Adaptive Control Strategies for Flexible Robotic Arm

NASA Technical Reports Server (NTRS)

Bialasiewicz, Jan T.

1996-01-01

The control problem of a flexible robotic arm has been investigated. The control strategies that have been developed have a wide application in approaching the general control problem of flexible space structures. The following control strategies have been developed and evaluated: neural self-tuning control algorithm, neural-network-based fuzzy logic control algorithm, and adaptive pole assignment algorithm. All of the above algorithms have been tested through computer simulation. In addition, the hardware implementation of a computer control system that controls the tip position of a flexible arm clamped on a rigid hub mounted directly on the vertical shaft of a dc motor, has been developed. An adaptive pole assignment algorithm has been applied to suppress vibrations of the described physical model of flexible robotic arm and has been successfully tested using this testbed.

Telerobotic control of a mobile coordinated robotic server, executive summary

NASA Technical Reports Server (NTRS)

Lee, Gordon

1993-01-01

This interim report continues with the research effort on advanced adaptive controls for space robotics systems. In particular, previous results developed by the principle investigator and his research team centered around fuzzy logic control (FLC) in which the lack of knowledge of the robotic system as well as the uncertainties of the environment are compensated for by a rule base structure which interacts with varying degrees of belief of control action using system measurements. An on-line adaptive algorithm was developed using a single parameter tuning scheme. In the effort presented, the methodology is further developed to include on-line scaling factor tuning and self-learning control as well as extended to the multi-input, multi-output (MIMO) case. Classical fuzzy logic control requires tuning input scale factors off-line through trial and error techniques. This is time-consuming and cannot adapt to new changes in the process. The new adaptive FLC includes a self-tuning scheme for choosing the scaling factors on-line. Further the rule base in classical FLC is usually produced by soliciting knowledge from human operators as to what is good control action for given circumstances. This usually requires full knowledge and experience of the process and operating conditions, which limits applicability. A self-learning scheme is developed which adaptively forms the rule base with very limited knowledge of the process. Finally, a MIMO method is presented employing optimization techniques. This is required for application to space robotics in which several degrees-of-freedom links are commonly used. Simulation examples are presented for terminal control - typical of robotic problems in which a desired terminal point is to be reached for each link. Future activities will be to implement the MIMO adaptive FLC on an INTEL microcontroller-based circuit and to test the algorithm on a robotic system at the Mars Mission Research Center at North Carolina State University.

Application of genetic algorithms to tuning fuzzy control systems

NASA Technical Reports Server (NTRS)

Espy, Todd; Vombrack, Endre; Aldridge, Jack

1993-01-01

Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.

Intelligent Systems for Stabilizing Mode-Locked Lasers and Frequency Combs: Machine Learning and Equation-Free Control Paradigms for Self-Tuning Optics

NASA Astrophysics Data System (ADS)

Kutz, J. Nathan; Brunton, Steven L.

2015-12-01

We demonstrate that a software architecture using innovations in machine learning and adaptive control provides an ideal integration platform for self-tuning optics. For mode-locked lasers, commercially available optical telecom components can be integrated with servocontrollers to enact a training and execution software module capable of self-tuning the laser cavity even in the presence of mechanical and/or environmental perturbations, thus potentially stabilizing a frequency comb. The algorithm training stage uses an exhaustive search of parameter space to discover best regions of performance for one or more objective functions of interest. The execution stage first uses a sparse sensing procedure to recognize the parameter space before quickly moving to the near optimal solution and maintaining it using the extremum seeking control protocol. The method is robust and equationfree, thus requiring no detailed or quantitatively accurate model of the physics. It can also be executed on a broad range of problems provided only that suitable objective functions can be found and experimentally measured.

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.

PID controller tuning using metaheuristic optimization algorithms for benchmark problems

NASA Astrophysics Data System (ADS)

Gholap, Vishal; Naik Dessai, Chaitali; Bagyaveereswaran, V.

2017-11-01

This paper contributes to find the optimal PID controller parameters using particle swarm optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The algorithms were developed through simulation of chemical process and electrical system and the PID controller is tuned. Here, two different fitness functions such as Integral Time Absolute Error and Time domain Specifications were chosen and applied on PSO, GA and SA while tuning the controller. The proposed Algorithms are implemented on two benchmark problems of coupled tank system and DC motor. Finally, comparative study has been done with different algorithms based on best cost, number of iterations and different objective functions. The closed loop process response for each set of tuned parameters is plotted for each system with each fitness function.

Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories

NASA Technical Reports Server (NTRS)

Burchett, Bradley T.

2003-01-01

The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.

Experiments on Adaptive Self-Tuning of Seismic Signal Detector Parameters

NASA Astrophysics Data System (ADS)

Knox, H. A.; Draelos, T.; Young, C. J.; Chael, E. P.; Peterson, M. G.; Lawry, B.; Phillips-Alonge, K. E.; Balch, R. S.; Ziegler, A.

2016-12-01

Scientific applications, including underground nuclear test monitoring and microseismic monitoring can benefit enormously from data-driven dynamic algorithms for tuning seismic and infrasound signal detection parameters since continuous streams are producing waveform archives on the order of 1TB per month. Tuning is a challenge because there are a large number of data processing parameters that interact in complex ways, and because the underlying populating of true signal detections is generally unknown. The largely manual process of identifying effective parameters, often performed only over a subset of stations over a short time period, is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. We present improvements to an Adaptive Self-Tuning algorithm for continuously adjusting detection parameters based on consistency with neighboring sensors. Results are shown for 1) data from a very dense network ( 120 stations, 10 km radius) deployed during 2008 on Erebus Volcano, Antarctica, and 2) data from a continuous downhole seismic array in the Farnsworth Field, an oil field in Northern Texas that hosts an ongoing carbon capture, utilization, and storage project. Performance is assessed in terms of missed detections and false detections relative to human analyst detections, simulated waveforms where ground-truth detections exist and visual inspection.

Self-tuning regulