in boundary conditions for active structural acoustic control

NASA Astrophysics Data System (ADS)

Sprofera, Joseph D.; Clark, Robert L.; Cabell, Randolph H.; Gibbs, Gary P.

2005-05-01

Turbulent boundary layer (TBL) noise is considered a primary contribution to the interior noise present in commercial airliners. There are numerous investigations of interior noise control devoted to aircraft panels; however, practical realization is a potential challenge since physical boundary conditions are uncertain at best. In most prior studies, pinned or clamped boundary conditions were assumed; however, realistic panels likely display a range of boundary conditions between these two limits. Uncertainty in boundary conditions is a challenge for control system designers, both in terms of the compensator implemented and the location of transducers required to achieve the desired control. The impact of model uncertainties, specifically uncertain boundaries, on the selection of transducer locations for structural acoustic control is considered herein. The final goal of this work is the design of an aircraft panel structure that can reduce TBL noise transmission through the use of a completely adaptive, single-input, single-output control system. The feasibility of this goal is demonstrated through the creation of a detailed analytical solution, followed by the implementation of a test model in a transmission loss apparatus. Successfully realizing a control system robust to variations in boundary conditions can lead to the design and implementation of practical adaptive structures that could be used to control the transmission of sound to the interior of aircraft. Results from this research effort indicate it is possible to optimize the design of actuator and sensor location and aperture, minimizing the impact of boundary conditions on the desired structural acoustic control.

Robust, Causal, and Incremental Approaches to Investigating Linguistic Adaptation

PubMed Central

Roberts, Seán G.

2018-01-01

This paper discusses the maximum robustness approach for studying cases of adaptation in language. We live in an age where we have more data on more languages than ever before, and more data to link it with from other domains. This should make it easier to test hypotheses involving adaptation, and also to spot new patterns that might be explained by adaptation. However, there is not much discussion of the overall approach to research in this area. There are outstanding questions about how to formalize theories, what the criteria are for directing research and how to integrate results from different methods into a clear assessment of a hypothesis. This paper addresses some of those issues by suggesting an approach which is causal, incremental and robust. It illustrates the approach with reference to a recent claim that dry environments select against the use of precise contrasts in pitch. Study 1 replicates a previous analysis of the link between humidity and lexical tone with an alternative dataset and finds that it is not robust. Study 2 performs an analysis with a continuous measure of tone and finds no significant correlation. Study 3 addresses a more recent analysis of the link between humidity and vowel use and finds that it is robust, though the effect size is small and the robustness of the measurement of vowel use is low. Methodological robustness of the general theory is addressed by suggesting additional approaches including iterated learning, a historical case study, corpus studies, and studying individual speech. PMID:29515487

Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties.

PubMed

Li, Zhijun; Su, Chun-Yi

2013-09-01

In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.

Designing Dynamic Adaptive Policy Pathways using Many-Objective Robust Decision Making

NASA Astrophysics Data System (ADS)

Kwakkel, Jan; Haasnoot, Marjolijn

2017-04-01

Dealing with climate risks in water management requires confronting a wide variety of deeply uncertain factors, while navigating a many dimensional space of trade-offs amongst objectives. There is an emerging body of literature on supporting this type of decision problem, under the label of decision making under deep uncertainty. Two approaches within this literature are Many-Objective Robust Decision Making, and Dynamic Adaptive Policy Pathways. In recent work, these approaches have been compared. One of the main conclusions of this comparison was that they are highly complementary. Many-Objective Robust Decision Making is a model based decision support approach, while Dynamic Adaptive Policy Pathways is primarily a conceptual framework for the design of flexible strategies that can be adapted over time in response to how the future is actually unfolding. In this research we explore this complementarity in more detail. Specifically, we demonstrate how Many-Objective Robust Decision Making can be used to design adaptation pathways. We demonstrate this combined approach using a water management problem, in the Netherlands. The water level of Lake IJselmeer, the main fresh water resource of the Netherlands, is currently managed through discharge by gravity. Due to climate change, this won't be possible in the future, unless water levels are changed. Changing the water level has undesirable flood risk and spatial planning consequences. The challenge is to find promising adaptation pathways that balance objectives related to fresh water supply, flood risk, and spatial issues, while accounting for uncertain climatic and land use change. We conclude that the combination of Many-Objective Robust Decision Making and Dynamic Adaptive Policy Pathways is particularly suited for dealing with deeply uncertain climate risks.

Command generator tracker based direct model reference adaptive tracking guidance for Mars atmospheric entry

NASA Astrophysics Data System (ADS)

Li, Shuang; Peng, Yuming

2012-01-01

In order to accurately deliver an entry vehicle through the Martian atmosphere to the prescribed parachute deployment point, active Mars entry guidance is essential. This paper addresses the issue of Mars atmospheric entry guidance using the command generator tracker (CGT) based direct model reference adaptive control to reduce the adverse effect of the bounded uncertainties on atmospheric density and aerodynamic coefficients. Firstly, the nominal drag acceleration profile meeting a variety of constraints is planned off-line in the longitudinal plane as the reference model to track. Then, the CGT based direct model reference adaptive controller and the feed-forward compensator are designed to robustly track the aforementioned reference drag acceleration profile and to effectively reduce the downrange error. Afterwards, the heading alignment logic is adopted in the lateral plane to reduce the crossrange error. Finally, the validity of the guidance algorithm proposed in this paper is confirmed by Monte Carlo simulation analysis.

Station Keeping of Small Outboard-Powered Boats

NASA Technical Reports Server (NTRS)

Fisher, A. D.; VanZwieten, J. H., Jr.; VanZwieten, T. S.

2010-01-01

Three station keeping controllers have been developed which work to minimize displacement of a small outboard-powered vessel from a desired location. Each of these three controllers has a common initial layer that uses fixed-gain feedback control to calculate the desired heading of the vessel. A second control layer uses a common fixed-gain feedback controller to calculate the net forward thrust, one of two algorithms for controlling engine angle (Fixed-Gain Proportional-integral-derivative (PID) or PID with Adaptively Augmented Gains), and one of two algorithms for differential throttle control (Fixed-Gain PID and PID with Adaptive Differential Throttle gains), which work together to eliminate heading error. The three selected controllers are evaluated using a numerical simulation of a 33-foot center console vessel with twin outboards that is subject to wave, wind, and current disturbances. Each controller is tested for its ability to maintain position in the presence of three sets of environmental disturbances. These algorithms were tested with current velocity of 1.5 m/s, significant wave height of 0.5 m, and wind speeds of 2, 5, and 10 m/s. These values were chosen to model conditions a small vessel may experience in the Gulf Stream off of Fort Lauderdale. The Fixed-gain PID controller progressively got worse as wind speeds increased, while the controllers using adaptive methodologies showed consistent performance over all weather conditions and decreased heading error by as much as 20%. Thus, enhanced robustness to environmental changes has been gained by using an adaptive algorithm.

The Brain as an Efficient and Robust Adaptive Learner.

PubMed

Denève, Sophie; Alemi, Alireza; Bourdoukan, Ralph

2017-06-07

Understanding how the brain learns to compute functions reliably, efficiently, and robustly with noisy spiking activity is a fundamental challenge in neuroscience. Most sensory and motor tasks can be described as dynamical systems and could presumably be learned by adjusting connection weights in a recurrent biological neural network. However, this is greatly complicated by the credit assignment problem for learning in recurrent networks, e.g., the contribution of each connection to the global output error cannot be determined based only on locally accessible quantities to the synapse. Combining tools from adaptive control theory and efficient coding theories, we propose that neural circuits can indeed learn complex dynamic tasks with local synaptic plasticity rules as long as they associate two experimentally established neural mechanisms. First, they should receive top-down feedbacks driving both their activity and their synaptic plasticity. Second, inhibitory interneurons should maintain a tight balance between excitation and inhibition in the circuit. The resulting networks could learn arbitrary dynamical systems and produce irregular spike trains as variable as those observed experimentally. Yet, this variability in single neurons may hide an extremely efficient and robust computation at the population level. Copyright © 2017 Elsevier Inc. All rights reserved.

Adaptation of postural recovery responses to a vestibular sensory illusion in individuals with Parkinson disease and healthy controls.

PubMed

Lester, Mark E; Cavanaugh, James T; Foreman, K Bo; Shaffer, Scott W; Marcus, Robin; Dibble, Leland E

2017-10-01

The ability to adapt postural responses to sensory illusions diminishes with age and is further impaired by Parkinson disease. However, limited information exists regarding training-related adaptions of sensory reweighting in these populations. This study sought to determine whether Parkinson disease or age would differentially affect acute postural recovery or adaptive postural responses to novel or repeated exposure to sensory illusions using galvanic vestibular stimulation during quiet stance. Acutely, individuals with Parkinson disease demonstrated larger center of pressure coefficient of variation compared to controls. Unlike individuals with Parkinson disease and asymptomatic older adults, healthy young adults acutely demonstrated a reduction in Sample Entropy to the sensory illusion. Following a period of consolidation Sample Entropy increased in the healthy young group, which coincided with a decreased center of pressure coefficient of variation. Similar changes were not observed in the Parkinson disease or older adult groups. Taken together, these results suggest that young adults learn to adapt to vestibular illusion in a more robust manner than older adults or those with Parkinson disease. Further investigation into the nature of this adaptive difference is warranted. Published by Elsevier Ltd.

Efficacy and feasibility of a novel tri-modal robust exercise prescription in a retirement community: a randomized, controlled trial.

PubMed

Baker, Michael K; Kennedy, David J; Bohle, Philip L; Campbell, Deena S; Knapman, Leona; Grady, Jodie; Wiltshire, James; McNamara, Maria; Evans, William J; Atlantis, Evan; Fiatarone Singh, Maria A

2007-01-01

To test the feasibility and efficacy of current guidelines for multimodal exercise programs in older adults. Randomized, controlled trial. Retirement village. Thirty-eight subjects (14 men and 24 women) aged 76.6 +/- 6.1. A wait list control or 10 weeks of supervised exercise consisting of high-intensity (80% of one-repetition maximum (1RM)) progressive resistance training (PRT) 3 days per week, moderate-intensity (rating of perceived exertion 11 to 14/20) aerobic training 2 days per week, and progressive balance training 1 day per week. Blinded assessments of dynamic muscle strength (1RM), balance, 6-minute walk, gait velocity, chair stand, stair climb, depressive symptoms, self-efficacy, and habitual physical activity level. Higher baseline strength and psychological well-being were associated with better functional performance. Strength gains over 10 weeks averaged 39+/-31% in exercise, versus 21+/-24% in controls (P=.10), with greater improvements in hip flexion (P=.01), hip abduction (P=.02), and chest press (P=.04) in the exercise group. Strength adaptations were greatest in exercises in which the intended continuous progressive overload was achieved. Stair climb power (12.3+/-15%, P=.002) and chair stand time (-7.1+/-15%, P=.006) improved significantly and similarly in both groups. Reduction in depressive symptoms was significantly related to compliance (attendance rate r=-0.568, P=.009, PRT progression in loading r=-0.587, P=.02, and total volume of aerobic training r=-0.541, P=.01), as well as improvements in muscle strength (r=-0.498, P=.002). Robust physical and psychological adaptations to exercise are linked, although volumes and intensities of multiple exercise modalities sufficient to cause significant adaptation appear difficult to prescribe and adhere to simultaneously in older adults.

Participatory adaptive management leads to environmental learning outcomes extending beyond the sphere of science.

PubMed

Fujitani, Marie; McFall, Andrew; Randler, Christoph; Arlinghaus, Robert

2017-06-01

Resolving uncertainties in managed social-ecological systems requires adaptive experimentation at whole-ecosystem levels. However, whether participatory adaptive management fosters ecological understanding among stakeholders beyond the sphere of science is unknown. We experimentally involved members of German angling clubs ( n = 181 in workshops, n = 2483 in total) engaged in self-governance of freshwater fisheries resources in a large-scale ecological experiment of active adaptive management of fish stocking, which constitutes a controversial management practice for biodiversity and ecosystem functioning when conducted inappropriately. The collaborative ecological experiments spanned several years and manipulated fish densities in 24 lakes with two species. In parallel, we experimentally compared changes in ecological knowledge and antecedents of proenvironmental behavior in stakeholders and managers who were members of a participatory adaptive management treatment group, with those receiving only a standard lecture, relative to placebo controls. Using a within-subjects pretest-posttest control design, changes in ecological knowledge, environmental beliefs, attitudes, norms, and behavioral intentions were evaluated. Participants in adaptive management retained more knowledge of ecological topics after a period of 8 months compared to those receiving a standard lecture, both relative to controls. Involvement in adaptive management was also the only treatment that altered personal norms and beliefs related to stocking. Critically, only the stakeholders who participated in adaptive management reduced their behavioral intentions to engage in fish stocking in the future. Adaptive management is essential for robust ecological knowledge, and we show that involving stakeholders in adaptive management experiments is a powerful tool to enhance ecological literacy and build environmental capacity to move toward sustainability.

Participatory adaptive management leads to environmental learning outcomes extending beyond the sphere of science

PubMed Central

Fujitani, Marie; McFall, Andrew; Randler, Christoph; Arlinghaus, Robert

2017-01-01

Resolving uncertainties in managed social-ecological systems requires adaptive experimentation at whole-ecosystem levels. However, whether participatory adaptive management fosters ecological understanding among stakeholders beyond the sphere of science is unknown. We experimentally involved members of German angling clubs (n = 181 in workshops, n = 2483 in total) engaged in self-governance of freshwater fisheries resources in a large-scale ecological experiment of active adaptive management of fish stocking, which constitutes a controversial management practice for biodiversity and ecosystem functioning when conducted inappropriately. The collaborative ecological experiments spanned several years and manipulated fish densities in 24 lakes with two species. In parallel, we experimentally compared changes in ecological knowledge and antecedents of proenvironmental behavior in stakeholders and managers who were members of a participatory adaptive management treatment group, with those receiving only a standard lecture, relative to placebo controls. Using a within-subjects pretest-posttest control design, changes in ecological knowledge, environmental beliefs, attitudes, norms, and behavioral intentions were evaluated. Participants in adaptive management retained more knowledge of ecological topics after a period of 8 months compared to those receiving a standard lecture, both relative to controls. Involvement in adaptive management was also the only treatment that altered personal norms and beliefs related to stocking. Critically, only the stakeholders who participated in adaptive management reduced their behavioral intentions to engage in fish stocking in the future. Adaptive management is essential for robust ecological knowledge, and we show that involving stakeholders in adaptive management experiments is a powerful tool to enhance ecological literacy and build environmental capacity to move toward sustainability. PMID:28630904

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Shaohua; School of Automation, Chongqing University, Chongqing 400044; Sun, Quanping

This paper addresses chaos control of the micro-electro- mechanical resonator by using adaptive dynamic surface technology with extended state observer. To reveal the mechanism of the micro- electro-mechanical resonator, the phase diagrams and corresponding time histories are given to research the nonlinear dynamics and chaotic behavior, and Homoclinic and heteroclinic chaos which relate closely with the appearance of chaos are presented based on the potential function. To eliminate the effect of chaos, an adaptive dynamic surface control scheme with extended state observer is designed to convert random motion into regular motion without precise system model parameters and measured variables. Puttingmore » tracking differentiator into chaos controller solves the ‘explosion of complexity’ of backstepping and poor precision of the first-order filters. Meanwhile, to obtain high performance, a neural network with adaptive law is employed to approximate unknown nonlinear function in the process of controller design. The boundedness of all the signals of the closed-loop system is proved in theoretical analysis. Finally, numerical simulations are executed and extensive results illustrate effectiveness and robustness of the proposed scheme.« less

Adaptive terminal sliding mode control for hypersonic flight vehicles with strictly lower convex function based nonlinear disturbance observer.

PubMed

Wu, Yun-Jie; Zuo, Jing-Xing; Sun, Liang-Hua

2017-11-01

In this paper, the altitude and velocity tracking control of a generic hypersonic flight vehicle (HFV) is considered. A novel adaptive terminal sliding mode controller (ATSMC) with strictly lower convex function based nonlinear disturbance observer (SDOB) is proposed for the longitudinal dynamics of HFV in presence of both parametric uncertainties and external disturbances. First, for the sake of enhancing the anti-interference capability, SDOB is presented to estimate and compensate the equivalent disturbances by introducing a strictly lower convex function. Next, the SDOB based ATSMC (SDOB-ATSMC) is proposed to guarantee the system outputs track the reference trajectory. Then, stability of the proposed control scheme is analyzed by the Lyapunov function method. Compared with other HFV control approaches, key novelties of SDOB-ATSMC are that a novel SDOB is proposed and drawn into the (virtual) control laws to compensate the disturbances and that several adaptive laws are used to deal with the differential explosion problem. Finally, it is illustrated by the simulation results that the new method exhibits an excellent robustness and a better disturbance rejection performance than the convention approach. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive extended-state observer-based fault tolerant attitude control for spacecraft with reaction wheels

NASA Astrophysics Data System (ADS)

Ran, Dechao; Chen, Xiaoqian; de Ruiter, Anton; Xiao, Bing

2018-04-01

This study presents an adaptive second-order sliding control scheme to solve the attitude fault tolerant control problem of spacecraft subject to system uncertainties, external disturbances and reaction wheel faults. A novel fast terminal sliding mode is preliminarily designed to guarantee that finite-time convergence of the attitude errors can be achieved globally. Based on this novel sliding mode, an adaptive second-order observer is then designed to reconstruct the system uncertainties and the actuator faults. One feature of the proposed observer is that the design of the observer does not necessitate any priori information of the upper bounds of the system uncertainties and the actuator faults. In view of the reconstructed information supplied by the designed observer, a second-order sliding mode controller is developed to accomplish attitude maneuvers with great robustness and precise tracking accuracy. Theoretical stability analysis proves that the designed fault tolerant control scheme can achieve finite-time stability of the closed-loop system, even in the presence of reaction wheel faults and system uncertainties. Numerical simulations are also presented to demonstrate the effectiveness and superiority of the proposed control scheme over existing methodologies.

Linear matrix inequality-based nonlinear adaptive robust control with application to unmanned aircraft systems

NASA Astrophysics Data System (ADS)

Kun, David William

Unmanned aircraft systems (UASs) are gaining popularity in civil and commercial applications as their lightweight on-board computers become more powerful and affordable, their power storage devices improve, and the Federal Aviation Administration addresses the legal and safety concerns of integrating UASs in the national airspace. Consequently, many researchers are pursuing novel methods to control UASs in order to improve their capabilities, dependability, and safety assurance. The nonlinear control approach is a common choice as it offers several benefits for these highly nonlinear aerospace systems (e.g., the quadrotor). First, the controller design is physically intuitive and is derived from well known dynamic equations. Second, the final control law is valid in a larger region of operation, including far from the equilibrium states. And third, the procedure is largely methodical, requiring less expertise with gain tuning, which can be arduous for a novice engineer. Considering these facts, this thesis proposes a nonlinear controller design method that combines the advantages of adaptive robust control (ARC) with the powerful design tools of linear matrix inequalities (LMI). The ARC-LMI controller is designed with a discontinuous projection-based adaptation law, and guarantees a prescribed transient and steady state tracking performance for uncertain systems in the presence of matched disturbances. The norm of the tracking error is bounded by a known function that depends on the controller design parameters in a known form. Furthermore, the LMI-based part of the controller ensures the stability of the system while overcoming polytopic uncertainties, and minimizes the control effort. This can reduce the number of parameters that require adaptation, and helps to avoid control input saturation. These desirable characteristics make the ARC-LMI control algorithm well suited for the quadrotor UAS, which may have unknown parameters and may encounter external disturbances such as wind gusts and turbulence. This thesis develops the ARC-LMI attitude and position controllers for an X-configuration quadrotor helicopter. The inner-loop of the autopilot controls the attitude and altitude of the quadrotor, and the outer-loop controls its position in the earth-fixed coordinate frame. Furthermore, by intelligently generating a smooth trajectory from the given reference coordinates (waypoints), the transient performance is improved. The simulation results indicate that the ARC-LMI controller design is useful for a variety of quadrotor applications, including precise trajectory tracking, autonomous waypoint navigation in the presence of disturbances, and package delivery without loss of performance.

A Low Cost Approach to the Design of Autopilot for Hypersonic Glider

NASA Astrophysics Data System (ADS)

Liang, Wang; Weihua, Zhang; Ke, Peng; Donghui, Wang

2017-12-01

This paper proposes a novel integrated guidance and control (IGC) approach to improve the autopilot design with low cost for hypersonic glider in dive and pull-up phase. The main objective is robust and adaptive tracking of flight path angle (FPA) under severe flight scenarios. Firstly, the nonlinear IGC model is developed with a second order actuator dynamics. Then the adaptive command filtered back-stepping control is implemented to deal with the large aerodynamics coefficient uncertainties, control surface uncertainties and unmatched time-varying disturbances. For the autopilot, a back-stepping sliding mode control is designed to track the control surface deflection, and a nonlinear differentiator is used to avoid direct differentiating the control input. Through a series of 6-DOF numerical simulations, it’s shown that the proposed scheme successfully cancels out the large uncertainties and disturbances in tracking different kinds of FPA trajectory. The contribution of this paper lies in the application and determination of nonlinear integrated design of guidance and control system for hypersonic glider.

An adaptive nonlinear internal-model control for the speed control of homopolar salient-pole BLDC motor

NASA Astrophysics Data System (ADS)

CheshmehBeigi, Hassan Moradi

2018-05-01

In this paper, a novel speed control method for Homopolar Brushless DC (HBLDC) motor based on the adaptive nonlinear internal-model control (ANIMC) is presented. Rotor position information is obtained online by the Hall-Effect sensors placed on the motor's shaft, and is used to calculate the accurate model and accurate inverse model of the HBLDC motor. The online inverse model of the motor is used in the controller structure. To suppress the reference ? error, the negative feedback of difference between the motor speed and its model output ? is applied in the proposed controller. An appropriate signal is the output of the controller, which drives the power switches to converge the motor speed to the constant desired speed. Simulations and experiments are carried out on a ? three-phase HBLDC motor. The proposed drive system operates well in the speed response and has good robustness with respect to the disturbances. To validate the theoretical analysis, several experimental results are discussed in this paper.

Novel neural control for a class of uncertain pure-feedback systems.

PubMed

Shen, Qikun; Shi, Peng; Zhang, Tianping; Lim, Cheng-Chew

2014-04-01

This paper is concerned with the problem of adaptive neural tracking control for a class of uncertain pure-feedback nonlinear systems. Using the implicit function theorem and backstepping technique, a practical robust adaptive neural control scheme is proposed to guarantee that the tracking error converges to an adjusted neighborhood of the origin by choosing appropriate design parameters. In contrast to conventional Lyapunov-based design techniques, an alternative Lyapunov function is constructed for the development of control law and learning algorithms. Differing from the existing results in the literature, the control scheme does not need to compute the derivatives of virtual control signals at each step in backstepping design procedures. Furthermore, the scheme requires the desired trajectory and its first derivative rather than its first n derivatives. In addition, the useful property of the basis function of the radial basis function, which will be used in control design, is explored. Simulation results illustrate the effectiveness of the proposed techniques.

Adaptive Core Simulation Employing Discrete Inverse Theory - Part II: Numerical Experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abdel-Khalik, Hany S.; Turinsky, Paul J.

2005-07-15

Use of adaptive simulation is intended to improve the fidelity and robustness of important core attribute predictions such as core power distribution, thermal margins, and core reactivity. Adaptive simulation utilizes a selected set of past and current reactor measurements of reactor observables, i.e., in-core instrumentation readings, to adapt the simulation in a meaningful way. The companion paper, ''Adaptive Core Simulation Employing Discrete Inverse Theory - Part I: Theory,'' describes in detail the theoretical background of the proposed adaptive techniques. This paper, Part II, demonstrates several computational experiments conducted to assess the fidelity and robustness of the proposed techniques. The intentmore » is to check the ability of the adapted core simulator model to predict future core observables that are not included in the adaption or core observables that are recorded at core conditions that differ from those at which adaption is completed. Also, this paper demonstrates successful utilization of an efficient sensitivity analysis approach to calculate the sensitivity information required to perform the adaption for millions of input core parameters. Finally, this paper illustrates a useful application for adaptive simulation - reducing the inconsistencies between two different core simulator code systems, where the multitudes of input data to one code are adjusted to enhance the agreement between both codes for important core attributes, i.e., core reactivity and power distribution. Also demonstrated is the robustness of such an application.« less

Real-Time Smart Grids Control for Preventing Cascading Failures and Blackout using Neural Networks: Experimental Approach for N-1-1 Contingency

NASA Astrophysics Data System (ADS)

Zarrabian, Sina; Belkacemi, Rabie; Babalola, Adeniyi A.

2016-12-01

In this paper, a novel intelligent control is proposed based on Artificial Neural Networks (ANN) to mitigate cascading failure (CF) and prevent blackout in smart grid systems after N-1-1 contingency condition in real-time. The fundamental contribution of this research is to deploy the machine learning concept for preventing blackout at early stages of its occurrence and to make smart grids more resilient, reliable, and robust. The proposed method provides the best action selection strategy for adaptive adjustment of generators' output power through frequency control. This method is able to relieve congestion of transmission lines and prevent consecutive transmission line outage after N-1-1 contingency condition. The proposed ANN-based control approach is tested on an experimental 100 kW test system developed by the authors to test intelligent systems. Additionally, the proposed approach is validated on the large-scale IEEE 118-bus power system by simulation studies. Experimental results show that the ANN approach is very promising and provides accurate and robust control by preventing blackout. The technique is compared to a heuristic multi-agent system (MAS) approach based on communication interchanges. The ANN approach showed more accurate and robust response than the MAS algorithm.

Towards autonomous fuzzy control

NASA Technical Reports Server (NTRS)

Shenoi, Sujeet; Ramer, Arthur

1993-01-01

The efficient implementation of on-line adaptation in real time is an important research problem in fuzzy control. The goal is to develop autonomous self-organizing controllers employing system-independent control meta-knowledge which enables them to adjust their control policies depending on the systems they control and the environments in which they operate. An autonomous fuzzy controller would continuously observe system behavior while implementing its control actions and would use the outcomes of these actions to refine its control policy. It could be designed to lie dormant when its control actions give rise to adequate performance characteristics but could rapidly and autonomously initiate real-time adaptation whenever its performance degrades. Such an autonomous fuzzy controller would have immense practical value. It could accommodate individual variations in system characteristics and also compensate for degradations in system characteristics caused by wear and tear. It could also potentially deal with black-box systems and control scenarios. On-going research in autonomous fuzzy control is reported. The ultimate research objective is to develop robust and relatively inexpensive autonomous fuzzy control hardware suitable for use in real time environments.

Epigenetic regulation of serotype expression antagonizes transcriptome dynamics in Paramecium tetraurelia.

PubMed

Cheaib, Miriam; Dehghani Amirabad, Azim; Nordström, Karl J V; Schulz, Marcel H; Simon, Martin

2015-08-01

Phenotypic variation of a single genotype is achieved by alterations in gene expression patterns. Regulation of such alterations depends on their time scale, where short-time adaptations differ from permanently established gene expression patterns maintained by epigenetic mechanisms. In the ciliate Paramecium, serotypes were described for an epigenetically controlled gene expression pattern of an individual multigene family. Paradoxically, individual serotypes can be triggered in Paramecium by alternating environments but are then stabilized by epigenetic mechanisms, thus raising the question to which extend their expression follows environmental stimuli. To characterize environmental adaptation in the context of epigenetically controlled serotype expression, we used RNA-seq to characterize transcriptomes of serotype pure cultures. The resulting vegetative transcriptome resource is first analysed for genes involved in the adaptive response to the altered environment. Secondly, we identified groups of genes that do not follow the adaptive response but show co-regulation with the epigenetically controlled serotype system, suggesting that their gene expression pattern becomes manifested by similar mechanisms. In our experimental set-up, serotype expression and the entire group of co-regulated genes were stable among environmental changes and only heat-shock genes altered expression of these gene groups. The data suggest that the maintenance of these gene expression patterns in a lineage represents epigenetically controlled robustness counteracting short-time adaptation processes. © The Author 2015. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

Adaptive Peer Sampling with Newscast

NASA Astrophysics Data System (ADS)

Tölgyesi, Norbert; Jelasity, Márk

The peer sampling service is a middleware service that provides random samples from a large decentralized network to support gossip-based applications such as multicast, data aggregation and overlay topology management. Lightweight gossip-based implementations of the peer sampling service have been shown to provide good quality random sampling while also being extremely robust to many failure scenarios, including node churn and catastrophic failure. We identify two problems with these approaches. The first problem is related to message drop failures: if a node experiences a higher-than-average message drop rate then the probability of sampling this node in the network will decrease. The second problem is that the application layer at different nodes might request random samples at very different rates which can result in very poor random sampling especially at nodes with high request rates. We propose solutions for both problems. We focus on Newscast, a robust implementation of the peer sampling service. Our solution is based on simple extensions of the protocol and an adaptive self-control mechanism for its parameters, namely—without involving failure detectors—nodes passively monitor local protocol events using them as feedback for a local control loop for self-tuning the protocol parameters. The proposed solution is evaluated by simulation experiments.

Spontaneous neuronal activity as a self-organized critical phenomenon

NASA Astrophysics Data System (ADS)

de Arcangelis, L.; Herrmann, H. J.

2013-01-01

Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behaviour. Avalanche activity can be modelled within the self-organized criticality framework, including threshold firing, refractory period and activity-dependent synaptic plasticity. The size and duration distributions confirm that the system acts in a critical state, whose scaling behaviour is very robust. Next, we discuss the temporal organization of neuronal avalanches. This is given by the alternation between states of high and low activity, named up and down states, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homeostatic mechanisms. Finally, we verify if a system with no characteristic response can ever learn in a controlled and reproducible way. Learning in the model occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. Learning is a truly collective process and the learning dynamics exhibits universal features. Even complex rules can be learned provided that the plastic adaptation is sufficiently slow.

Optimal and robust control of a class of nonlinear systems using dynamically re-optimised single network adaptive critic design

NASA Astrophysics Data System (ADS)

Tiwari, Shivendra N.; Padhi, Radhakant

2018-01-01

Following the philosophy of adaptive optimal control, a neural network-based state feedback optimal control synthesis approach is presented in this paper. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesised offline. However, another linear-in-weight neural network (called as NN2) is trained online and augmented to NN1 in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which is done by synthesising yet another linear-in-weight neural network (called as NN3) online. Training of NN3 is done by utilising the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function. This helps in training NN2 successfully to capture the required optimal relationship. The overall architecture is named as 'Dynamically Re-optimised single network adaptive critic (DR-SNAC)'. Numerical results for two motivating illustrative problems are presented, including comparison studies with closed form solution for one problem, which clearly demonstrate the effectiveness and benefit of the proposed approach.

Adaptive Wing Camber Optimization: A Periodic Perturbation Approach

NASA Technical Reports Server (NTRS)

Espana, Martin; Gilyard, Glenn

1994-01-01

Available redundancy among aircraft control surfaces allows for effective wing camber modifications. As shown in the past, this fact can be used to improve aircraft performance. To date, however, algorithm developments for in-flight camber optimization have been limited. This paper presents a perturbational approach for cruise optimization through in-flight camber adaptation. The method uses, as a performance index, an indirect measurement of the instantaneous net thrust. As such, the actual performance improvement comes from the integrated effects of airframe and engine. The algorithm, whose design and robustness properties are discussed, is demonstrated on the NASA Dryden B-720 flight simulator.

How robust is a robust policy? A comparative analysis of alternative robustness metrics for supporting robust decision analysis.

NASA Astrophysics Data System (ADS)

Kwakkel, Jan; Haasnoot, Marjolijn

2015-04-01

In response to climate and socio-economic change, in various policy domains there is increasingly a call for robust plans or policies. That is, plans or policies that performs well in a very large range of plausible futures. In the literature, a wide range of alternative robustness metrics can be found. The relative merit of these alternative conceptualizations of robustness has, however, received less attention. Evidently, different robustness metrics can result in different plans or policies being adopted. This paper investigates the consequences of several robustness metrics on decision making, illustrated here by the design of a flood risk management plan. A fictitious case, inspired by a river reach in the Netherlands is used. The performance of this system in terms of casualties, damages, and costs for flood and damage mitigation actions is explored using a time horizon of 100 years, and accounting for uncertainties pertaining to climate change and land use change. A set of candidate policy options is specified up front. This set of options includes dike raising, dike strengthening, creating more space for the river, and flood proof building and evacuation options. The overarching aim is to design an effective flood risk mitigation strategy that is designed from the outset to be adapted over time in response to how the future actually unfolds. To this end, the plan will be based on the dynamic adaptive policy pathway approach (Haasnoot, Kwakkel et al. 2013) being used in the Dutch Delta Program. The policy problem is formulated as a multi-objective robust optimization problem (Kwakkel, Haasnoot et al. 2014). We solve the multi-objective robust optimization problem using several alternative robustness metrics, including both satisficing robustness metrics and regret based robustness metrics. Satisficing robustness metrics focus on the performance of candidate plans across a large ensemble of plausible futures. Regret based robustness metrics compare the performance of a candidate plan with the performance of other candidate plans across a large ensemble of plausible futures. Initial results suggest that the simplest satisficing metric, inspired by the signal to noise ratio, results in very risk averse solutions. Other satisficing metrics, which handle the average performance and the dispersion around the average separately, provide substantial additional insights into the trade off between the average performance, and the dispersion around this average. In contrast, the regret-based metrics enhance insight into the relative merits of candidate plans, while being less clear on the average performance or the dispersion around this performance. These results suggest that it is beneficial to use multiple robustness metrics when doing a robust decision analysis study. Haasnoot, M., J. H. Kwakkel, W. E. Walker and J. Ter Maat (2013). "Dynamic Adaptive Policy Pathways: A New Method for Crafting Robust Decisions for a Deeply Uncertain World." Global Environmental Change 23(2): 485-498. Kwakkel, J. H., M. Haasnoot and W. E. Walker (2014). "Developing Dynamic Adaptive Policy Pathways: A computer-assisted approach for developing adaptive strategies for a deeply uncertain world." Climatic Change.

Novel Observer Scheme of Fuzzy-MRAS Sensorless Speed Control of Induction Motor Drive

NASA Astrophysics Data System (ADS)

Chekroun, S.; Zerikat, M.; Mechernene, A.; Benharir, N.

2017-01-01

This paper presents a novel approach Fuzzy-MRAS conception for robust accurate tracking of induction motor drive operating in a high-performance drives environment. Of the different methods for sensorless control of induction motor drive the model reference adaptive system (MRAS) finds lot of attention due to its good performance. The analysis of the sensorless vector control system using MRAS is presented and the resistance parameters variations and speed observer using new Fuzzy Self-Tuning adaptive IP Controller is proposed. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The present approach helps to achieve a good dynamic response, disturbance rejection and low to plant parameter variations of the induction motor. In order to verify the performances of the proposed observer and control algorithms and to test behaviour of the controlled system, numerical simulation is achieved. Simulation results are presented and discussed to shown the validity and the performance of the proposed observer.

Adaptive Critic-based Neurofuzzy Controller for the Steam Generator Water Level

NASA Astrophysics Data System (ADS)

Fakhrazari, Amin; Boroushaki, Mehrdad

2008-06-01

In this paper, an adaptive critic-based neurofuzzy controller is presented for water level regulation of nuclear steam generators. The problem has been of great concern for many years as the steam generator is a highly nonlinear system showing inverse response dynamics especially at low operating power levels. Fuzzy critic-based learning is a reinforcement learning method based on dynamic programming. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the backpropagation of error algorithm to tune online conclusion parts of the fuzzy inference rules. The critic agent here has a proportional-derivative structure and the fuzzy rule base has nine rules. The proposed controller shows satisfactory transient responses, disturbance rejection and robustness to model uncertainty. Its simple design procedure and structure, nominates it as one of the suitable controller designs for the steam generator water level control in nuclear power plant industry.

Anatomical robust optimization to account for nasal cavity filling variation during intensity-modulated proton therapy: a comparison with conventional and adaptive planning strategies

NASA Astrophysics Data System (ADS)

van de Water, Steven; Albertini, Francesca; Weber, Damien C.; Heijmen, Ben J. M.; Hoogeman, Mischa S.; Lomax, Antony J.

2018-01-01

The aim of this study is to develop an anatomical robust optimization method for intensity-modulated proton therapy (IMPT) that accounts for interfraction variations in nasal cavity filling, and to compare it with conventional single-field uniform dose (SFUD) optimization and online plan adaptation. We included CT data of five patients with tumors in the sinonasal region. Using the planning CT, we generated for each patient 25 ‘synthetic’ CTs with varying nasal cavity filling. The robust optimization method available in our treatment planning system ‘Erasmus-iCycle’ was extended to also account for anatomical uncertainties by including (synthetic) CTs with varying patient anatomy as error scenarios in the inverse optimization. For each patient, we generated treatment plans using anatomical robust optimization and, for benchmarking, using SFUD optimization and online plan adaptation. Clinical target volume (CTV) and organ-at-risk (OAR) doses were assessed by recalculating the treatment plans on the synthetic CTs, evaluating dose distributions individually and accumulated over an entire fractionated 50 GyRBE treatment, assuming each synthetic CT to correspond to a 2 GyRBE fraction. Treatment plans were also evaluated using actual repeat CTs. Anatomical robust optimization resulted in adequate CTV doses (V95%  ⩾  98% and V107%  ⩽  2%) if at least three synthetic CTs were included in addition to the planning CT. These CTV requirements were also fulfilled for online plan adaptation, but not for the SFUD approach, even when applying a margin of 5 mm. Compared with anatomical robust optimization, OAR dose parameters for the accumulated dose distributions were on average 5.9 GyRBE (20%) higher when using SFUD optimization and on average 3.6 GyRBE (18%) lower for online plan adaptation. In conclusion, anatomical robust optimization effectively accounted for changes in nasal cavity filling during IMPT, providing substantially improved CTV and OAR doses compared with conventional SFUD optimization. OAR doses can be further reduced by using online plan adaptation.

A fuzzy call admission control scheme in wireless networks

NASA Astrophysics Data System (ADS)

Ma, Yufeng; Gong, Shenguang; Hu, Xiulin; Zhang, Yunyu

2007-11-01

Scarcity of the spectrum resource and mobility of users make quality of service (QoS) provision a critical issue in wireless networks. This paper presents a fuzzy call admission control scheme to meet the requirement of the QoS. A performance measure is formed as a weighted linear function of new call and handoff call blocking probabilities. Simulation compares the proposed fuzzy scheme with an adaptive channel reservation scheme. Simulation results show that fuzzy scheme has a better robust performance in terms of average blocking criterion.

A Lyapunov-based Approach for Time-Coordinated 3D Path-Following of Multiple Quadrotors in SO(3)

DTIC Science & Technology

2012-12-10

January 2006. [22] T. Lee, “ Robust adaptive geometric tracking controls on so(3) with an application to the attitude dynamicsof a quadrotor uav,” 2011...in the presence of time-varying communication networks and spatial and temporal constraints. The objective is to enable n Quadrotors to track prede?ned...developing control laws to solve the Time-Coordinated 3D Path-Following task for multiple Quadrotor UAVs in the presence of time-varying communication

Adaptive sliding control of non-autonomous active suspension systems with time-varying loadings

NASA Astrophysics Data System (ADS)

Chen, Po-Chang; Huang, An-Chyau

2005-04-01

An adaptive sliding controller is proposed in this paper for controlling a non-autonomous quarter-car suspension system with time-varying loadings. The bound of the car-body loading is assumed to be available. Then, the reference coordinate is placed at the static position under the nominal loading so that the system dynamic equation is derived. Due to spring nonlinearities, the system property becomes asymmetric after coordinate transformation. Besides, in practical cases, system parameters are not easy to be obtained precisely for controller design. Therefore, in this paper, system uncertainties are lumped into two unknown time-varying functions. Since the variation bound of one of the unknown functions is not available, conventional adaptive schemes and robust designs are not applicable. To deal with this problem, the function approximation technique is employed to represent the unknown function as a finite combination of basis functions. The Lyapunov direct method can thus be used to find adaptive laws for updating coefficients in the approximating series and to prove stability of the closed-loop system. Since the position and velocity measurements of the unsprung mass are lumped into the unknown function, there is no need to install sensors on the axle and wheel assembly in the actual implementation. Simulation results are presented to show the performance of the proposed strategy.

Performance recovery of a class of uncertain non-affine systems with unmodelled dynamics: an indirect dynamic inversion method

NASA Astrophysics Data System (ADS)

Yi, Bowen; Lin, Shuyi; Yang, Bo; Zhang, Weidong

2018-02-01

This paper presents an output feedback indirect dynamic inversion (IDI) approach for a class of uncertain nonaffine systems with input unmodelled dynamics. Compared with previous approaches to achieve performance recovery, the proposed method aims at dealing with a broader class of nonaffine-in-control systems with triangular structure. An IDI state feedback law is designed first, in which less knowledge of the model plant is needed compared to earlier approximate dynamic inversion methods, thus yielding more robust performance. After that, an extended high-gain observer is designed to accomplish the task with output feedback. Finally, we prove that the designed IDI controller is equivalent to an adaptive proportional-integral (PI) controller, with respect to both time response equivalence and robustness equivalence. The conclusion implies that for the studied strict-feedback non-affine systems with unmodelled dynamics, there always exits a PI controller to stabilise the systems. The effectiveness and benefits of the designed approach are verified by three examples.

Adaptive Environmental Source Localization and Tracking with Unknown Permittivity and Path Loss Coefficients †

PubMed Central

Fidan, Barış; Umay, Ilknur

2015-01-01

Accurate signal-source and signal-reflector target localization tasks via mobile sensory units and wireless sensor networks (WSNs), including those for environmental monitoring via sensory UAVs, require precise knowledge of specific signal propagation properties of the environment, which are permittivity and path loss coefficients for the electromagnetic signal case. Thus, accurate estimation of these coefficients has significant importance for the accuracy of location estimates. In this paper, we propose a geometric cooperative technique to instantaneously estimate such coefficients, with details provided for received signal strength (RSS) and time-of-flight (TOF)-based range sensors. The proposed technique is integrated to a recursive least squares (RLS)-based adaptive localization scheme and an adaptive motion control law, to construct adaptive target localization and adaptive target tracking algorithms, respectively, that are robust to uncertainties in aforementioned environmental signal propagation coefficients. The efficiency of the proposed adaptive localization and tracking techniques are both mathematically analysed and verified via simulation experiments. PMID:26690441

Emergence of robust growth laws from optimal regulation of ribosome synthesis.

PubMed

Scott, Matthew; Klumpp, Stefan; Mateescu, Eduard M; Hwa, Terence

2014-08-22

Bacteria must constantly adapt their growth to changes in nutrient availability; yet despite large-scale changes in protein expression associated with sensing, adaptation, and processing different environmental nutrients, simple growth laws connect the ribosome abundance and the growth rate. Here, we investigate the origin of these growth laws by analyzing the features of ribosomal regulation that coordinate proteome-wide expression changes with cell growth in a variety of nutrient conditions in the model organism Escherichia coli. We identify supply-driven feedforward activation of ribosomal protein synthesis as the key regulatory motif maximizing amino acid flux, and autonomously guiding a cell to achieve optimal growth in different environments. The growth laws emerge naturally from the robust regulatory strategy underlying growth rate control, irrespective of the details of the molecular implementation. The study highlights the interplay between phenomenological modeling and molecular mechanisms in uncovering fundamental operating constraints, with implications for endogenous and synthetic design of microorganisms. © 2014 The Authors. Published under the terms of the CC BY 4.0 license.

Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis

NASA Astrophysics Data System (ADS)

Wang, Zhuozheng; Deller, J. R.; Fleet, Blair D.

2016-01-01

Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness.

Addressing Climate Change in Long-Term Water Planning Using Robust Decisionmaking

NASA Astrophysics Data System (ADS)

Groves, D. G.; Lempert, R.

2008-12-01

Addressing climate change in long-term natural resource planning is difficult because future management conditions are deeply uncertain and the range of possible adaptation options are so extensive. These conditions pose challenges to standard optimization decision-support techniques. This talk will describe a methodology called Robust Decisionmaking (RDM) that can complement more traditional analytic approaches by utilizing screening-level water management models to evaluate large numbers of strategies against a wide range of plausible future scenarios. The presentation will describe a recent application of the methodology to evaluate climate adaptation strategies for the Inland Empire Utilities Agency in Southern California. This project found that RDM can provide a useful way for addressing climate change uncertainty and identify robust adaptation strategies.

Differential flatness properties and multivariable adaptive control of ovarian system dynamics

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos

2016-12-01

The ovarian system exhibits nonlinear dynamics which is modeled by a set of coupled nonlinear differential equations. The paper proposes adaptive fuzzy control based on differential flatness theory for the complex dynamics of the ovarian system. It is proven that the dynamic model of the ovarian system, having as state variables the LH and the FSH hormones and their derivatives, is a differentially flat one. This means that all its state variables and its control inputs can be described as differential functions of the flat output. By exploiting differential flatness properties the system's dynamic model is written in the multivariable linear canonical (Brunovsky) form, for which the design of a state feedback controller becomes possible. After this transformation, the new control inputs of the system contain unknown nonlinear parts, which are identified with the use of neurofuzzy approximators. The learning procedure for these estimators is determined by the requirement the first derivative of the closed-loop's Lyapunov function to be a negative one. Moreover, Lyapunov stability analysis shows that H-infinity tracking performance is succeeded for the feedback control loop and this assures improved robustness to the aforementioned model uncertainty as well as to external perturbations. The efficiency of the proposed adaptive fuzzy control scheme is confirmed through simulation experiments.

Controlling Tensegrity Robots Through Evolution

NASA Technical Reports Server (NTRS)

Iscen, Atil; Agogino, Adrian; SunSpiral, Vytas; Tumer, Kagan

2013-01-01

Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling between components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball-shaped tensegrity robot. Experimental results performed with a variety of evolutionary algorithms in a detailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400 percent better than a hand-coded solution, while the multi-agent evolution performs 800 percent better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future.

Systems biology of the modified branched Entner-Doudoroff pathway in Sulfolobus solfataricus

PubMed Central

Figueiredo, Ana Sofia; Esser, Dominik; Haferkamp, Patrick; Wieloch, Patricia; Schomburg, Dietmar; Siebers, Bettina; Schaber, Jörg

2017-01-01

Sulfolobus solfataricus is a thermoacidophilic Archaeon that thrives in terrestrial hot springs (solfatares) with optimal growth at 80°C and pH 2–4. It catabolizes specific carbon sources, such as D-glucose, to pyruvate via the modified Entner-Doudoroff (ED) pathway. This pathway has two parallel branches, the semi-phosphorylative and the non-phosphorylative. However, the strategy of S.solfataricus to endure in such an extreme environment in terms of robustness and adaptation is not yet completely understood. Here, we present the first dynamic mathematical model of the ED pathway parameterized with quantitative experimental data. These data consist of enzyme activities of the branched pathway at 70°C and 80°C and of metabolomics data at the same temperatures for the wild type and for a metabolic engineered knockout of the semi-phosphorylative branch. We use the validated model to address two questions: 1. Is this system more robust to perturbations at its optimal growth temperature? 2. Is the ED robust to deletion and perturbations? We employed a systems biology approach to answer these questions and to gain further knowledge on the emergent properties of this biological system. Specifically, we applied deterministic and stochastic approaches to study the sensitivity and robustness of the system, respectively. The mathematical model we present here, shows that: 1. Steady state metabolite concentrations of the ED pathway are consistently more robust to stochastic internal perturbations at 80°C than at 70°C; 2. These metabolite concentrations are highly robust when faced with the knockout of either branch. Connected with this observation, these two branches show different properties at the level of metabolite production and flux control. These new results reveal how enzyme kinetics and metabolomics synergizes with mathematical modelling to unveil new systemic properties of the ED pathway in S.solfataricus in terms of its adaptation and robustness. PMID:28692669

Analysis and improvements of Adaptive Particle Refinement (APR) through CPU time, accuracy and robustness considerations

NASA Astrophysics Data System (ADS)

Chiron, L.; Oger, G.; de Leffe, M.; Le Touzé, D.

2018-02-01

While smoothed-particle hydrodynamics (SPH) simulations are usually performed using uniform particle distributions, local particle refinement techniques have been developed to concentrate fine spatial resolutions in identified areas of interest. Although the formalism of this method is relatively easy to implement, its robustness at coarse/fine interfaces can be problematic. Analysis performed in [16] shows that the radius of refined particles should be greater than half the radius of unrefined particles to ensure robustness. In this article, the basics of an Adaptive Particle Refinement (APR) technique, inspired by AMR in mesh-based methods, are presented. This approach ensures robustness with alleviated constraints. Simulations applying the new formalism proposed achieve accuracy comparable to fully refined spatial resolutions, together with robustness, low CPU times and maintained parallel efficiency.

Sliding-mode control combined with improved adaptive feedforward for wafer scanner

NASA Astrophysics Data System (ADS)

Li, Xiaojie; Wang, Yiguang

2018-03-01

In this paper, a sliding-mode control method combined with improved adaptive feedforward is proposed for wafer scanner to improve the tracking performance of the closed-loop system. Particularly, In addition to the inverse model, the nonlinear force ripple effect which may degrade the tracking accuracy of permanent magnet linear motor (PMLM) is considered in the proposed method. The dominant position periodicity of force ripple is determined by using the Fast Fourier Transform (FFT) analysis for experimental data and the improved feedforward control is achieved by the online recursive least-squares (RLS) estimation of the inverse model and the force ripple. The improved adaptive feedforward is given in a general form of nth-order model with force ripple effect. This proposed method is motivated by the motion controller design of the long-stroke PMLM and short-stroke voice coil motor for wafer scanner. The stability of the closed-loop control system and the convergence of the motion tracking are guaranteed by the proposed sliding-mode feedback and adaptive feedforward methods theoretically. Comparative experiments on a precision linear motion platform can verify the correctness and effectiveness of the proposed method. The experimental results show that comparing to traditional method the proposed one has better performance of rapidity and robustness, especially for high speed motion trajectory. And, the improvements on both tracking accuracy and settling time can be achieved.

Certification Considerations for Adaptive Systems

NASA Technical Reports Server (NTRS)

Bhattacharyya, Siddhartha; Cofer, Darren; Musliner, David J.; Mueller, Joseph; Engstrom, Eric

2015-01-01

Advanced capabilities planned for the next generation of aircraft, including those that will operate within the Next Generation Air Transportation System (NextGen), will necessarily include complex new algorithms and non-traditional software elements. These aircraft will likely incorporate adaptive control algorithms that will provide enhanced safety, autonomy, and robustness during adverse conditions. Unmanned aircraft will operate alongside manned aircraft in the National Airspace (NAS), with intelligent software performing the high-level decision-making functions normally performed by human pilots. Even human-piloted aircraft will necessarily include more autonomy. However, there are serious barriers to the deployment of new capabilities, especially for those based upon software including adaptive control (AC) and artificial intelligence (AI) algorithms. Current civil aviation certification processes are based on the idea that the correct behavior of a system must be completely specified and verified prior to operation. This report by Rockwell Collins and SIFT documents our comprehensive study of the state of the art in intelligent and adaptive algorithms for the civil aviation domain, categorizing the approaches used and identifying gaps and challenges associated with certification of each approach.

Adaptation to low pH and lignocellulosic inhibitors resulting in ethanolic fermentation and growth of Saccharomyces cerevisiae

DOE Office of Scientific and Technical Information (OSTI.GOV)

Narayanan, Venkatachalam; Sànchez i Nogué, Violeta; van Niel, Ed W. J.

Here, lignocellulosic bioethanol from renewable feedstocks using Saccharomyces cerevisiae is a promising alternative to fossil fuels owing to environmental challenges. S. cerevisiae is frequently challenged by bacterial contamination and a combination of lignocellulosic inhibitors formed during the pre-treatment, in terms of growth, ethanol yield and productivity. We investigated the phenotypic robustness of a brewing yeast strain TMB3500 and its ability to adapt to low pH thereby preventing bacterial contamination along with lignocellulosic inhibitors by short-term adaptation and adaptive lab evolution (ALE). The short-term adaptation strategy was used to investigate the inherent ability of strain TMB3500 to activate a robust phenotypemore » involving pre-culturing yeast cells in defined medium with lignocellulosic inhibitors at pH 5.0 until late exponential phase prior to inoculating them in defined media with the same inhibitor cocktail at pH 3.7. Adapted cells were able to grow aerobically, ferment anaerobically (glucose exhaustion by 19 +/- 5 h to yield 0.45 +/- 0.01 g ethanol g glucose -1) and portray significant detoxification of inhibitors at pH 3.7, when compared to non-adapted cells. ALE was performed to investigate whether a stable strain could be developed to grow and ferment at low pH with lignocellulosic inhibitors in a continuous suspension culture. Though a robust population was obtained after 3600 h with an ability to grow and ferment at pH 3.7 with inhibitors, inhibitor robustness was not stable as indicated by the characterisation of the evolved culture possibly due to phenotypic plasticity. With further research, this short-term adaptation and low pH strategy could be successfully applied in lignocellulosic ethanol plants to prevent bacterial contamination.« less

Adaptation to low pH and lignocellulosic inhibitors resulting in ethanolic fermentation and growth of Saccharomyces cerevisiae

DOE PAGES

Narayanan, Venkatachalam; Sànchez i Nogué, Violeta; van Niel, Ed W. J.; ...

2016-08-26

Here, lignocellulosic bioethanol from renewable feedstocks using Saccharomyces cerevisiae is a promising alternative to fossil fuels owing to environmental challenges. S. cerevisiae is frequently challenged by bacterial contamination and a combination of lignocellulosic inhibitors formed during the pre-treatment, in terms of growth, ethanol yield and productivity. We investigated the phenotypic robustness of a brewing yeast strain TMB3500 and its ability to adapt to low pH thereby preventing bacterial contamination along with lignocellulosic inhibitors by short-term adaptation and adaptive lab evolution (ALE). The short-term adaptation strategy was used to investigate the inherent ability of strain TMB3500 to activate a robust phenotypemore » involving pre-culturing yeast cells in defined medium with lignocellulosic inhibitors at pH 5.0 until late exponential phase prior to inoculating them in defined media with the same inhibitor cocktail at pH 3.7. Adapted cells were able to grow aerobically, ferment anaerobically (glucose exhaustion by 19 +/- 5 h to yield 0.45 +/- 0.01 g ethanol g glucose -1) and portray significant detoxification of inhibitors at pH 3.7, when compared to non-adapted cells. ALE was performed to investigate whether a stable strain could be developed to grow and ferment at low pH with lignocellulosic inhibitors in a continuous suspension culture. Though a robust population was obtained after 3600 h with an ability to grow and ferment at pH 3.7 with inhibitors, inhibitor robustness was not stable as indicated by the characterisation of the evolved culture possibly due to phenotypic plasticity. With further research, this short-term adaptation and low pH strategy could be successfully applied in lignocellulosic ethanol plants to prevent bacterial contamination.« less

Robust algebraic image enhancement for intelligent control systems

NASA Technical Reports Server (NTRS)

Lerner, Bao-Ting; Morrelli, Michael

1993-01-01

Robust vision capability for intelligent control systems has been an elusive goal in image processing. The computationally intensive techniques a necessary for conventional image processing make real-time applications, such as object tracking and collision avoidance difficult. In order to endow an intelligent control system with the needed vision robustness, an adequate image enhancement subsystem capable of compensating for the wide variety of real-world degradations, must exist between the image capturing and the object recognition subsystems. This enhancement stage must be adaptive and must operate with consistency in the presence of both statistical and shape-based noise. To deal with this problem, we have developed an innovative algebraic approach which provides a sound mathematical framework for image representation and manipulation. Our image model provides a natural platform from which to pursue dynamic scene analysis, and its incorporation into a vision system would serve as the front-end to an intelligent control system. We have developed a unique polynomial representation of gray level imagery and applied this representation to develop polynomial operators on complex gray level scenes. This approach is highly advantageous since polynomials can be manipulated very easily, and are readily understood, thus providing a very convenient environment for image processing. Our model presents a highly structured and compact algebraic representation of grey-level images which can be viewed as fuzzy sets.

A dynamic feedforward neural network based on gaussian particle swarm optimization and its application for predictive control.

PubMed

Han, Min; Fan, Jianchao; Wang, Jun

2011-09-01

A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays.

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

NASA Astrophysics Data System (ADS)

Ji, Xuewu; He, Xiangkun; Lv, Chen; Liu, Yahui; Wu, Jian

2018-06-01

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.

Emergent Adaptive Noise Reduction from Communal Cooperation of Sensor Grid

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Jones, Michael G.; Nark, Douglas M.; Lodding, Kenneth N.

2010-01-01

In the last decade, the realization of small, inexpensive, and powerful devices with sensors, computers, and wireless communication has promised the development of massive sized sensor networks with dense deployments over large areas capable of high fidelity situational assessments. However, most management models have been based on centralized control and research has concentrated on methods for passing data from sensor devices to the central controller. Most implementations have been small but, as it is not scalable, this methodology is insufficient for massive deployments. Here, a specific application of a large sensor network for adaptive noise reduction demonstrates a new paradigm where communities of sensor/computer devices assess local conditions and make local decisions from which emerges a global behaviour. This approach obviates many of the problems of centralized control as it is not prone to single point of failure and is more scalable, efficient, robust, and fault tolerant

Control of finite critical behaviour in a small-scale social system

NASA Astrophysics Data System (ADS)

Daniels, Bryan C.; Krakauer, David C.; Flack, Jessica C.

2017-02-01

Many adaptive systems sit near a tipping or critical point. For systems near a critical point small changes to component behaviour can induce large-scale changes in aggregate structure and function. Criticality can be adaptive when the environment is changing, but entails reduced robustness through sensitivity. This tradeoff can be resolved when criticality can be tuned. We address the control of finite measures of criticality using data on fight sizes from an animal society model system (Macaca nemestrina, n=48). We find that a heterogeneous, socially organized system, like homogeneous, spatial systems (flocks and schools), sits near a critical point; the contributions individuals make to collective phenomena can be quantified; there is heterogeneity in these contributions; and distance from the critical point (DFC) can be controlled through biologically plausible mechanisms exploiting heterogeneity. We propose two alternative hypotheses for why a system decreases the distance from the critical point.

Coordinated Control of Slip Ratio for Wheeled Mobile Robots Climbing Loose Sloped Terrain

PubMed Central

Li, Zhengcai; Wang, Yang

2014-01-01

A challenging problem faced by wheeled mobile robots (WMRs) such as planetary rovers traversing loose sloped terrain is the inevitable longitudinal slip suffered by the wheels, which often leads to their deviation from the predetermined trajectory, reduced drive efficiency, and possible failures. This study investigates this problem using terramechanics analysis of the wheel-soil interaction. First, a slope-based wheel-soil interaction terramechanics model is built, and an online slip coordinated algorithm is designed based on the goal of optimal drive efficiency. An equation of state is established using the coordinated slip as the desired input and the actual slip as a state variable. To improve the robustness and adaptability of the control system, an adaptive neural network is designed. Analytical results and those of a simulation using Vortex demonstrate the significantly improved mobile performance of the WMR using the proposed control system. PMID:25276849

Coordinated control of slip ratio for wheeled mobile robots climbing loose sloped terrain.

PubMed

Li, Zhengcai; Wang, Yang

2014-01-01

A challenging problem faced by wheeled mobile robots (WMRs) such as planetary rovers traversing loose sloped terrain is the inevitable longitudinal slip suffered by the wheels, which often leads to their deviation from the predetermined trajectory, reduced drive efficiency, and possible failures. This study investigates this problem using terramechanics analysis of the wheel-soil interaction. First, a slope-based wheel-soil interaction terramechanics model is built, and an online slip coordinated algorithm is designed based on the goal of optimal drive efficiency. An equation of state is established using the coordinated slip as the desired input and the actual slip as a state variable. To improve the robustness and adaptability of the control system, an adaptive neural network is designed. Analytical results and those of a simulation using Vortex demonstrate the significantly improved mobile performance of the WMR using the proposed control system.

Vital Role for CD8+ Cells in Controlling Retroviral Infections ▿

PubMed Central

Kane, Melissa; Case, Laure K.; Golovkina, Tatyana V.

2011-01-01

Antiviral adaptive immune defenses consist of humoral and cell-mediated responses, which together eliminate extracellular and intracellular virus. As most retrovirus-infected individuals do not raise efficient protective antivirus immune responses, the relative importance of humoral and cell-mediated responses in restraining retroviral infection is not well understood. We utilized retrovirus-resistant I/LnJ mice, which control infection with mouse mammary tumor virus (MMTV) and murine leukemia virus (MuLV) via an adaptive immune mechanism, to assess the contribution of cellular responses and virus-neutralizing antibodies (Abs) to the control of retroviral infection. We found that in retrovirus-infected CD8-deficient I/LnJ mice, viral titers exceed the neutralizing capability of antiviral Abs, resulting in augmented virus spread and disease induction. Thus, even in the presence of robust neutralizing Ab responses, CD8-mediated responses are essential for full protection against retroviral infection. PMID:21248041

Robust speech perception: Recognize the familiar, generalize to the similar, and adapt to the novel

PubMed Central

Kleinschmidt, Dave F.; Jaeger, T. Florian

2016-01-01

Successful speech perception requires that listeners map the acoustic signal to linguistic categories. These mappings are not only probabilistic, but change depending on the situation. For example, one talker’s /p/ might be physically indistinguishable from another talker’s /b/ (cf. lack of invariance). We characterize the computational problem posed by such a subjectively non-stationary world and propose that the speech perception system overcomes this challenge by (1) recognizing previously encountered situations, (2) generalizing to other situations based on previous similar experience, and (3) adapting to novel situations. We formalize this proposal in the ideal adapter framework: (1) to (3) can be understood as inference under uncertainty about the appropriate generative model for the current talker, thereby facilitating robust speech perception despite the lack of invariance. We focus on two critical aspects of the ideal adapter. First, in situations that clearly deviate from previous experience, listeners need to adapt. We develop a distributional (belief-updating) learning model of incremental adaptation. The model provides a good fit against known and novel phonetic adaptation data, including perceptual recalibration and selective adaptation. Second, robust speech recognition requires listeners learn to represent the structured component of cross-situation variability in the speech signal. We discuss how these two aspects of the ideal adapter provide a unifying explanation for adaptation, talker-specificity, and generalization across talkers and groups of talkers (e.g., accents and dialects). The ideal adapter provides a guiding framework for future investigations into speech perception and adaptation, and more broadly language comprehension. PMID:25844873

A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band.

PubMed

Trong Bui, Duong; Nguyen, Nhan Duc; Jeong, Gu-Min

2018-06-25

Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2⁻4.2% depending on the type of wrist activities.

Distributed Time-Varying Formation Robust Tracking for General Linear Multiagent Systems With Parameter Uncertainties and External Disturbances.

PubMed

Hua, Yongzhao; Dong, Xiwang; Li, Qingdong; Ren, Zhang

2017-05-18

This paper investigates the time-varying formation robust tracking problems for high-order linear multiagent systems with a leader of unknown control input in the presence of heterogeneous parameter uncertainties and external disturbances. The followers need to accomplish an expected time-varying formation in the state space and track the state trajectory produced by the leader simultaneously. First, a time-varying formation robust tracking protocol with a totally distributed form is proposed utilizing the neighborhood state information. With the adaptive updating mechanism, neither any global knowledge about the communication topology nor the upper bounds of the parameter uncertainties, external disturbances and leader's unknown input are required in the proposed protocol. Then, in order to determine the control parameters, an algorithm with four steps is presented, where feasible conditions for the followers to accomplish the expected time-varying formation tracking are provided. Furthermore, based on the Lyapunov-like analysis theory, it is proved that the formation tracking error can converge to zero asymptotically. Finally, the effectiveness of the theoretical results is verified by simulation examples.

Robust local search for spacecraft operations using adaptive noise

NASA Technical Reports Server (NTRS)

Fukunaga, Alex S.; Rabideau, Gregg; Chien, Steve

2004-01-01

Randomization is a standard technique for improving the performance of local search algorithms for constraint satisfaction. However, it is well-known that local search algorithms are constraints satisfaction. However, it is well-known that local search algorithms are to the noise values selected. We investigate the use of an adaptive noise mechanism in an iterative repair-based planner/scheduler for spacecraft operations. Preliminary results indicate that adaptive noise makes the use of randomized repair moves safe and robust; that is, using adaptive noise makes it possible to consistently achieve, performance comparable with the best tuned noise setting without the need for manually tuning the noise parameter.

Addressing uncertainty in adaptation planning for agriculture.

PubMed

Vermeulen, Sonja J; Challinor, Andrew J; Thornton, Philip K; Campbell, Bruce M; Eriyagama, Nishadi; Vervoort, Joost M; Kinyangi, James; Jarvis, Andy; Läderach, Peter; Ramirez-Villegas, Julian; Nicklin, Kathryn J; Hawkins, Ed; Smith, Daniel R

2013-05-21

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop-climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty.

Addressing uncertainty in adaptation planning for agriculture

PubMed Central

Vermeulen, Sonja J.; Challinor, Andrew J.; Thornton, Philip K.; Campbell, Bruce M.; Eriyagama, Nishadi; Vervoort, Joost M.; Kinyangi, James; Jarvis, Andy; Läderach, Peter; Ramirez-Villegas, Julian; Nicklin, Kathryn J.; Hawkins, Ed; Smith, Daniel R.

2013-01-01

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop–climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty. PMID:23674681

Development of robust building energy demand-side control strategy under uncertainty

NASA Astrophysics Data System (ADS)

Kim, Sean Hay

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.

Space Launch System Ascent Flight Control Design

NASA Technical Reports Server (NTRS)

VanZwieten, Tannen S.; Orr, Jeb S.; Wall, John H.; Hall, Charles E.

2014-01-01

A robust and flexible autopilot architecture for NASA's Space Launch System (SLS) family of launch vehicles is presented. As the SLS configurations represent a potentially significant increase in complexity and performance capability of the integrated flight vehicle, it was recognized early in the program that a new, generalized autopilot design should be formulated to fulfill the needs of this new space launch architecture. The present design concept is intended to leverage existing NASA and industry launch vehicle design experience and maintain the extensibility and modularity necessary to accommodate multiple vehicle configurations while relying on proven and flight-tested control design principles for large boost vehicles. The SLS flight control architecture combines a digital three-axis autopilot with traditional bending filters to support robust active or passive stabilization of the vehicle's bending and sloshing dynamics using optimally blended measurements from multiple rate gyros on the vehicle structure. The algorithm also relies on a pseudo-optimal control allocation scheme to maximize the performance capability of multiple vectored engines while accommodating throttling and engine failure contingencies in real time with negligible impact to stability characteristics. The architecture supports active in-flight load relief through the use of a nonlinear observer driven by acceleration measurements, and envelope expansion and robustness enhancement is obtained through the use of a multiplicative forward gain modulation law based upon a simple model reference adaptive control scheme.

Space Launch System Ascent Flight Control Design

NASA Technical Reports Server (NTRS)

Orr, Jeb S.; Wall, John H.; VanZwieten, Tannen S.; Hall, Charles E.

2014-01-01

A robust and flexible autopilot architecture for NASA's Space Launch System (SLS) family of launch vehicles is presented. The SLS configurations represent a potentially significant increase in complexity and performance capability when compared with other manned launch vehicles. It was recognized early in the program that a new, generalized autopilot design should be formulated to fulfill the needs of this new space launch architecture. The present design concept is intended to leverage existing NASA and industry launch vehicle design experience and maintain the extensibility and modularity necessary to accommodate multiple vehicle configurations while relying on proven and flight-tested control design principles for large boost vehicles. The SLS flight control architecture combines a digital three-axis autopilot with traditional bending filters to support robust active or passive stabilization of the vehicle's bending and sloshing dynamics using optimally blended measurements from multiple rate gyros on the vehicle structure. The algorithm also relies on a pseudo-optimal control allocation scheme to maximize the performance capability of multiple vectored engines while accommodating throttling and engine failure contingencies in real time with negligible impact to stability characteristics. The architecture supports active in-flight disturbance compensation through the use of nonlinear observers driven by acceleration measurements. Envelope expansion and robustness enhancement is obtained through the use of a multiplicative forward gain modulation law based upon a simple model reference adaptive control scheme.

A Robust and Scalable Software Library for Parallel Adaptive Refinement on Unstructured Meshes

NASA Technical Reports Server (NTRS)

Lou, John Z.; Norton, Charles D.; Cwik, Thomas A.

1999-01-01

The design and implementation of Pyramid, a software library for performing parallel adaptive mesh refinement (PAMR) on unstructured meshes, is described. This software library can be easily used in a variety of unstructured parallel computational applications, including parallel finite element, parallel finite volume, and parallel visualization applications using triangular or tetrahedral meshes. The library contains a suite of well-designed and efficiently implemented modules that perform operations in a typical PAMR process. Among these are mesh quality control during successive parallel adaptive refinement (typically guided by a local-error estimator), parallel load-balancing, and parallel mesh partitioning using the ParMeTiS partitioner. The Pyramid library is implemented in Fortran 90 with an interface to the Message-Passing Interface (MPI) library, supporting code efficiency, modularity, and portability. An EM waveguide filter application, adaptively refined using the Pyramid library, is illustrated.

A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems.

PubMed

Lyu, Weiwei; Cheng, Xianghong

2017-11-28

Transfer alignment is always a key technology in a strapdown inertial navigation system (SINS) because of its rapidity and accuracy. In this paper a transfer alignment model is established, which contains the SINS error model and the measurement model. The time delay in the process of transfer alignment is analyzed, and an H∞ filtering method with delay compensation is presented. Then the H∞ filtering theory and the robust mechanism of H∞ filter are deduced and analyzed in detail. In order to improve the transfer alignment accuracy in SINS with time delay, an adaptive H∞ filtering method with delay compensation is proposed. Since the robustness factor plays an important role in the filtering process and has effect on the filtering accuracy, the adaptive H∞ filter with delay compensation can adjust the value of robustness factor adaptively according to the dynamic external environment. The vehicle transfer alignment experiment indicates that by using the adaptive H∞ filtering method with delay compensation, the transfer alignment accuracy and the pure inertial navigation accuracy can be dramatically improved, which demonstrates the superiority of the proposed filtering method.

A Methodology for Investigating Adaptive Postural Control

NASA Technical Reports Server (NTRS)

McDonald, P. V.; Riccio, G. E.

1999-01-01

Our research on postural control and human-environment interactions provides an appropriate scientific foundation for understanding the skill of mass handling by astronauts in weightless conditions (e.g., extravehicular activity or EVA). We conducted an investigation of such skills in NASA's principal mass-handling simulator, the Precision Air-Bearing Floor, at the Johnson Space Center. We have studied skilled movement-body within a multidisciplinary context that draws on concepts and methods from biological and behavioral sciences (e.g., psychology, kinesiology and neurophysiology) as well as bioengineering. Our multidisciplinary research has led to the development of measures, for manual interactions between individuals and the substantial environment, that plausibly are observable by human sensory systems. We consider these methods to be the most important general contribution of our EVA investigation. We describe our perspective as control theoretic because it draws more on fundamental concepts about control systems in engineering than it does on working constructs from the subdisciplines of biomechanics and motor control in the bio-behavioral sciences. At the same time, we have attempted to identify the theoretical underpinnings of control-systems engineering that are most relevant to control by human beings. We believe that these underpinnings are implicit in the assumptions that cut across diverse methods in control-systems engineering, especially the various methods associated with "nonlinear control", "fuzzy control," and "adaptive control" in engineering. Our methods are based on these theoretical foundations rather than on the mathematical formalisms that are associated with particular methods in control-systems engineering. The most important aspects of the human-environment interaction in our investigation of mass handling are the functional consequences that body configuration and stability have for the pick up of information or the achievement of overt goals. It follows that an essential characteristic of postural behavior is the effective maintenance of the orientation and stability of the sensory and motor "platforms" (e.g., head or shoulders) over variations in the human, the environment and the task. This general skill suggests that individuals should be sensitive to the functional consequences of body configuration and stability. In other words, individuals should perceive the relation between configuration, stability, and performance so that they can adaptively control their interaction with the surroundings. Human-environment interactions constitute robust systems in that individuals can maintain the stability of such interactions over uncertainty about and variations in the dynamics of the interaction. Robust interactions allow individuals to adopt orientations and configurations that are not optimal with respect to purely energetic criteria. Individuals can tolerate variation in postural states, and such variation can serve an important function in adaptive systems. Postural variability generates stimulation which is "textured" by the dynamics of the human-environment system. The texture or structure in stimulation provides information about variation in dynamics, and such information can be sufficient to guide adaption in control strategies. Our method were designed to measure informative patterns of movement variability.

Adapting to Adaptations: Behavioural Strategies that are Robust to Mutations and Other Organisational-Transformations

PubMed Central

Egbert, Matthew D.; Pérez-Mercader, Juan

2016-01-01

Genetic mutations, infection by parasites or symbionts, and other events can transform the way that an organism’s internal state changes in response to a given environment. We use a minimalistic computational model to support an argument that by behaving “interoceptively,” i.e. responding to internal state rather than to the environment, organisms can be robust to these organisational-transformations. We suggest that the robustness of interoceptive behaviour is due, in part, to the asymmetrical relationship between an organism and its environment, where the latter more substantially influences the former than vice versa. This relationship means that interoceptive behaviour can respond to the environment, the internal state and the interaction between the two, while exteroceptive behaviour can only respond to the environment. We discuss the possibilities that (i) interoceptive behaviour may play an important role of facilitating adaptive evolution (especially in the early evolution of primitive life) and (ii) interoceptive mechanisms could prove useful in efforts to create more robust synthetic life-forms. PMID:26743579

Dynamic neural networks based on-line identification and control of high performance motor drives

NASA Technical Reports Server (NTRS)

Rubaai, Ahmed; Kotaru, Raj

1995-01-01

In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

Holonic Rationale and Bio-inspiration on Design of Complex Emergent and Evolvable Systems

NASA Astrophysics Data System (ADS)

Leitao, Paulo

Traditional centralized and rigid control structures are becoming inflexible to face the requirements of reconfigurability, responsiveness and robustness, imposed by customer demands in the current global economy. The Holonic Manufacturing Systems (HMS) paradigm, which was pointed out as a suitable solution to face these requirements, translates the concepts inherited from social organizations and biology to the manufacturing world. It offers an alternative way of designing adaptive systems where the traditional centralized control is replaced by decentralization over distributed and autonomous entities organized in hierarchical structures formed by intermediate stable forms. In spite of its enormous potential, methods regarding the self-adaptation and self-organization of complex systems are still missing. This paper discusses how the insights from biology in connection with new fields of computer science can be useful to enhance the holonic design aiming to achieve more self-adaptive and evolvable systems. Special attention is devoted to the discussion of emergent behavior and self-organization concepts, and the way they can be combined with the holonic rationale.

Investigation of whether in-room CT-based adaptive intracavitary brachytherapy for uterine cervical cancer is robust against interfractional location variations of organs and/or applicators

PubMed Central

Oku, Yoshifumi; Arimura, Hidetaka; Nguyen, Tran Thi Thao; Hiraki, Yoshiyuki; Toyota, Masahiko; Saigo, Yasumasa; Yoshiura, Takashi; Hirata, Hideki

2016-01-01

This study investigates whether in-room computed tomography (CT)-based adaptive treatment planning (ATP) is robust against interfractional location variations, namely, interfractional organ motions and/or applicator displacements, in 3D intracavitary brachytherapy (ICBT) for uterine cervical cancer. In ATP, the radiation treatment plans, which have been designed based on planning CT images (and/or MR images) acquired just before the treatments, are adaptively applied for each fraction, taking into account the interfractional location variations. 2D and 3D plans with ATP for 14 patients were simulated for 56 fractions at a prescribed dose of 600 cGy per fraction. The standard deviations (SDs) of location displacements (interfractional location variations) of the target and organs at risk (OARs) with 3D ATP were significantly smaller than those with 2D ATP (P < 0.05). The homogeneity index (HI), conformity index (CI) and tumor control probability (TCP) in 3D ATP were significantly higher for high-risk clinical target volumes than those in 2D ATP. The SDs of the HI, CI, TCP, bladder and rectum D2cc, and the bladder and rectum normal tissue complication probability (NTCP) in 3D ATP were significantly smaller than those in 2D ATP. The results of this study suggest that the interfractional location variations give smaller impacts on the planning evaluation indices in 3D ATP than in 2D ATP. Therefore, the 3D plans with ATP are expected to be robust against interfractional location variations in each treatment fraction. PMID:27296250

Reciprocity Between Robustness of Period and Plasticity of Phase in Biological Clocks

NASA Astrophysics Data System (ADS)

Hatakeyama, Tetsuhiro S.; Kaneko, Kunihiko

2015-11-01

Circadian clocks exhibit the robustness of period and plasticity of phase against environmental changes such as temperature and nutrient conditions. Thus far, however, it is unclear how both are simultaneously achieved. By investigating distinct models of circadian clocks, we demonstrate reciprocity between robustness and plasticity: higher robustness in the period implies higher plasticity in the phase, where changes in period and in phase follow a linear relationship with a negative coefficient. The robustness of period is achieved by the adaptation on the limit cycle via a concentration change of a buffer molecule, whose temporal change leads to a phase shift following a shift of the limit-cycle orbit in phase space. Generality of reciprocity in clocks with the adaptation mechanism is confirmed with theoretical analysis of simple models, while biological significance is discussed.

[Dialectical Behavior Therapy (DBT)--developments and empirical evidence].

PubMed

Burmeister, Kerstin; Höschel, Klaus; von Auer, Anne Kristin; Reiske, Sophie; Schweiger, Ulrich; Sipos, Valerija; Philipsen, Alexandra; Priebe, Kathlen; Bohus, Martin

2014-07-01

Dialectical Behavior Therapy has been initially designed and evaluated as an outpatient-treatment program for chronic suicidal female patients. Within the last years, several adaptations of DBT for specific comorbidities, other settings or other disorders related to emotion dysregulation have been developed. This report reviews conceptual aspects and the scientific evidence of initially designed Dialectical Behavior Therapy and the adaptations. Systematic literature search and systematic review. Recently, two meta-analyses which are based on randomized controlled trials conclude robust and stabile effects of DBT Evidence from further RCTs and other studies show promise for the properties of many DBT adaptations. The current review of the literature suggests a good effectiveness of DBT, especially on complex disorders with deficits in the field of emotion regulation. © Georg Thieme Verlag KG Stuttgart · New York.

Adaptation to a cortex controlled robot attached at the pelvis and engaged during locomotion in rats

PubMed Central

Song, Weiguo; Giszter, Simon F.

2011-01-01

Brain Machine Interfaces (BMIs) should ideally show robust adaptation of the BMI across different tasks and daily activities. Most BMIs have used over-practiced tasks. Little is known about BMIs in dynamic environments. How are mechanically body-coupled BMIs integrated into ongoing rhythmic dynamics, e.g., in locomotion? To examine this we designed a novel BMI using neural discharge in the hindlimb/trunk motor cortex in rats during locomotion to control a robot attached at the pelvis. We tested neural adaptation when rats experienced (a) control locomotion, (b) ‘simple elastic load’ (a robot load on locomotion without any BMI neural control) and (c) ‘BMI with elastic load’ (in which the robot loaded locomotion and a BMI neural control could counter this load). Rats significantly offset applied loads with the BMI while preserving more normal pelvic height compared to load alone. Adaptation occurred over about 100–200 step cycles in a trial. Firing rates increased in both the loaded conditions compared to baseline. Mean phases of cells’ discharge in the step cycle shifted significantly between BMI and the simple load condition. Over time more BMI cells became positively correlated with the external force and modulated more deeply, and neurons’ network correlations on a 100ms timescale increased. Loading alone showed none of these effects. The BMI neural changes of rate and force correlations persisted or increased over repeated trials. Our results show that rats have the capacity to use motor adaptation and motor learning to fairly rapidly engage hindlimb/trunk coupled BMIs in their locomotion. PMID:21414932

Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion.

PubMed

Kumar, Rajesh; Srivastava, Smriti; Gupta, J R P

2017-03-01

In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive Inverse Control for Rotorcraft Vibration Reduction

NASA Technical Reports Server (NTRS)

Jacklin, Stephen A.

1985-01-01

This thesis extends the Least Mean Square (LMS) algorithm to solve the mult!ple-input, multiple-output problem of alleviating N/Rev (revolutions per minute by number of blades) helicopter fuselage vibration by means of adaptive inverse control. A frequency domain locally linear model is used to represent the transfer matrix relating the higher harmonic pitch control inputs to the harmonic vibration outputs to be controlled. By using the inverse matrix as the controller gain matrix, an adaptive inverse regulator is formed to alleviate the N/Rev vibration. The stability and rate of convergence properties of the extended LMS algorithm are discussed. It is shown that the stability ranges for the elements of the stability gain matrix are directly related to the eigenvalues of the vibration signal information matrix for the learning phase, but not for the control phase. The overall conclusion is that the LMS adaptive inverse control method can form a robust vibration control system, but will require some tuning of the input sensor gains, the stability gain matrix, and the amount of control relaxation to be used. The learning curve of the controller during the learning phase is shown to be quantitatively close to that predicted by averaging the learning curves of the normal modes. For higher order transfer matrices, a rough estimate of the inverse is needed to start the algorithm efficiently. The simulation results indicate that the factor which most influences LMS adaptive inverse control is the product of the control relaxation and the the stability gain matrix. A small stability gain matrix makes the controller less sensitive to relaxation selection, and permits faster and more stable vibration reduction, than by choosing the stability gain matrix large and the control relaxation term small. It is shown that the best selections of the stability gain matrix elements and the amount of control relaxation is basically a compromise between slow, stable convergence and fast convergence with increased possibility of unstable identification. In the simulation studies, the LMS adaptive inverse control algorithm is shown to be capable of adapting the inverse (controller) matrix to track changes in the flight conditions. The algorithm converges quickly for moderate disturbances, while taking longer for larger disturbances. Perfect knowledge of the inverse matrix is not required for good control of the N/Rev vibration. However it is shown that measurement noise will prevent the LMS adaptive inverse control technique from controlling the vibration, unless the signal averaging method presented is incorporated into the algorithm.

Should We Turn the Robots Loose?

DTIC Science & Technology

2010-05-02

interference. Potential sources of electromagnetic interference include everyday signals such as cell phones and Wifi , intentional friendly jamming of IED...might even attempt to hack or hijack our robotic warriors. Our current enemies have proven to be very adaptable and have developed simple counters to our...demonstrates the ease with which robot command and control might be hacked . It is reasonable to suspect that a future threat with a more robust

Adaptive Grid Generation Using Elliptic Generating Equations with Precise Coordinate Controls

DTIC Science & Technology

1986-07-08

nonhomogeneous terms, which are strong eration that are of critical importance in choosing a and typically greatly slow the iterative convergence grid...computational mechan- calcuiauons. particulary three-dimensionai turbuient studies. ics in October 1989. 1 do not : hink that the overall cost of -te...flow in gas turbine diffusers, and from the National Science Foundation (Mathematics Division) on "Robust and Fast Numerical Grid Generation". The

Robust adaptive multichannel SAR processing based on covariance matrix reconstruction

NASA Astrophysics Data System (ADS)

Tan, Zhen-ya; He, Feng

2018-04-01

With the combination of digital beamforming (DBF) processing, multichannel synthetic aperture radar(SAR) systems in azimuth promise well in high-resolution and wide-swath imaging, whereas conventional processing methods don't take the nonuniformity of scattering coefficient into consideration. This paper brings up a robust adaptive Multichannel SAR processing method which utilizes the Capon spatial spectrum estimator to obtain the spatial spectrum distribution over all ambiguous directions first, and then the interference-plus-noise covariance Matrix is reconstructed based on definition to acquire the Multichannel SAR processing filter. The performance of processing under nonuniform scattering coefficient is promoted by this novel method and it is robust again array errors. The experiments with real measured data demonstrate the effectiveness and robustness of the proposed method.

Modeling Power Systems as Complex Adaptive Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chassin, David P.; Malard, Joel M.; Posse, Christian

2004-12-30

Physical analogs have shown considerable promise for understanding the behavior of complex adaptive systems, including macroeconomics, biological systems, social networks, and electric power markets. Many of today's most challenging technical and policy questions can be reduced to a distributed economic control problem. Indeed, economically based control of large-scale systems is founded on the conjecture that the price-based regulation (e.g., auctions, markets) results in an optimal allocation of resources and emergent optimal system control. This report explores the state-of-the-art physical analogs for understanding the behavior of some econophysical systems and deriving stable and robust control strategies for using them. We reviewmore » and discuss applications of some analytic methods based on a thermodynamic metaphor, according to which the interplay between system entropy and conservation laws gives rise to intuitive and governing global properties of complex systems that cannot be otherwise understood. We apply these methods to the question of how power markets can be expected to behave under a variety of conditions.« less

Robust adaptive kinematic control of redundant robots

NASA Technical Reports Server (NTRS)

Tarokh, M.; Zuck, D. D.

1992-01-01

The paper presents a general method for the resolution of redundancy that combines the Jacobian pseudoinverse and augmentation approaches. A direct adaptive control scheme is developed to generate joint angle trajectories for achieving desired end-effector motion as well as additional user defined tasks. The scheme ensures arbitrarily small errors between the desired and the actual motion of the manipulator. Explicit bounds on the errors are established that are directly related to the mismatch between actual and estimated pseudoinverse Jacobian matrix, motion velocity and the controller gain. It is shown that the scheme is tolerant of the mismatch and consequently only infrequent pseudoinverse computations are needed during a typical robot motion. As a result, the scheme is computationally fast, and can be implemented for real-time control of redundant robots. A method is incorporated to cope with the robot singularities allowing the manipulator to get very close or even pass through a singularity while maintaining a good tracking performance and acceptable joint velocities. Computer simulations and experimental results are provided in support of the theoretical developments.

Bio-Inspired Integrated Sensing and Control Flapping Flight for Micro Aerial Vehicles

DTIC Science & Technology

2012-02-28

Atmospheric Flight Mechanics Conference, Chicago, IL, 2009, AIAA Paper 2009–6045. [56] B . Obradovic and K . Subbarao , “Modeling of dynamic loading of morphing...Robotics, vol. 26, no. 2, pp. 244 – 255, 2010. 51 [32] W. He, S. S. Ge, B . V. E. How, Y. S. Choo, and K . S. Hong, “Robust adaptive boundary control of a...ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON Soon-Jo Chung a. REPORT b . ABSTRACT c. THIS PAGE 19b. TELEPHONE NUMBER

Engineering of Fast and Robust Adaptive Control for Fixed-Wing Unmanned Aircraft

DTIC Science & Technology

2017-06-01

12.6volts) lithium polymer batteries wired in parallel. The remaining cargo space was used for accommodating the Pixhawk autopilot. 33 Figure 4.3...was achievable under four hours of build time. 4.4.1 Spear Specifications • weight without battery : 1.45 lbs (658 g) • center of gravity: 3 – 3.5” (76...motor: 425 sized, 1200 kv minimum • prop: 9 x 4.5 CW (reverse) prop • electronic speed control (ESC): 30 amp minimum • battery : (2) 2200 mAH 12.6 volt

Climate Change: From Science to Practice.

PubMed

Wheeler, Nicola; Watts, Nick

2018-03-01

Climate change poses a significant threat to human health. Understanding how climate science can be translated into public health practice is an essential first step in enabling robust adaptation and improving resiliency to climate change. Recent research highlights the importance of iterative approaches to public health adaptation to climate change, enabling uncertainties of health impacts and barriers to adaptation to be accounted for. There are still significant barriers to adaptation, which are context-specific and thus present unique challenges to public health practice. The implementation of flexible adaptation approaches, using frameworks targeted for public health, is key to ensuring robust adaptation to climate change in public health practice. The BRACE framework provides an excellent approach for health adaptation to climate change. Combining this with the insights provided and by the adaptation pathways approach allows for more deliberate accounting of long-term uncertainties. The mainstreaming of climate change adaptation into public health practice and planning is important in facilitating this approach and overcoming the significant barriers to effective adaptation. Yet, the immediate and future limits to adaptation provide clear justification for urgent and accelerated efforts to mitigate climate change.

Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization

NASA Astrophysics Data System (ADS)

Lin, Chuang; Wang, Binghui; Jiang, Ning; Farina, Dario

2018-04-01

Objective. This paper proposes a novel simultaneous and proportional multiple degree of freedom (DOF) myoelectric control method for active prostheses. Approach. The approach is based on non-negative matrix factorization (NMF) of surface EMG signals with the inclusion of sparseness constraints. By applying a sparseness constraint to the control signal matrix, it is possible to extract the basis information from arbitrary movements (quasi-unsupervised approach) for multiple DOFs concurrently. Main Results. In online testing based on target hitting, able-bodied subjects reached a greater throughput (TP) when using sparse NMF (SNMF) than with classic NMF or with linear regression (LR). Accordingly, the completion time (CT) was shorter for SNMF than NMF or LR. The same observations were made in two patients with unilateral limb deficiencies. Significance. The addition of sparseness constraints to NMF allows for a quasi-unsupervised approach to myoelectric control with superior results with respect to previous methods for the simultaneous and proportional control of multi-DOF. The proposed factorization algorithm allows robust simultaneous and proportional control, is superior to previous supervised algorithms, and, because of minimal supervision, paves the way to online adaptation in myoelectric control.

Model-free adaptive sliding mode controller design for generalized projective synchronization of the fractional-order chaotic system via radial basis function neural networks

NASA Astrophysics Data System (ADS)

Wang, L. M.

2017-09-01

A novel model-free adaptive sliding mode strategy is proposed for a generalized projective synchronization (GPS) between two entirely unknown fractional-order chaotic systems subject to the external disturbances. To solve the difficulties from the little knowledge about the master-slave system and to overcome the bad effects of the external disturbances on the generalized projective synchronization, the radial basis function neural networks are used to approach the packaged unknown master system and the packaged unknown slave system (including the external disturbances). Consequently, based on the slide mode technology and the neural network theory, a model-free adaptive sliding mode controller is designed to guarantee asymptotic stability of the generalized projective synchronization error. The main contribution of this paper is that a control strategy is provided for the generalized projective synchronization between two entirely unknown fractional-order chaotic systems subject to the unknown external disturbances, and the proposed control strategy only requires that the master system has the same fractional orders as the slave system. Moreover, the proposed method allows us to achieve all kinds of generalized projective chaos synchronizations by turning the user-defined parameters onto the desired values. Simulation results show the effectiveness of the proposed method and the robustness of the controlled system.

On the applicability of the decentralized control mechanism extracted from the true slime mold: a robotic case study with a serpentine robot.

PubMed

Sato, Takahide; Kano, Takeshi; Ishiguro, Akio

2011-06-01

A systematic method for an autonomous decentralized control system is still lacking, despite its appealing concept. In order to alleviate this, we focused on the amoeboid locomotion of the true slime mold, and extracted a design scheme for the decentralized control mechanism that leads to adaptive behavior for the entire system, based on the so-called discrepancy function. In this paper, we intensively investigate the universality of this design scheme by applying it to a different type of locomotion based on a 'synthetic approach'. As a first step, we implement this design scheme to the control of a real physical two-dimensional serpentine robot that exhibits slithering locomotion. The experimental results show that the robot exhibits adaptive behavior and responds to the environmental changes; it is also robust against malfunctions of the body segments due to the local sensory feedback control that is based on the discrepancy function. We expect the results to shed new light on the methodology of autonomous decentralized control systems.

Robust optimization model and algorithm for railway freight center location problem in uncertain environment.

PubMed

Liu, Xing-Cai; He, Shi-Wei; Song, Rui; Sun, Yang; Li, Hao-Dong

2014-01-01

Railway freight center location problem is an important issue in railway freight transport programming. This paper focuses on the railway freight center location problem in uncertain environment. Seeing that the expected value model ignores the negative influence of disadvantageous scenarios, a robust optimization model was proposed. The robust optimization model takes expected cost and deviation value of the scenarios as the objective. A cloud adaptive clonal selection algorithm (C-ACSA) was presented. It combines adaptive clonal selection algorithm with Cloud Model which can improve the convergence rate. Design of the code and progress of the algorithm were proposed. Result of the example demonstrates the model and algorithm are effective. Compared with the expected value cases, the amount of disadvantageous scenarios in robust model reduces from 163 to 21, which prove the result of robust model is more reliable.

Flatness-based adaptive fuzzy control of chaotic finance dynamics

NASA Astrophysics Data System (ADS)

Rigatos, G.; Siano, P.; Loia, V.; Tommasetti, A.; Troisi, O.

2017-11-01

A flatness-based adaptive fuzzy control is applied to the problem of stabilization of the dynamics of a chaotic finance system, describing interaction between the interest rate, the investment demand and the price exponent. By proving that the system is differentially flat and by applying differential flatness diffeomorphisms, its transformation to the linear canonical (Brunovsky) is performed. For the latter description of the system, the design of a stabilizing state feedback controller becomes possible. A first problem in the design of such a controller is that the dynamic model of the finance system is unknown and thus it has to be identified with the use neurofuzzy approximators. The estimated dynamics provided by the approximators is used in the computation of the control input, thus establishing an indirect adaptive control scheme. The learning rate of the approximators is chosen from the requirement the system's Lyapunov function to have always a negative first-order derivative. Another problem that has to be dealt with is that the control loop is implemented only with the use of output feedback. To estimate the non-measurable state vector elements of the finance system, a state observer is implemented in the control loop. The computation of the feedback control signal requires the solution of two algebraic Riccati equations at each iteration of the control algorithm. Lyapunov stability analysis demonstrates first that an H-infinity tracking performance criterion is satisfied. This signifies elevated robustness against modelling errors and external perturbations. Moreover, the global asymptotic stability is proven for the control loop.

Distributed Control Architecture for Gas Turbine Engine. Chapter 4

NASA Technical Reports Server (NTRS)

Culley, Dennis; Garg, Sanjay

2009-01-01

The transformation of engine control systems from centralized to distributed architecture is both necessary and enabling for future aeropropulsion applications. The continued growth of adaptive control applications and the trend to smaller, light weight cores is a counter influence on the weight and volume of control system hardware. A distributed engine control system using high temperature electronics and open systems communications will reverse the growing trend of control system weight ratio to total engine weight and also be a major factor in decreasing overall cost of ownership for aeropropulsion systems. The implementation of distributed engine control is not without significant challenges. There are the needs for high temperature electronics, development of simple, robust communications, and power supply for the on-board electronics.

Robust path planning for flexible needle insertion using Markov decision processes.

PubMed

Tan, Xiaoyu; Yu, Pengqian; Lim, Kah-Bin; Chui, Chee-Kong

2018-05-11

Flexible needle has the potential to accurately navigate to a treatment region in the least invasive manner. We propose a new planning method using Markov decision processes (MDPs) for flexible needle navigation that can perform robust path planning and steering under the circumstance of complex tissue-needle interactions. This method enhances the robustness of flexible needle steering from three different perspectives. First, the method considers the problem caused by soft tissue deformation. The method then resolves the common needle penetration failure caused by patterns of targets, while the last solution addresses the uncertainty issues in flexible needle motion due to complex and unpredictable tissue-needle interaction. Computer simulation and phantom experimental results show that the proposed method can perform robust planning and generate a secure control policy for flexible needle steering. Compared with a traditional method using MDPs, the proposed method achieves higher accuracy and probability of success in avoiding obstacles under complicated and uncertain tissue-needle interactions. Future work will involve experiment with biological tissue in vivo. The proposed robust path planning method can securely steer flexible needle within soft phantom tissues and achieve high adaptability in computer simulation.

Self-tuning bistable parametric feedback oscillator: Near-optimal amplitude maximization without model information

NASA Astrophysics Data System (ADS)

Braun, David J.; Sutas, Andrius; Vijayakumar, Sethu

2017-01-01

Theory predicts that parametrically excited oscillators, tuned to operate under resonant condition, are capable of large-amplitude oscillation useful in diverse applications, such as signal amplification, communication, and analog computation. However, due to amplitude saturation caused by nonlinearity, lack of robustness to model uncertainty, and limited sensitivity to parameter modulation, these oscillators require fine-tuning and strong modulation to generate robust large-amplitude oscillation. Here we present a principle of self-tuning parametric feedback excitation that alleviates the above-mentioned limitations. This is achieved using a minimalistic control implementation that performs (i) self-tuning (slow parameter adaptation) and (ii) feedback pumping (fast parameter modulation), without sophisticated signal processing past observations. The proposed approach provides near-optimal amplitude maximization without requiring model-based control computation, previously perceived inevitable to implement optimal control principles in practical application. Experimental implementation of the theory shows that the oscillator self-tunes itself near to the onset of dynamic bifurcation to achieve extreme sensitivity to small resonant parametric perturbations. As a result, it achieves large-amplitude oscillations by capitalizing on the effect of nonlinearity, despite substantial model uncertainties and strong unforeseen external perturbations. We envision the present finding to provide an effective and robust approach to parametric excitation when it comes to real-world application.

Robust Mean and Covariance Structure Analysis through Iteratively Reweighted Least Squares.

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Bentler, Peter M.

2000-01-01

Adapts robust schemes to mean and covariance structures, providing an iteratively reweighted least squares approach to robust structural equation modeling. Each case is weighted according to its distance, based on first and second order moments. Test statistics and standard error estimators are given. (SLD)

A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems

PubMed Central

Lyu, Weiwei

2017-01-01

Transfer alignment is always a key technology in a strapdown inertial navigation system (SINS) because of its rapidity and accuracy. In this paper a transfer alignment model is established, which contains the SINS error model and the measurement model. The time delay in the process of transfer alignment is analyzed, and an H∞ filtering method with delay compensation is presented. Then the H∞ filtering theory and the robust mechanism of H∞ filter are deduced and analyzed in detail. In order to improve the transfer alignment accuracy in SINS with time delay, an adaptive H∞ filtering method with delay compensation is proposed. Since the robustness factor plays an important role in the filtering process and has effect on the filtering accuracy, the adaptive H∞ filter with delay compensation can adjust the value of robustness factor adaptively according to the dynamic external environment. The vehicle transfer alignment experiment indicates that by using the adaptive H∞ filtering method with delay compensation, the transfer alignment accuracy and the pure inertial navigation accuracy can be dramatically improved, which demonstrates the superiority of the proposed filtering method. PMID:29182592

A robust adaptive flightpath reconstruction technique

NASA Technical Reports Server (NTRS)

Verhaegen, M. H.

1986-01-01

Computational schemes are presented that allow accurate reconstruction of an aircraft's flightpath in real-time. The reconstruction of the flightpath is formulated as a linear state reconstruction problem, which can be solved via Kalman filtering (KF) techniques. This imposes some conditions upon the flight-test equipment. A reliable square root covariance KF (SRCF) implementation is chosen and further developed into a fully adaptive flightpath reconstruction scheme. Therefore, the basic SRCF is modified in order to cope with several practical problems such as: the automatic control of the convergence of the recursive KF calculations, time varying zero-bias errors on the input signal of the system model used in the KF, and the changing aircraft dynamics owing to a change in reference flight condition. The developed solutions for these problems are all implemented in a numerically stable way, which guarantees the overall flightpath reconstruction scheme to be robust. Furthermore, some special features of the used system model are exploited to make the algorithmic implementation very efficient. An experimental simulation study using simulated flight test data demonstrated these different capabilities.

Fast and robust reconstruction for fluorescence molecular tomography via a sparsity adaptive subspace pursuit method.

PubMed

Ye, Jinzuo; Chi, Chongwei; Xue, Zhenwen; Wu, Ping; An, Yu; Xu, Han; Zhang, Shuang; Tian, Jie

2014-02-01

Fluorescence molecular tomography (FMT), as a promising imaging modality, can three-dimensionally locate the specific tumor position in small animals. However, it remains challenging for effective and robust reconstruction of fluorescent probe distribution in animals. In this paper, we present a novel method based on sparsity adaptive subspace pursuit (SASP) for FMT reconstruction. Some innovative strategies including subspace projection, the bottom-up sparsity adaptive approach, and backtracking technique are associated with the SASP method, which guarantees the accuracy, efficiency, and robustness for FMT reconstruction. Three numerical experiments based on a mouse-mimicking heterogeneous phantom have been performed to validate the feasibility of the SASP method. The results show that the proposed SASP method can achieve satisfactory source localization with a bias less than 1mm; the efficiency of the method is much faster than mainstream reconstruction methods; and this approach is robust even under quite ill-posed condition. Furthermore, we have applied this method to an in vivo mouse model, and the results demonstrate the feasibility of the practical FMT application with the SASP method.

Neural joint control for Space Shuttle Remote Manipulator System

NASA Technical Reports Server (NTRS)

Atkins, Mark A.; Cox, Chadwick J.; Lothers, Michael D.; Pap, Robert M.; Thomas, Charles R.

1992-01-01

Neural networks are being used to control a robot arm in a telerobotic operation. The concept uses neural networks for both joint and inverse kinematics in a robotic control application. An upper level neural network is trained to learn inverse kinematic mappings. The output, a trajectory, is then fed to the Decentralized Adaptive Joint Controllers. This neural network implementation has shown that the controlled arm recovers from unexpected payload changes while following the reference trajectory. The neural network-based decentralized joint controller is faster, more robust and efficient than conventional approaches. Implementations of this architecture are discussed that would relax assumptions about dynamics, obstacles, and heavy loads. This system is being developed to use with the Space Shuttle Remote Manipulator System.

Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation.

PubMed

Oyama, Shintaro; Shimoda, Shingo; Alnajjar, Fady S K; Iwatsuki, Katsuyuki; Hoshiyama, Minoru; Tanaka, Hirotaka; Hirata, Hitoshi

2016-01-01

Background: For mechanically reconstructing human biomechanical function, intuitive proportional control, and robustness to unexpected situations are required. Particularly, creating a functional hand prosthesis is a typical challenge in the reconstruction of lost biomechanical function. Nevertheless, currently available control algorithms are in the development phase. The most advanced algorithms for controlling multifunctional prosthesis are machine learning and pattern recognition of myoelectric signals. Despite the increase in computational speed, these methods cannot avoid the requirement of user consciousness and classified separation errors. "Tacit Learning System" is a simple but novel adaptive control strategy that can self-adapt its posture to environment changes. We introduced the strategy in the prosthesis rotation control to achieve compensatory reduction, as well as evaluated the system and its effects on the user. Methods: We conducted a non-randomized study involving eight prosthesis users to perform a bar relocation task with/without Tacit Learning System support. Hand piece and body motions were recorded continuously with goniometers, videos, and a motion-capture system. Findings: Reduction in the participants' upper extremity rotatory compensation motion was monitored during the relocation task in all participants. The estimated profile of total body energy consumption improved in five out of six participants. Interpretation: Our system rapidly accomplished nearly natural motion without unexpected errors. The Tacit Learning System not only adapts human motions but also enhances the human ability to adapt to the system quickly, while the system amplifies compensation generated by the residual limb. The concept can be extended to various situations for reconstructing lost functions that can be compensated.

Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks

NASA Astrophysics Data System (ADS)

Bennett, C.; Dunne, J. F.; Trimby, S.; Richardson, D.

2017-02-01

A recurrent non-linear autoregressive with exogenous input (NARX) neural network is proposed, and a suitable fully-recurrent training methodology is adapted and tuned, for reconstructing cylinder pressure in multi-cylinder IC engines using measured crank kinematics. This type of indirect sensing is important for cost effective closed-loop combustion control and for On-Board Diagnostics. The challenge addressed is to accurately predict cylinder pressure traces within the cycle under generalisation conditions: i.e. using data not previously seen by the network during training. This involves direct construction and calibration of a suitable inverse crank dynamic model, which owing to singular behaviour at top-dead-centre (TDC), has proved difficult via physical model construction, calibration, and inversion. The NARX architecture is specialised and adapted to cylinder pressure reconstruction, using a fully-recurrent training methodology which is needed because the alternatives are too slow and unreliable for practical network training on production engines. The fully-recurrent Robust Adaptive Gradient Descent (RAGD) algorithm, is tuned initially using synthesised crank kinematics, and then tested on real engine data to assess the reconstruction capability. Real data is obtained from a 1.125 l, 3-cylinder, in-line, direct injection spark ignition (DISI) engine involving synchronised measurements of crank kinematics and cylinder pressure across a range of steady-state speed and load conditions. The paper shows that a RAGD-trained NARX network using both crank velocity and crank acceleration as input information, provides fast and robust training. By using the optimum epoch identified during RAGD training, acceptably accurate cylinder pressures, and especially accurate location-of-peak-pressure, can be reconstructed robustly under generalisation conditions, making it the most practical NARX configuration and recurrent training methodology for use on production engines.

Investigation of whether in-room CT-based adaptive intracavitary brachytherapy for uterine cervical cancer is robust against interfractional location variations of organs and/or applicators.

PubMed

Oku, Yoshifumi; Arimura, Hidetaka; Nguyen, Tran Thi Thao; Hiraki, Yoshiyuki; Toyota, Masahiko; Saigo, Yasumasa; Yoshiura, Takashi; Hirata, Hideki

2016-11-01

This study investigates whether in-room computed tomography (CT)-based adaptive treatment planning (ATP) is robust against interfractional location variations, namely, interfractional organ motions and/or applicator displacements, in 3D intracavitary brachytherapy (ICBT) for uterine cervical cancer. In ATP, the radiation treatment plans, which have been designed based on planning CT images (and/or MR images) acquired just before the treatments, are adaptively applied for each fraction, taking into account the interfractional location variations. 2D and 3D plans with ATP for 14 patients were simulated for 56 fractions at a prescribed dose of 600 cGy per fraction. The standard deviations (SDs) of location displacements (interfractional location variations) of the target and organs at risk (OARs) with 3D ATP were significantly smaller than those with 2D ATP (P < 0.05). The homogeneity index (HI), conformity index (CI) and tumor control probability (TCP) in 3D ATP were significantly higher for high-risk clinical target volumes than those in 2D ATP. The SDs of the HI, CI, TCP, bladder and rectum D 2cc , and the bladder and rectum normal tissue complication probability (NTCP) in 3D ATP were significantly smaller than those in 2D ATP. The results of this study suggest that the interfractional location variations give smaller impacts on the planning evaluation indices in 3D ATP than in 2D ATP. Therefore, the 3D plans with ATP are expected to be robust against interfractional location variations in each treatment fraction. © The Author 2016. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology.

Estimating power capability of aged lithium-ion batteries in presence of communication delays

NASA Astrophysics Data System (ADS)

Fridholm, Björn; Wik, Torsten; Kuusisto, Hannes; Klintberg, Anton

2018-04-01

Efficient control of electrified powertrains requires accurate estimation of the power capability of the battery for the next few seconds into the future. When implemented in a vehicle, the power estimation is part of a control loop that may contain several networked controllers which introduces time delays that may jeopardize stability. In this article, we present and evaluate an adaptive power estimation method that robustly can handle uncertain health status and time delays. A theoretical analysis shows that stability of the closed loop system can be lost if the resistance of the model is under-estimated. Stability can, however, be restored by filtering the estimated power at the expense of slightly reduced bandwidth of the signal. The adaptive algorithm is experimentally validated in lab tests using an aged lithium-ion cell subject to a high power load profile in temperatures from -20 to +25 °C. The upper voltage limit was set to 4.15 V and the lower voltage limit to 2.6 V, where significant non-linearities are occurring and the validity of the model is limited. After an initial transient when the model parameters are adapted, the prediction accuracy is within ± 2 % of the actually available power.

Conflict monitoring and adaptation as reflected by N2 amplitude in obsessive-compulsive disorder.

PubMed

Riesel, A; Klawohn, J; Kathmann, N; Endrass, T

2017-06-01

Feelings of doubt and perseverative behaviours are key symptoms of obsessive-compulsive disorder (OCD) and have been linked to hyperactive error and conflict signals in the brain. While enhanced neural correlates of error monitoring have been robustly shown, far less is known about conflict processing and adaptation in OCD. We examined event-related potentials during conflict processing in 70 patients with OCD and 70 matched healthy comparison participants, focusing on the stimulus-locked N2 elicited in a flanker task. Conflict adaptation was evaluated by analysing sequential adjustments in N2 and behaviour, i.e. current conflict effects as a function of preceding conflict. Patients with OCD showed enhanced N2 amplitudes compared with healthy controls. Further, patients showed stronger conflict adaptation effects on reaction times and N2 amplitude. Thus, the effect of previous compatibility was larger in patients than in healthy participants as indicated by greater N2 adjustments in change trials (i.e. iC, cI). As a result of stronger conflict adaptation in patients, N2 amplitudes were comparable between groups in incompatible trials following incompatible trials. Larger N2 amplitudes and greater conflict adaptation in OCD point to enhanced conflict monitoring leading to increased recruitment of cognitive control in patients. This was most pronounced in change trials and was associated with stronger conflict adjustment in N2 and behaviour. Thus, hyperactive conflict monitoring in OCD may be beneficial in situations that require a high amount of control to resolve conflict, but may also reflect an effortful process that is linked to distress and symptoms of OCD.

A-Book: A Feedback-Based Adaptive System to Enhance Meta-Cognitive Skills during Reading.

PubMed

Guerra, Ernesto; Mellado, Guido

2017-01-01

In the digital era, tech devices (hardware and software) are increasingly within hand's reach. Yet, implementing information and communication technologies for educational contexts that have robust and long-lasting effects on student learning outcomes is still a challenge. We propose that any such system must a) be theoretically motivated and designed to tackle specific cognitive skills (e.g., inference making) supporting a given cognitive task (e.g., reading comprehension) and b) must be able to identify and adapt to the user's profile. In the present study, we implemented a feedback-based adaptive system called A-book (assisted-reading book) and tested it in a sample of 4th, 5th, and 6th graders. To assess our hypotheses, we contrasted three experimental assisted-reading conditions; one that supported meta-cognitive skills and adapted to the user profile (adaptive condition), one that supported meta-cognitive skills but did not adapt to the user profile (training condition) and a control condition. The results provide initial support for our proposal; participants in the adaptive condition improved their accuracy scores on inference making questions over time, outperforming both the training and control groups. There was no evidence, however, of significant improvements on other tested meta-cognitive skills (i.e., text structure knowledge, comprehension monitoring). We discussed the practical implications of using the A-book for the enhancement of meta-cognitive skills in school contexts, as well as its current limitations and future developments that could improve the system.

Effect of light intensity on flight control and temporal properties of photoreceptors in bumblebees.

PubMed

Reber, Therese; Vähäkainu, Antti; Baird, Emily; Weckström, Matti; Warrant, Eric; Dacke, Marie

2015-05-01

To control flight, insects rely on the pattern of visual motion generated on the retina as they move through the environment. When light levels fall, vision becomes less reliable and flight control thus becomes more challenging. Here, we investigated the effect of light intensity on flight control by filming the trajectories of free-flying bumblebees (Bombus terrestris, Linnaeus 1758) in an experimental tunnel at different light levels. As light levels fell, flight speed decreased and the flight trajectories became more tortuous but the bees were still remarkably good at centring their flight about the tunnel's midline. To investigate whether this robust flight performance can be explained by visual adaptations in the bumblebee retina, we also examined the response speed of the green-sensitive photoreceptors at the same light intensities. We found that the response speed of the photoreceptors significantly decreased as light levels fell. This indicates that bumblebees have both behavioural (reduction in flight speed) and retinal (reduction in response speed of the photoreceptors) adaptations to allow them to fly in dim light. However, the more tortuous flight paths recorded in dim light suggest that these adaptations do not support flight with the same precision during the twilight hours of the day. © 2015. Published by The Company of Biologists Ltd.

Orthonormal filters for identification in active control systems

NASA Astrophysics Data System (ADS)

Mayer, Dirk

2015-12-01

Many active noise and vibration control systems require models of the control paths. When the controlled system changes slightly over time, adaptive digital filters for the identification of the models are useful. This paper aims at the investigation of a special class of adaptive digital filters: orthonormal filter banks possess the robust and simple adaptation of the widely applied finite impulse response (FIR) filters, but at a lower model order, which is important when considering implementation on embedded systems. However, the filter banks require prior knowledge about the resonance frequencies and damping of the structure. This knowledge can be supposed to be of limited precision, since in many practical systems, uncertainties in the structural parameters exist. In this work, a procedure using a number of training systems to find the fixed parameters for the filter banks is applied. The effect of uncertainties in the prior knowledge on the model error is examined both with a basic example and in an experiment. Furthermore, the possibilities to compensate for the imprecise prior knowledge by a higher filter order are investigated. Also comparisons with FIR filters are implemented in order to assess the possible advantages of the orthonormal filter banks. Numerical and experimental investigations show that significantly lower computational effort can be reached by the filter banks under certain conditions.

A modular robust control framework for control of movement elicited by multi-electrode intraspinal microstimulation

NASA Astrophysics Data System (ADS)

Roshani, Amir; Erfanian, Abbas

2016-08-01

Objective. An important issue in restoring motor function through intraspinal microstimulation (ISMS) is the motor control. To provide a physiologically plausible motor control using ISMS, it should be able to control the individual motor unit which is the lowest functional unit of motor control. By focal stimulation only a small group of motor neurons (MNs) within a motor pool can be activated. Different groups of MNs within a motor pool can potentially be activated without involving adjacent motor pools by local stimulation of different parts of a motor pool via microelectrode array implanted into a motor pool. However, since the system has multiple inputs with single output during multi-electrode ISMS, it poses a challenge to movement control. In this paper, we proposed a modular robust control strategy for movement control, whereas multi-electrode array is implanted into each motor activation pool of a muscle. Approach. The controller was based on the combination of proportional-integral-derivative and adaptive fuzzy sliding mode control. The global stability of the controller was guaranteed. Main results. The results of the experiments on rat models showed that the multi-electrode control can provide a more robust control and accurate tracking performance than a single-electrode control. The control output can be pulse amplitude (pulse amplitude modulation, PAM) or pulse width (pulse width modulation, PWM) of the stimulation signal. The results demonstrated that the controller with PAM provided faster convergence rate and better tracking performance than the controller with PWM. Significance. This work represents a promising control approach to the restoring motor functions using ISMS. The proposed controller requires no prior knowledge about the dynamics of the system to be controlled and no offline learning phase. The proposed control design is modular in the sense that each motor pool has an independent controller and each controller is able to control ISMS through an array of microelectrodes.

Illumination robust face recognition using spatial adaptive shadow compensation based on face intensity prior

NASA Astrophysics Data System (ADS)

Hsieh, Cheng-Ta; Huang, Kae-Horng; Lee, Chang-Hsing; Han, Chin-Chuan; Fan, Kuo-Chin

2017-12-01

Robust face recognition under illumination variations is an important and challenging task in a face recognition system, particularly for face recognition in the wild. In this paper, a face image preprocessing approach, called spatial adaptive shadow compensation (SASC), is proposed to eliminate shadows in the face image due to different lighting directions. First, spatial adaptive histogram equalization (SAHE), which uses face intensity prior model, is proposed to enhance the contrast of each local face region without generating visible noises in smooth face areas. Adaptive shadow compensation (ASC), which performs shadow compensation in each local image block, is then used to produce a wellcompensated face image appropriate for face feature extraction and recognition. Finally, null-space linear discriminant analysis (NLDA) is employed to extract discriminant features from SASC compensated images. Experiments performed on the Yale B, Yale B extended, and CMU PIE face databases have shown that the proposed SASC always yields the best face recognition accuracy. That is, SASC is more robust to face recognition under illumination variations than other shadow compensation approaches.

The performance of a reduced-order adaptive controller when used in multi-antenna hyperthermia treatments with nonlinear temperature-dependent perfusion.

PubMed

Cheng, Kung-Shan; Yuan, Yu; Li, Zhen; Stauffer, Paul R; Maccarini, Paolo; Joines, William T; Dewhirst, Mark W; Das, Shiva K

2009-04-07

In large multi-antenna systems, adaptive controllers can aid in steering the heat focus toward the tumor. However, the large number of sources can greatly increase the steering time. Additionally, controller performance can be degraded due to changes in tissue perfusion which vary non-linearly with temperature, as well as with time and spatial position. The current work investigates whether a reduced-order controller with the assumption of piecewise constant perfusion is robust to temperature-dependent perfusion and achieves steering in a shorter time than required by a full-order controller. The reduced-order controller assumes that the optimal heating setting lies in a subspace spanned by the best heating vectors (virtual sources) of an initial, approximate, patient model. An initial, approximate, reduced-order model is iteratively updated by the controller, using feedback thermal images, until convergence of the heat focus to the tumor. Numerical tests were conducted in a patient model with a right lower leg sarcoma, heated in a 10-antenna cylindrical mini-annual phased array applicator operating at 150 MHz. A half-Gaussian model was used to simulate temperature-dependent perfusion. Simulated magnetic resonance temperature images were used as feedback at each iteration step. Robustness was validated for the controller, starting from four approximate initial models: (1) a 'standard' constant perfusion lower leg model ('standard' implies a model that exactly models the patient with the exception that perfusion is considered constant, i.e., not temperature dependent), (2) a model with electrical and thermal tissue properties varied from 50% higher to 50% lower than the standard model, (3) a simplified constant perfusion pure-muscle lower leg model with +/-50% deviated properties and (4) a standard model with the tumor position in the leg shifted by 1.5 cm. Convergence to the desired focus of heating in the tumor was achieved for all four simulated models. The controller accomplished satisfactory therapeutic outcomes: approximately 80% of the tumor was heated to temperatures 43 degrees C and approximately 93% was maintained at temperatures <41 degrees C. Compared to the controller without model reduction, a approximately 9-25 fold reduction in convergence time was accomplished using approximately 2-3 orthonormal virtual sources. In the situations tested, the controller was robust to the presence of temperature-dependent perfusion. The results of this work can help to lay the foundation for real-time thermal control of multi-antenna hyperthermia systems in clinical situations where perfusion can change rapidly with temperature.

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation

PubMed Central

Kong, Zehui; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control. PMID:28671967

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

PubMed

Kong, Zehui; Zou, Yuan; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

A fast new algorithm for a robot neurocontroller using inverse QR decomposition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morris, A.S.; Khemaissia, S.

2000-01-01

A new adaptive neural network controller for robots is presented. The controller is based on direct adaptive techniques. Unlike many neural network controllers in the literature, inverse dynamical model evaluation is not required. A numerically robust, computationally efficient processing scheme for neutral network weight estimation is described, namely, the inverse QR decomposition (INVQR). The inverse QR decomposition and a weighted recursive least-squares (WRLS) method for neural network weight estimation is derived using Cholesky factorization of the data matrix. The algorithm that performs the efficient INVQR of the underlying space-time data matrix may be implemented in parallel on a triangular array.more » Furthermore, its systolic architecture is well suited for VLSI implementation. Another important benefit is well suited for VLSI implementation. Another important benefit of the INVQR decomposition is that it solves directly for the time-recursive least-squares filter vector, while avoiding the sequential back-substitution step required by the QR decomposition approaches.« less

Platform for Automated Real-Time High Performance Analytics on Medical Image Data.

PubMed

Allen, William J; Gabr, Refaat E; Tefera, Getaneh B; Pednekar, Amol S; Vaughn, Matthew W; Narayana, Ponnada A

2018-03-01

Biomedical data are quickly growing in volume and in variety, providing clinicians an opportunity for better clinical decision support. Here, we demonstrate a robust platform that uses software automation and high performance computing (HPC) resources to achieve real-time analytics of clinical data, specifically magnetic resonance imaging (MRI) data. We used the Agave application programming interface to facilitate communication, data transfer, and job control between an MRI scanner and an off-site HPC resource. In this use case, Agave executed the graphical pipeline tool GRAphical Pipeline Environment (GRAPE) to perform automated, real-time, quantitative analysis of MRI scans. Same-session image processing will open the door for adaptive scanning and real-time quality control, potentially accelerating the discovery of pathologies and minimizing patient callbacks. We envision this platform can be adapted to other medical instruments, HPC resources, and analytics tools.

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding

PubMed Central

Resnik, Andrey; Celikel, Tansu; Englitz, Bernhard

2016-01-01

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information. PMID:27304526

Adaptive Spike Threshold Enables Robust and Temporally Precise Neuronal Encoding.

PubMed

Huang, Chao; Resnik, Andrey; Celikel, Tansu; Englitz, Bernhard

2016-06-01

Neural processing rests on the intracellular transformation of information as synaptic inputs are translated into action potentials. This transformation is governed by the spike threshold, which depends on the history of the membrane potential on many temporal scales. While the adaptation of the threshold after spiking activity has been addressed before both theoretically and experimentally, it has only recently been demonstrated that the subthreshold membrane state also influences the effective spike threshold. The consequences for neural computation are not well understood yet. We address this question here using neural simulations and whole cell intracellular recordings in combination with information theoretic analysis. We show that an adaptive spike threshold leads to better stimulus discrimination for tight input correlations than would be achieved otherwise, independent from whether the stimulus is encoded in the rate or pattern of action potentials. The time scales of input selectivity are jointly governed by membrane and threshold dynamics. Encoding information using adaptive thresholds further ensures robust information transmission across cortical states i.e. decoding from different states is less state dependent in the adaptive threshold case, if the decoding is performed in reference to the timing of the population response. Results from in vitro neural recordings were consistent with simulations from adaptive threshold neurons. In summary, the adaptive spike threshold reduces information loss during intracellular information transfer, improves stimulus discriminability and ensures robust decoding across membrane states in a regime of highly correlated inputs, similar to those seen in sensory nuclei during the encoding of sensory information.

Efficient robust doubly adaptive regularized regression with applications.

PubMed

Karunamuni, Rohana J; Kong, Linglong; Tu, Wei

2018-01-01

We consider the problem of estimation and variable selection for general linear regression models. Regularized regression procedures have been widely used for variable selection, but most existing methods perform poorly in the presence of outliers. We construct a new penalized procedure that simultaneously attains full efficiency and maximum robustness. Furthermore, the proposed procedure satisfies the oracle properties. The new procedure is designed to achieve sparse and robust solutions by imposing adaptive weights on both the decision loss and the penalty function. The proposed method of estimation and variable selection attains full efficiency when the model is correct and, at the same time, achieves maximum robustness when outliers are present. We examine the robustness properties using the finite-sample breakdown point and an influence function. We show that the proposed estimator attains the maximum breakdown point. Furthermore, there is no loss in efficiency when there are no outliers or the error distribution is normal. For practical implementation of the proposed method, we present a computational algorithm. We examine the finite-sample and robustness properties using Monte Carlo studies. Two datasets are also analyzed.

Neural network controller development for a magnetically suspended flywheel energy storage system

NASA Technical Reports Server (NTRS)

Fittro, Roger L.; Pang, Da-Chen; Anand, Davinder K.

1994-01-01

A neural network controller has been developed to accommodate disturbances and nonlinearities and improve the robustness of a magnetically suspended flywheel energy storage system. The controller is trained using the back propagation-through-time technique incorporated with a time-averaging scheme. The resulting nonlinear neural network controller improves system performance by adapting flywheel stiffness and damping based on operating speed. In addition, a hybrid multi-layered neural network controller is developed off-line which is capable of improving system performance even further. All of the research presented in this paper was implemented via a magnetic bearing computer simulation. However, careful attention was paid to developing a practical methodology which will make future application to the actual bearing system fairly straightforward.

A New Look at NASA: Strategic Research In Information Technology

NASA Technical Reports Server (NTRS)

Alfano, David; Tu, Eugene (Technical Monitor)

2002-01-01

This viewgraph presentation provides information on research undertaken by NASA to facilitate the development of information technologies. Specific ideas covered here include: 1) Bio/nano technologies: biomolecular and nanoscale systems and tools for assembly and computing; 2) Evolvable hardware: autonomous self-improving, self-repairing hardware and software for survivable space systems in extreme environments; 3) High Confidence Software Technologies: formal methods, high-assurance software design, and program synthesis; 4) Intelligent Controls and Diagnostics: Next generation machine learning, adaptive control, and health management technologies; 5) Revolutionary computing: New computational models to increase capability and robustness to enable future NASA space missions.

Adaptive Evolution Is Substantially Impeded by Hill–Robertson Interference in Drosophila

PubMed Central

Castellano, David; Coronado-Zamora, Marta; Campos, Jose L.; Barbadilla, Antonio; Eyre-Walker, Adam

2016-01-01

Hill–Robertson interference (HRi) is expected to reduce the efficiency of natural selection when two or more linked selected sites do not segregate freely, but no attempt has been done so far to quantify the overall impact of HRi on the rate of adaptive evolution for any given genome. In this work, we estimate how much HRi impedes the rate of adaptive evolution in the coding genome of Drosophila melanogaster. We compiled a data set of 6,141 autosomal protein-coding genes from Drosophila, from which polymorphism levels in D. melanogaster and divergence out to D. yakuba were estimated. The rate of adaptive evolution was calculated using a derivative of the McDonald–Kreitman test that controls for slightly deleterious mutations. We find that the rate of adaptive amino acid substitution at a given position of the genome is positively correlated to both the rate of recombination and the mutation rate, and negatively correlated to the gene density of the region. These correlations are robust to controlling for each other, for synonymous codon bias and for gene functions related to immune response and testes. We show that HRi diminishes the rate of adaptive evolution by approximately 27%. Interestingly, genes with low mutation rates embedded in gene poor regions lose approximately 17% of their adaptive substitutions whereas genes with high mutation rates embedded in gene rich regions lose approximately 60%. We conclude that HRi hampers the rate of adaptive evolution in Drosophila and that the variation in recombination, mutation, and gene density along the genome affects the HRi effect. PMID:26494843

An adaptive discontinuous Galerkin solver for aerodynamic flows

NASA Astrophysics Data System (ADS)

Burgess, Nicholas K.

This work considers the accuracy, efficiency, and robustness of an unstructured high-order accurate discontinuous Galerkin (DG) solver for computational fluid dynamics (CFD). Recently, there has been a drive to reduce the discretization error of CFD simulations using high-order methods on unstructured grids. However, high-order methods are often criticized for lacking robustness and having high computational cost. The goal of this work is to investigate methods that enhance the robustness of high-order discontinuous Galerkin (DG) methods on unstructured meshes, while maintaining low computational cost and high accuracy of the numerical solutions. This work investigates robustness enhancement of high-order methods by examining effective non-linear solvers, shock capturing methods, turbulence model discretizations and adaptive refinement techniques. The goal is to develop an all encompassing solver that can simulate a large range of physical phenomena, where all aspects of the solver work together to achieve a robust, efficient and accurate solution strategy. The components and framework for a robust high-order accurate solver that is capable of solving viscous, Reynolds Averaged Navier-Stokes (RANS) and shocked flows is presented. In particular, this work discusses robust discretizations of the turbulence model equation used to close the RANS equations, as well as stable shock capturing strategies that are applicable across a wide range of discretization orders and applicable to very strong shock waves. Furthermore, refinement techniques are considered as both efficiency and robustness enhancement strategies. Additionally, efficient non-linear solvers based on multigrid and Krylov subspace methods are presented. The accuracy, efficiency, and robustness of the solver is demonstrated using a variety of challenging aerodynamic test problems, which include turbulent high-lift and viscous hypersonic flows. Adaptive mesh refinement was found to play a critical role in obtaining a robust and efficient high-order accurate flow solver. A goal-oriented error estimation technique has been developed to estimate the discretization error of simulation outputs. For high-order discretizations, it is shown that functional output error super-convergence can be obtained, provided the discretization satisfies a property known as dual consistency. The dual consistency of the DG methods developed in this work is shown via mathematical analysis and numerical experimentation. Goal-oriented error estimation is also used to drive an hp-adaptive mesh refinement strategy, where a combination of mesh or h-refinement, and order or p-enrichment, is employed based on the smoothness of the solution. The results demonstrate that the combination of goal-oriented error estimation and hp-adaptation yield superior accuracy, as well as enhanced robustness and efficiency for a variety of aerodynamic flows including flows with strong shock waves. This work demonstrates that DG discretizations can be the basis of an accurate, efficient, and robust CFD solver. Furthermore, enhancing the robustness of DG methods does not adversely impact the accuracy or efficiency of the solver for challenging and complex flow problems. In particular, when considering the computation of shocked flows, this work demonstrates that the available shock capturing techniques are sufficiently accurate and robust, particularly when used in conjunction with adaptive mesh refinement . This work also demonstrates that robust solutions of the Reynolds Averaged Navier-Stokes (RANS) and turbulence model equations can be obtained for complex and challenging aerodynamic flows. In this context, the most robust strategy was determined to be a low-order turbulence model discretization coupled to a high-order discretization of the RANS equations. Although RANS solutions using high-order accurate discretizations of the turbulence model were obtained, the behavior of current-day RANS turbulence models discretized to high-order was found to be problematic, leading to solver robustness issues. This suggests that future work is warranted in the area of turbulence model formulation for use with high-order discretizations. Alternately, the use of Large-Eddy Simulation (LES) subgrid scale models with high-order DG methods offers the potential to leverage the high accuracy of these methods for very high fidelity turbulent simulations. This thesis has developed the algorithmic improvements that will lay the foundation for the development of a three-dimensional high-order flow solution strategy that can be used as the basis for future LES simulations.

Failure of Anisotropic Unstructured Mesh Adaption Based on Multidimensional Residual Minimization

NASA Technical Reports Server (NTRS)

Wood, William A.; Kleb, William L.

2003-01-01

An automated anisotropic unstructured mesh adaptation strategy is proposed, implemented, and assessed for the discretization of viscous flows. The adaption criteria is based upon the minimization of the residual fluctuations of a multidimensional upwind viscous flow solver. For scalar advection, this adaption strategy has been shown to use fewer grid points than gradient based adaption, naturally aligning mesh edges with discontinuities and characteristic lines. The adaption utilizes a compact stencil and is local in scope, with four fundamental operations: point insertion, point deletion, edge swapping, and nodal displacement. Evaluation of the solution-adaptive strategy is performed for a two-dimensional blunt body laminar wind tunnel case at Mach 10. The results demonstrate that the strategy suffers from a lack of robustness, particularly with regard to alignment of the bow shock in the vicinity of the stagnation streamline. In general, constraining the adaption to such a degree as to maintain robustness results in negligible improvement to the solution. Because the present method fails to consistently or significantly improve the flow solution, it is rejected in favor of simple uniform mesh refinement.

Homeostasis control of building environment using sensor agent robot

NASA Astrophysics Data System (ADS)

Nagahama, Eri; Mita, Akira

2012-04-01

A human centered system for building is demanded to meet variety of needs due to the diversification and maturation of society. Smart buildings and smart houses have been studied to satisfy this demand. However, it is difficult for such systems to respond flexibly to unexpected events and needs that are caused by aging and complicate emotion changes. With this regards, we suggest "Biofied Buildings". The goal for this research is to realize buildings that are safer, more comfortable and more energy-efficient by embedding adaptive functions of life into buildings. In this paper, we propose a new control system for building environments, focused on physiological adaptation, particularly homeostasis, endocrine system and immune system. Residents are used as living sensors and controllers in the control loop. A sensor agent robot is used to acquire resident's discomfort feeling, and to output hormone-like signals to activate devices to control the environments. The proposed system could control many devices without establishing complicated scenarios. Results obtained from some simulations and the demonstration experiments using an LED lighting system showed that the proposed system were able to achieve robust and stable control of environments without complicated scenarios.

Characterizing cognitive control abilities in children with 16p11.2 deletion using adaptive 'video game' technology: a pilot study.

PubMed

Anguera, J A; Brandes-Aitken, A N; Rolle, C E; Skinner, S N; Desai, S S; Bower, J D; Martucci, W E; Chung, W K; Sherr, E H; Marco, E J

2016-09-20

Assessing cognitive abilities in children is challenging for two primary reasons: lack of testing engagement can lead to low testing sensitivity and inherent performance variability. Here we sought to explore whether an engaging, adaptive digital cognitive platform built to look and feel like a video game would reliably measure attention-based abilities in children with and without neurodevelopmental disabilities related to a known genetic condition, 16p11.2 deletion. We assessed 20 children with 16p11.2 deletion, a genetic variation implicated in attention deficit/hyperactivity disorder and autism, as well as 16 siblings without the deletion and 75 neurotypical age-matched children. Deletion carriers showed significantly slower response times and greater response variability when compared with all non-carriers; by comparison, traditional non-adaptive selective attention assessments were unable to discriminate group differences. This phenotypic characterization highlights the potential power of administering tools that integrate adaptive psychophysical mechanics into video-game-style mechanics to achieve robust, reliable measurements.

How MAP kinase modules function as robust, yet adaptable, circuits.

PubMed

Tian, Tianhai; Harding, Angus

2014-01-01

Genetic and biochemical studies have revealed that the diversity of cell types and developmental patterns evident within the animal kingdom is generated by a handful of conserved, core modules. Core biological modules must be robust, able to maintain functionality despite perturbations, and yet sufficiently adaptable for random mutations to generate phenotypic variation during evolution. Understanding how robust, adaptable modules have influenced the evolution of eukaryotes will inform both evolutionary and synthetic biology. One such system is the MAP kinase module, which consists of a 3-tiered kinase circuit configuration that has been evolutionarily conserved from yeast to man. MAP kinase signal transduction pathways are used across eukaryotic phyla to drive biological functions that are crucial for life. Here we ask the fundamental question, why do MAPK modules follow a conserved 3-tiered topology rather than some other number? Using computational simulations, we identify a fundamental 2-tiered circuit topology that can be readily reconfigured by feedback loops and scaffolds to generate diverse signal outputs. When this 2-kinase circuit is connected to proximal input kinases, a 3-tiered modular configuration is created that is both robust and adaptable, providing a biological circuit that can regulate multiple phenotypes and maintain functionality in an uncertain world. We propose that the 3-tiered signal transduction module has been conserved through positive selection, because it facilitated the generation of phenotypic variation during eukaryotic evolution.

How MAP kinase modules function as robust, yet adaptable, circuits

PubMed Central

Tian, Tianhai; Harding, Angus

2014-01-01

Genetic and biochemical studies have revealed that the diversity of cell types and developmental patterns evident within the animal kingdom is generated by a handful of conserved, core modules. Core biological modules must be robust, able to maintain functionality despite perturbations, and yet sufficiently adaptable for random mutations to generate phenotypic variation during evolution. Understanding how robust, adaptable modules have influenced the evolution of eukaryotes will inform both evolutionary and synthetic biology. One such system is the MAP kinase module, which consists of a 3-tiered kinase circuit configuration that has been evolutionarily conserved from yeast to man. MAP kinase signal transduction pathways are used across eukaryotic phyla to drive biological functions that are crucial for life. Here we ask the fundamental question, why do MAPK modules follow a conserved 3-tiered topology rather than some other number? Using computational simulations, we identify a fundamental 2-tiered circuit topology that can be readily reconfigured by feedback loops and scaffolds to generate diverse signal outputs. When this 2-kinase circuit is connected to proximal input kinases, a 3-tiered modular configuration is created that is both robust and adaptable, providing a biological circuit that can regulate multiple phenotypes and maintain functionality in an uncertain world. We propose that the 3-tiered signal transduction module has been conserved through positive selection, because it facilitated the generation of phenotypic variation during eukaryotic evolution. PMID:25483189

Physical constraints on biological integral control design for homeostasis and sensory adaptation.

PubMed

Ang, Jordan; McMillen, David R

2013-01-22

Synthetic biology includes an effort to use design-based approaches to create novel controllers, biological systems aimed at regulating the output of other biological processes. The design of such controllers can be guided by results from control theory, including the strategy of integral feedback control, which is central to regulation, sensory adaptation, and long-term robustness. Realization of integral control in a synthetic network is an attractive prospect, but the nature of biochemical networks can make the implementation of even basic control structures challenging. Here we present a study of the general challenges and important constraints that will arise in efforts to engineer biological integral feedback controllers or to analyze existing natural systems. Constraints arise from the need to identify target output values that the combined process-plus-controller system can reach, and to ensure that the controller implements a good approximation of integral feedback control. These constraints depend on mild assumptions about the shape of input-output relationships in the biological components, and thus will apply to a variety of biochemical systems. We summarize our results as a set of variable constraints intended to provide guidance for the design or analysis of a working biological integral feedback controller. Copyright © 2013 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Exploring critical pathways for urban water management to identify robust strategies under deep uncertainties.

PubMed

Urich, Christian; Rauch, Wolfgang

2014-12-01

Long-term projections for key drivers needed in urban water infrastructure planning such as climate change, population growth, and socio-economic changes are deeply uncertain. Traditional planning approaches heavily rely on these projections, which, if a projection stays unfulfilled, can lead to problematic infrastructure decisions causing high operational costs and/or lock-in effects. New approaches based on exploratory modelling take a fundamentally different view. Aim of these is, to identify an adaptation strategy that performs well under many future scenarios, instead of optimising a strategy for a handful. However, a modelling tool to support strategic planning to test the implication of adaptation strategies under deeply uncertain conditions for urban water management does not exist yet. This paper presents a first step towards a new generation of such strategic planning tools, by combing innovative modelling tools, which coevolve the urban environment and urban water infrastructure under many different future scenarios, with robust decision making. The developed approach is applied to the city of Innsbruck, Austria, which is spatially explicitly evolved 20 years into the future under 1000 scenarios to test the robustness of different adaptation strategies. Key findings of this paper show that: (1) Such an approach can be used to successfully identify parameter ranges of key drivers in which a desired performance criterion is not fulfilled, which is an important indicator for the robustness of an adaptation strategy; and (2) Analysis of the rich dataset gives new insights into the adaptive responses of agents to key drivers in the urban system by modifying a strategy. Copyright © 2014 Elsevier Ltd. All rights reserved.

Data-Adaptive Bias-Reduced Doubly Robust Estimation.

PubMed

Vermeulen, Karel; Vansteelandt, Stijn

2016-05-01

Doubly robust estimators have now been proposed for a variety of target parameters in the causal inference and missing data literature. These consistently estimate the parameter of interest under a semiparametric model when one of two nuisance working models is correctly specified, regardless of which. The recently proposed bias-reduced doubly robust estimation procedure aims to partially retain this robustness in more realistic settings where both working models are misspecified. These so-called bias-reduced doubly robust estimators make use of special (finite-dimensional) nuisance parameter estimators that are designed to locally minimize the squared asymptotic bias of the doubly robust estimator in certain directions of these finite-dimensional nuisance parameters under misspecification of both parametric working models. In this article, we extend this idea to incorporate the use of data-adaptive estimators (infinite-dimensional nuisance parameters), by exploiting the bias reduction estimation principle in the direction of only one nuisance parameter. We additionally provide an asymptotic linearity theorem which gives the influence function of the proposed doubly robust estimator under correct specification of a parametric nuisance working model for the missingness mechanism/propensity score but a possibly misspecified (finite- or infinite-dimensional) outcome working model. Simulation studies confirm the desirable finite-sample performance of the proposed estimators relative to a variety of other doubly robust estimators.

Force control compensation method with variable load stiffness and damping of the hydraulic drive unit force control system

NASA Astrophysics Data System (ADS)

Kong, Xiangdong; Ba, Kaixian; Yu, Bin; Cao, Yuan; Zhu, Qixin; Zhao, Hualong

2016-05-01

Each joint of hydraulic drive quadruped robot is driven by the hydraulic drive unit (HDU), and the contacting between the robot foot end and the ground is complex and variable, which increases the difficulty of force control inevitably. In the recent years, although many scholars researched some control methods such as disturbance rejection control, parameter self-adaptive control, impedance control and so on, to improve the force control performance of HDU, the robustness of the force control still needs improving. Therefore, how to simulate the complex and variable load characteristics of the environment structure and how to ensure HDU having excellent force control performance with the complex and variable load characteristics are key issues to be solved in this paper. The force control system mathematic model of HDU is established by the mechanism modeling method, and the theoretical models of a novel force control compensation method and a load characteristics simulation method under different environment structures are derived, considering the dynamic characteristics of the load stiffness and the load damping under different environment structures. Then, simulation effects of the variable load stiffness and load damping under the step and sinusoidal load force are analyzed experimentally on the HDU force control performance test platform, which provides the foundation for the force control compensation experiment research. In addition, the optimized PID control parameters are designed to make the HDU have better force control performance with suitable load stiffness and load damping, under which the force control compensation method is introduced, and the robustness of the force control system with several constant load characteristics and the variable load characteristics respectively are comparatively analyzed by experiment. The research results indicate that if the load characteristics are known, the force control compensation method presented in this paper has positive compensation effects on the load characteristics variation, i.e., this method decreases the effects of the load characteristics variation on the force control performance and enhances the force control system robustness with the constant PID parameters, thereby, the online PID parameters tuning control method which is complex needs not be adopted. All the above research provides theoretical and experimental foundation for the force control method of the quadruped robot joints with high robustness.

Two-Thumbed Robot Hand

NASA Technical Reports Server (NTRS)

Lee, Sukhan

1989-01-01

Robot hand includes thumblike members on left and right sides and fingerlike member at middle. Configuration of digits enables hand to adapt to variously shaped objects, grasp them robustly and reliably, and manipulate them. Reduces complexity of control mechanisms and provides kinesthetic perception of shapes of grasped objects. Mechanical hand with two thumbs and middle finger made from commercially available components. With specially designed dc motors and assemblies of gears, size of hand reduced considerably. Suited to handling objects in industrial tasks.

An optimal model-based trajectory following architecture synthesising the lateral adaptive preview strategy and longitudinal velocity planning for highly automated vehicle

NASA Astrophysics Data System (ADS)

Cao, Haotian; Song, Xiaolin; Zhao, Song; Bao, Shan; Huang, Zhi

2017-08-01

Automated driving has received a broad of attentions from the academia and industry, since it is effective to greatly reduce the severity of potential traffic accidents and achieve the ultimate automobile safety and comfort. This paper presents an optimal model-based trajectory following architecture for highly automated vehicle in its driving tasks such as automated guidance or lane keeping, which includes a velocity-planning module, a steering controller and a velocity-tracking controller. The velocity-planning module considering the optimal time-consuming and passenger comforts simultaneously could generate a smooth velocity profile. The robust sliding mode control (SMC) steering controller with adaptive preview time strategy could not only track the target path well, but also avoid a big lateral acceleration occurred in its path-tracking progress due to a fuzzy-adaptive preview time mechanism introduced. In addition, an SMC controller with input-output linearisation method for velocity tracking is built and validated. Simulation results show this trajectory following architecture are effective and feasible for high automated driving vehicle, comparing with the Driver-in-the-Loop simulations performed by an experienced driver and novice driver, respectively. The simulation results demonstrate that the present trajectory following architecture could plan a satisfying longitudinal speed profile, track the target path well and safely when dealing with different road geometry structure, it ensures a good time efficiency and driving comfort simultaneously.

Efficient retrieval of landscape Hessian: Forced optimal covariance adaptive learning

NASA Astrophysics Data System (ADS)

Shir, Ofer M.; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel

2014-06-01

Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (≳30) with large Hessian condition numbers (≳104). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes.

Robust Adaptive Modified Newton Algorithm for Generalized Eigendecomposition and Its Application

NASA Astrophysics Data System (ADS)

Yang, Jian; Yang, Feng; Xi, Hong-Sheng; Guo, Wei; Sheng, Yanmin

2007-12-01

We propose a robust adaptive algorithm for generalized eigendecomposition problems that arise in modern signal processing applications. To that extent, the generalized eigendecomposition problem is reinterpreted as an unconstrained nonlinear optimization problem. Starting from the proposed cost function and making use of an approximation of the Hessian matrix, a robust modified Newton algorithm is derived. A rigorous analysis of its convergence properties is presented by using stochastic approximation theory. We also apply this theory to solve the signal reception problem of multicarrier DS-CDMA to illustrate its practical application. The simulation results show that the proposed algorithm has fast convergence and excellent tracking capability, which are important in a practical time-varying communication environment.

Combined Training Enhances Skeletal Muscle Mitochondrial Oxidative Capacity Independent of Age

PubMed Central

Lanza, Ian R.; Henderson, Gregory C.; Rao, Rajesh R.; Spiegelman, Bruce M.

2015-01-01

Context: Skeletal muscle from sedentary older adults exhibits reduced mitochondrial abundance and oxidative capacity. Objective: The primary objective was to determine whether 8 weeks of combined training (CT) has a more robust effect than endurance training (ET) or resistance training (RT) on mitochondrial physiology in healthy young (18–30 years) and older (≥65 years) adults. Intervention: Thirty-four young and 31 older adults were randomly assigned to 8 weeks of ET, RT, and control/CT. Control subjects completed 8 weeks of no exercise (control) followed by 8 weeks of CT. Body composition, skeletal muscle strength, and peak oxygen uptake were measured before and after the intervention. Vastus lateralis muscle biopsy samples were obtained before and 48 hours after the intervention. Mitochondrial physiology was evaluated by high-resolution respirometry and expression of mitochondrial proteins and transcription factors by quantitative PCR and immunoblotting. Results: ET and CT significantly increased oxidative capacity and expression of mitochondrial proteins and transcription factors. All training modalities improved body composition, cardiorespiratory fitness, and skeletal muscle strength. CT induced the most robust improvements in mitochondria-related outcomes and physical characteristics despite lower training volumes for the ET and RT components. Importantly, most of the adaptations to training occurred independent of age. Conclusion: Collectively, these results demonstrate that both ET and CT increase muscle mitochondrial abundance and capacity although CT induced the most robust improvements in the outcomes measured. In conclusion, CT provides a robust exercise regimen to improve muscle mitochondrial outcomes and physical characteristics independent of age. PMID:25599385

Robust and fast-converging level set method for side-scan sonar image segmentation

NASA Astrophysics Data System (ADS)

Liu, Yan; Li, Qingwu; Huo, Guanying

2017-11-01

A robust and fast-converging level set method is proposed for side-scan sonar (SSS) image segmentation. First, the noise in each sonar image is removed using the adaptive nonlinear complex diffusion filter. Second, k-means clustering is used to obtain the initial presegmentation image from the denoised image, and then the distance maps of the initial contours are reinitialized to guarantee the accuracy of the numerical calculation used in the level set evolution. Finally, the satisfactory segmentation is achieved using a robust variational level set model, where the evolution control parameters are generated by the presegmentation. The proposed method is successfully applied to both synthetic image with speckle noise and real SSS images. Experimental results show that the proposed method needs much less iteration and therefore is much faster than the fuzzy local information c-means clustering method, the level set method using a gamma observation model, and the enhanced region-scalable fitting method. Moreover, the proposed method can usually obtain more accurate segmentation results compared with other methods.

Artificial Immune System Approaches for Aerospace Applications

NASA Technical Reports Server (NTRS)

KrishnaKumar, Kalmanje; Koga, Dennis (Technical Monitor)

2002-01-01

Artificial Immune Systems (AIS) combine a priori knowledge with the adapting capabilities of biological immune system to provide a powerful alternative to currently available techniques for pattern recognition, modeling, design, and control. Immunology is the science of built-in defense mechanisms that are present in all living beings to protect against external attacks. A biological immune system can be thought of as a robust, adaptive system that is capable of dealing with an enormous variety of disturbances and uncertainties. Biological immune systems use a finite number of discrete "building blocks" to achieve this adaptiveness. These building blocks can be thought of as pieces of a puzzle which must be put together in a specific way-to neutralize, remove, or destroy each unique disturbance the system encounters. In this paper, we outline AIS models that are immediately applicable to aerospace problems and identify application areas that need further investigation.

Improved Convergence and Robustness of USM3D Solutions on Mixed-Element Grids

NASA Technical Reports Server (NTRS)

Pandya, Mohagna J.; Diskin, Boris; Thomas, James L.; Frink, Neal T.

2016-01-01

Several improvements to the mixed-element USM3D discretization and defect-correction schemes have been made. A new methodology for nonlinear iterations, called the Hierarchical Adaptive Nonlinear Iteration Method, has been developed and implemented. The Hierarchical Adaptive Nonlinear Iteration Method provides two additional hierarchies around a simple and approximate preconditioner of USM3D. The hierarchies are a matrix-free linear solver for the exact linearization of Reynolds-averaged Navier-Stokes equations and a nonlinear control of the solution update. Two variants of the Hierarchical Adaptive Nonlinear Iteration Method are assessed on four benchmark cases, namely, a zero-pressure-gradient flat plate, a bump-in-channel configuration, the NACA 0012 airfoil, and a NASA Common Research Model configuration. The new methodology provides a convergence acceleration factor of 1.4 to 13 over the preconditioner-alone method representing the baseline solver technology.

Improved Convergence and Robustness of USM3D Solutions on Mixed-Element Grids

NASA Technical Reports Server (NTRS)

Pandya, Mohagna J.; Diskin, Boris; Thomas, James L.; Frinks, Neal T.

2016-01-01

Several improvements to the mixed-elementUSM3Ddiscretization and defect-correction schemes have been made. A new methodology for nonlinear iterations, called the Hierarchical Adaptive Nonlinear Iteration Method, has been developed and implemented. The Hierarchical Adaptive Nonlinear Iteration Method provides two additional hierarchies around a simple and approximate preconditioner of USM3D. The hierarchies are a matrix-free linear solver for the exact linearization of Reynolds-averaged Navier-Stokes equations and a nonlinear control of the solution update. Two variants of the Hierarchical Adaptive Nonlinear Iteration Method are assessed on four benchmark cases, namely, a zero-pressure-gradient flat plate, a bump-in-channel configuration, the NACA 0012 airfoil, and a NASA Common Research Model configuration. The new methodology provides a convergence acceleration factor of 1.4 to 13 over the preconditioner-alone method representing the baseline solver technology.

Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault tolerant control

NASA Astrophysics Data System (ADS)

Habibi, Hamed; Rahimi Nohooji, Hamed; Howard, Ian

2017-09-01

Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method.

Cortex Inspired Model for Inverse Kinematics Computation for a Humanoid Robotic Finger

PubMed Central

Gentili, Rodolphe J.; Oh, Hyuk; Molina, Javier; Reggia, James A.; Contreras-Vidal, José L.

2013-01-01

In order to approach human hand performance levels, artificial anthropomorphic hands/fingers have increasingly incorporated human biomechanical features. However, the performance of finger reaching movements to visual targets involving the complex kinematics of multi-jointed, anthropomorphic actuators is a difficult problem. This is because the relationship between sensory and motor coordinates is highly nonlinear, and also often includes mechanical coupling of the two last joints. Recently, we developed a cortical model that learns the inverse kinematics of a simulated anthropomorphic finger. Here, we expand this previous work by assessing if this cortical model is able to learn the inverse kinematics for an actual anthropomorphic humanoid finger having its two last joints coupled and controlled by pneumatic muscles. The findings revealed that single 3D reaching movements, as well as more complex patterns of motion of the humanoid finger, were accurately and robustly performed by this cortical model while producing kinematics comparable to those of humans. This work contributes to the development of a bioinspired controller providing adaptive, robust and flexible control of dexterous robotic and prosthetic hands. PMID:23366569

Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration.

PubMed

Siu, Ho Chit; Arenas, Ana M; Sun, Tingxiao; Stirling, Leia A

2018-02-05

Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue.

Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration

PubMed Central

Arenas, Ana M.; Sun, Tingxiao

2018-01-01

Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue. PMID:29401754

Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

PubMed

Pohlmeyer, Eric A; Mahmoudi, Babak; Geng, Shijia; Prins, Noeline; Sanchez, Justin C

2012-01-01

Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.

Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer.

PubMed

He, Wei; Yan, Zichen; Sun, Changyin; Chen, Yunan

2017-10-01

The research of this paper works out the attitude and position control of the flapping wing micro aerial vehicle (FWMAV). Neural network control with full state and output feedback are designed to deal with uncertainties in this complex nonlinear FWMAV dynamic system and enhance the system robustness. Meanwhile, we design disturbance observers which are exerted into the FWMAV system via feedforward loops to counteract the bad influence of disturbances. Then, a Lyapunov function is proposed to prove the closed-loop system stability and the semi-global uniform ultimate boundedness of all state variables. Finally, a series of simulation results indicate that proposed controllers can track desired trajectories well via selecting appropriate control gains. And the designed controllers possess potential applications in FWMAVs.

Multiple model self-tuning control for a class of nonlinear systems

NASA Astrophysics Data System (ADS)

Huang, Miao; Wang, Xin; Wang, Zhenlei

2015-10-01

This study develops a novel nonlinear multiple model self-tuning control method for a class of nonlinear discrete-time systems. An increment system model and a modified robust adaptive law are proposed to expand the application range, thus eliminating the assumption that either the nonlinear term of the nonlinear system or its differential term is global-bounded. The nonlinear self-tuning control method can address the situation wherein the nonlinear system is not subject to a globally uniformly asymptotically stable zero dynamics by incorporating the pole-placement scheme. A novel, nonlinear control structure based on this scheme is presented to improve control precision. Stability and convergence can be confirmed when the proposed multiple model self-tuning control method is applied. Furthermore, simulation results demonstrate the effectiveness of the proposed method.

Adaptive transmission disequilibrium test for family trio design.

PubMed

Yuan, Min; Tian, Xin; Zheng, Gang; Yang, Yaning

2009-01-01

The transmission disequilibrium test (TDT) is a standard method to detect association using family trio design. It is optimal for an additive genetic model. Other TDT-type tests optimal for recessive and dominant models have also been developed. Association tests using family data, including the TDT-type statistics, have been unified to a class of more comprehensive and flexable family-based association tests (FBAT). TDT-type tests have high efficiency when the genetic model is known or correctly specified, but may lose power if the model is mis-specified. Hence tests that are robust to genetic model mis-specification yet efficient are preferred. Constrained likelihood ratio test (CLRT) and MAX-type test have been shown to be efficiency robust. In this paper we propose a new efficiency robust procedure, referred to as adaptive TDT (aTDT). It uses the Hardy-Weinberg disequilibrium coefficient to identify the potential genetic model underlying the data and then applies the TDT-type test (or FBAT for general applications) corresponding to the selected model. Simulation demonstrates that aTDT is efficiency robust to model mis-specifications and generally outperforms the MAX test and CLRT in terms of power. We also show that aTDT has power close to, but much more robust, than the optimal TDT-type test based on a single genetic model. Applications to real and simulated data from Genetic Analysis Workshop (GAW) illustrate the use of our adaptive TDT.

Active vibration suppression of self-excited structures using an adaptive LMS algorithm

NASA Astrophysics Data System (ADS)

Danda Roy, Indranil

The purpose of this investigation is to study the feasibility of an adaptive feedforward controller for active flutter suppression in representative linear wing models. The ability of the controller to suppress limit-cycle oscillations in wing models having root springs with freeplay nonlinearities has also been studied. For the purposes of numerical simulation, mathematical models of a rigid and a flexible wing structure have been developed. The rigid wing model is represented by a simple three-degree-of-freedom airfoil while the flexible wing is modelled by a multi-degree-of-freedom finite element representation with beam elements for bending and rod elements for torsion. Control action is provided by one or more flaps attached to the trailing edge and extending along the entire wing span for the rigid model and a fraction of the wing span for the flexible model. Both two-dimensional quasi-steady aerodynamics and time-domain unsteady aerodynamics have been used to generate the airforces in the wing models. An adaptive feedforward controller has been designed based on the filtered-X Least Mean Squares (LMS) algorithm. The control configuration for the rigid wing model is single-input single-output (SISO) while both SISO and multi-input multi-output (MIMO) configurations have been applied on the flexible wing model. The controller includes an on-line adaptive system identification scheme which provides the LMS controller with a reasonably accurate model of the plant. This enables the adaptive controller to track time-varying parameters in the plant and provide effective control. The wing models in closed-loop exhibit highly damped responses at airspeeds where the open-loop responses are destructive. Simulations with the rigid and the flexible wing models in a time-varying airstream show a 63% and 53% increase, respectively, over their corresponding open-loop flutter airspeeds. The ability of the LMS controller to suppress wing store flutter in the two models has also been investigated. With 10% measurement noise introduced in the flexible wing model, the controller demonstrated good robustness to the extraneous disturbances. In the examples studied it is found that adaptation is rapid enough to successfully control flutter at accelerations in the airstream of up to 15 ft/sec2 for the rigid wing model and 9 ft/sec2 for the flexible wing model.

Robust lane detection and tracking using multiple visual cues under stochastic lane shape conditions

NASA Astrophysics Data System (ADS)

Huang, Zhi; Fan, Baozheng; Song, Xiaolin

2018-03-01

As one of the essential components of environment perception techniques for an intelligent vehicle, lane detection is confronted with challenges including robustness against the complicated disturbance and illumination, also adaptability to stochastic lane shapes. To overcome these issues, we proposed a robust lane detection method named classification-generation-growth-based (CGG) operator to the detected lines, whereby the linear lane markings are identified by synergizing multiple visual cues with the a priori knowledge and spatial-temporal information. According to the quality of linear lane fitting, the linear and linear-parabolic models are dynamically switched to describe the actual lane. The Kalman filter with adaptive noise covariance and the region of interests (ROI) tracking are applied to improve the robustness and efficiency. Experiments were conducted with images covering various challenging scenarios. The experimental results evaluate the effectiveness of the presented method for complicated disturbances, illumination, and stochastic lane shapes.

Bio-inspired control of joint torque and knee stiffness in a robotic lower limb exoskeleton using a central pattern generator.

PubMed

Schrade, Stefan O; Nager, Yannik; Wu, Amy R; Gassert, Roger; Ijspeert, Auke

2017-07-01

Robotic lower limb exoskeletons are becoming increasingly popular in therapy and recreational use. However, most exoskeletons are still rather limited in their locomotion speed and the activities of daily live they can perform. Furthermore, they typically do not allow for a dynamic adaptation to the environment, as they are often controlled with predefined reference trajectories. Inspired by human leg stiffness modulation during walking, variable stiffness actuators increase flexibility without the need for more complex controllers. Actuation with adaptable stiffness is inspired by the human leg stiffness modulation during walking. However, this actuation principle also introduces the stiffness setpoint as an additional degree of freedom that needs to be coordinated with the joint trajectories. As a potential solution to this issue a bio-inspired controller based on a central pattern generator (CPG) is presented in this work. It generates coordinated joint torques and knee stiffness modulations to produce flexible and dynamic gait patterns for an exoskeleton with variable knee stiffness actuation. The CPG controller is evaluated and optimized in simulation using a model of the exoskeleton. The CPG controller produced stable and smooth gait for walking speeds from 0.4 m/s up to 1.57 m/s with a torso stabilizing force that simulated the use of crutches, which are commonly needed by exoskeleton users. Through the CPG, the knee stiffness intrinsically adapted to the frequency and phase of the gait, when the speed was changed. Additionally, it adjusted to changes in the environment in the form of uneven terrain by reacting to ground contact forces. This could allow future exoskeletons to be more adaptive to various environments, thus making ambulation more robust.

A model for simulating adaptive, dynamic flows on networks: Application to petroleum infrastructure

DOE PAGES

Corbet, Thomas F.; Beyeler, Walt; Wilson, Michael L.; ...

2017-10-03

Simulation models can greatly improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, thenmore » on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.« less

A model for simulating adaptive, dynamic flows on networks: Application to petroleum infrastructure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Corbet, Thomas F.; Beyeler, Walt; Wilson, Michael L.

Simulation models can greatly improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, thenmore » on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.« less

Adaptive piezoelectric sensoriactuators for active structural acoustic control

NASA Astrophysics Data System (ADS)

Vipperman, Jeffrey Stuart

1997-09-01

A new transducer technology with application to active control systems, modal analysis, and autonomous system health monitoring, is brought to fruition in this work. It has the advantages of being lightweight, potentially cost-effective, self-tuning, has negligible dynamics, and most importantly (from a robustness perspective), it provides a colocated sensor/actuator pair. The transducer consists of a piezoceramic element which serves as both an actuator and a sensor and will be referred to in this work as a sensoriactuator. Simple, adaptive signal processing in conjunction with a voltage controlled amplifier, reference capacitor, and a common-mode rejection circuit extract the mechanical response from the total response of the piezoelectric sensoriactuator for sensing. The digital portion of the adaptive piezoelectric sensoriactuator merely serves to tune the circuit, avoiding the potentially destabilizing effects of introducing a digital delay in the signal path, when used for feedback control applications. Adaptive compensation of the sensoriactuator is necessary since the signal to noise ratio is typically greater than 40 dB, making it prohibitive to tune the circuit manually. In addition, the constitutive properties of piezoceramics vary with time and environment, necessitating that the circuit be periodically re-tuned. The analog portion of the hardware is based upon op-amp circuits and an AD632 analog multiplier chip, which serves as both a voltage controlled amplifier (VCA) and a common mode rejection (CMR) circuit. A single coefficient least-mean square (LMS) adaptive filter continuously adjusts the gain of the VCA circuit as necessary. Nonideal behavior of piezoceramics is discussed along with methods to counter the consequential deterioration in circuit performance. A multiple input multiple output (MIMO) implementation of the adaptive piezoelectric sensoriactuator is developed using orthogonal white noise training signals for each sensoriactuator. Two piezostructures were used to demonstrate and verify the adaptive piezoelectric sensoriactuator, a cantilevered beam and a simply-supported plate. The experimental open- loop results compare well with theory. A preliminary closed-loop rate controller applied to the cantilevered beam demonstrates simultaneous control and adaptation of the piezoelectric sensoriactuator. Lastly, [/cal H]2 optimal feedback Active Structural Acoustic Control (ASAC) is demonstrated using the adaptive piezoelectric sensoriactuators and the simply- supported plate test bed. A cost function is formulated based upon control effort and predicted radiated acoustic power. Radiation filters are created to predict acoustic power based on the self and mutual radiation efficiencies of the plate modes to be controlled. Both static output feedback and state-feedback compensation as well as dynamic (Linear Quadratic Gaussian) compensation are investigated and compared analytically. The importance of choosing an appropriate spatial aperture for the piezoceramic transducer for static compensation is discussed. Finally, multivariable Active Vibration Control (AVC) and ASAC are implemented experimentally on a simply-supported plate test bed using an array of four Adaptive Piezoelectric Sensoriactuators as the control sensors and actuators. Unfavorable high-frequency response from the given piezoceramic transducers required that dynamic, Linear Quadratic Gaussian (LQG) compensation be used to achieve good control performance.

Robust H(infinity) tracking control of boiler-turbine systems.

PubMed

Wu, J; Nguang, S K; Shen, J; Liu, G; Li, Y G

2010-07-01

In this paper, the problem of designing a fuzzy H(infinity) state feedback tracking control of a boiler-turbine is solved. First, the Takagi and Sugeno fuzzy model is used to model a boiler-turbine system. Next, based on the Takagi and Sugeno fuzzy model, sufficient conditions for the existence of a fuzzy H(infinity) nonlinear state feedback tracking control are derived in terms of linear matrix inequalities. The advantage of the proposed tracking control design is that it does not involve feedback linearization technique and complicated adaptive scheme. An industrial boiler-turbine system is used to illustrate the effectiveness of the proposed design as compared with a linearized approach. 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Integral sliding mode-based attitude coordinated tracking for spacecraft formation with communication delays

NASA Astrophysics Data System (ADS)

Zhang, Jian; Hu, Qinglei; Xie, Wenbo

2017-11-01

This paper investigates the attitude coordinated tracking control for a group of rigid spacecraft under directed communication topology, in which inertia uncertainties, external disturbances, input saturation and constant time-delays between the formation members are handled. Initially, the nominal system with communication delays is studied. A delay-dependent controller is proposed by using Lyapunov-Krasovskii function and sufficient condition for system stability is derived. Then, an integral sliding manifold is designed and adaptive control approach is employed to deal with the total perturbation. Meanwhile, the boundary layer method is introduced to alleviate the unexpected chattering as system trajectories cross the switching surface. Finally, numerical simulation results are presented to validate the effectiveness and robustness of the proposed control strategy.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Preston, Benjamin L.; Mustelin, Johanna; Maloney, Megan C.

The adaptation science enterprise has expanded rapidly in recent years, presumably in response to growth in demand for knowledge that can facilitate adaptation policy and practice. However, evidence suggests such investments in adaptation science have not necessarily translated into adaptation implementation. One potential constraint on adaptation may be the underlying heuristics that are used as the foundation for both adaptation research and practice. In this paper, we explore the adaptation academic literature with the objective of identifying adaptation heuristics, assessing the extent to which they have become entrenched within the adaptation discourse, and discussing potential weaknesses in their framing thatmore » could undermine adaptation efforts. This investigation is supported by a multi-method analysis that includes both a quantitative content analysis of the adaptation literature that evidences the use of adaptation heuristics and a qualitative analysis of the implications of such heuristics for enhancing or hindering the implementation of adaptation. Results demonstrate that a number of heuristic devices are commonly used in both the peer-reviewed adaptation literature as well as within grey literature designed to inform adaptation practitioners. Furthermore, the apparent lack of critical reflection upon the robustness of these heuristics for diverse contexts may contribute to potential cognitive bias with respect to the framing of adaptation by both researchers and practitioners. Finally, we discuss this phenomenon by drawing upon heuristic-analytic theory, which has explanatory utility in understanding both the origins of such heuristics as well as the measures that can be pursued toward the co-generation of more robust approaches to adaptation problem-solving.« less

Application of stakeholder-based and modelling approaches for supporting robust adaptation decision making under future climatic uncertainty and changing urban-agricultural water demand

NASA Astrophysics Data System (ADS)

Bhave, Ajay; Dessai, Suraje; Conway, Declan; Stainforth, David

2016-04-01

Deep uncertainty in future climate change and socio-economic conditions necessitates the use of assess-risk-of-policy approaches over predict-then-act approaches for adaptation decision making. Robust Decision Making (RDM) approaches embody this principle and help evaluate the ability of adaptation options to satisfy stakeholder preferences under wide-ranging future conditions. This study involves the simultaneous application of two RDM approaches; qualitative and quantitative, in the Cauvery River Basin in Karnataka (population ~23 million), India. The study aims to (a) determine robust water resources adaptation options for the 2030s and 2050s and (b) compare the usefulness of a qualitative stakeholder-driven approach with a quantitative modelling approach. For developing a large set of future scenarios a combination of climate narratives and socio-economic narratives was used. Using structured expert elicitation with a group of climate experts in the Indian Summer Monsoon, climatic narratives were developed. Socio-economic narratives were developed to reflect potential future urban and agricultural water demand. In the qualitative RDM approach, a stakeholder workshop helped elicit key vulnerabilities, water resources adaptation options and performance criteria for evaluating options. During a second workshop, stakeholders discussed and evaluated adaptation options against the performance criteria for a large number of scenarios of climatic and socio-economic change in the basin. In the quantitative RDM approach, a Water Evaluation And Planning (WEAP) model was forced by precipitation and evapotranspiration data, coherent with the climatic narratives, together with water demand data based on socio-economic narratives. We find that compared to business-as-usual conditions options addressing urban water demand satisfy performance criteria across scenarios and provide co-benefits like energy savings and reduction in groundwater depletion, while options reducing agricultural water demand significantly affect downstream water availability. Water demand options demonstrate potential to improve environmental flow conditions and satisfy legal water supply requirements for downstream riparian states. On the other hand, currently planned large scale infrastructural projects demonstrate reduced value in certain scenarios, illustrating the impacts of lock-in effects of large scale infrastructure. From a methodological perspective, we find that while the stakeholder-driven approach revealed robust options in a resource-light manner and helped initiate much needed interaction amongst stakeholders, the modelling approach provides complementary quantitative information. The study reveals robust adaptation options for this important basin and provides a strong methodological basis for carrying out future studies that support adaptation decision making.

Human Guidance Behavior Decomposition and Modeling

NASA Astrophysics Data System (ADS)

Feit, Andrew James

Trained humans are capable of high performance, adaptable, and robust first-person dynamic motion guidance behavior. This behavior is exhibited in a wide variety of activities such as driving, piloting aircraft, skiing, biking, and many others. Human performance in such activities far exceeds the current capability of autonomous systems in terms of adaptability to new tasks, real-time motion planning, robustness, and trading safety for performance. The present work investigates the structure of human dynamic motion guidance that enables these performance qualities. This work uses a first-person experimental framework that presents a driving task to the subject, measuring control inputs, vehicle motion, and operator visual gaze movement. The resulting data is decomposed into subspace segment clusters that form primitive elements of action-perception interactive behavior. Subspace clusters are defined by both agent-environment system dynamic constraints and operator control strategies. A key contribution of this work is to define transitions between subspace cluster segments, or subgoals, as points where the set of active constraints, either system or operator defined, changes. This definition provides necessary conditions to determine transition points for a given task-environment scenario that allow a solution trajectory to be planned from known behavior elements. In addition, human gaze behavior during this task contains predictive behavior elements, indicating that the identified control modes are internally modeled. Based on these ideas, a generative, autonomous guidance framework is introduced that efficiently generates optimal dynamic motion behavior in new tasks. The new subgoal planning algorithm is shown to generate solutions to certain tasks more quickly than existing approaches currently used in robotics.

Generation and protective efficacy of a cold-adapted attenuated avian H9N2 influenza vaccine.

PubMed

Wei, Yandi; Qi, Lu; Gao, Huijie; Sun, Honglei; Pu, Juan; Sun, Yipeng; Liu, Jinhua

2016-07-26

To prevent H9N2 avian influenza virus infection in chickens, a long-term vaccination program using inactivated vaccines has been implemented in China. However, the protective efficacy of inactivated vaccines against antigenic drift variants is limited, and H9N2 influenza virus continues to circulate in vaccinated chicken flocks in China. Therefore, developing a cross-reactive vaccine to control the impact of H9N2 influenza in the poultry industry remains a high priority. In the present study, we developed a live cold-adapted H9N2 influenza vaccine candidate (SD/01/10-ca) by serial passages in embryonated eggs at successively lower temperatures. A total of 13 amino acid mutations occurred during the cold-adaptation of this H9N2 virus. The candidate was safe in chickens and induced robust hemagglutination-inhibition antibody responses and influenza virus-specific CD4(+) and CD8(+) T cell immune responses in chickens immunized intranasally. Importantly, the candidate could confer protection of chickens from homologous and heterogenous H9N2 viruses. These results demonstrated that the cold-adapted attenuated H9N2 virus would be selected as a vaccine to control the infection of prevalent H9N2 influenza viruses in chickens.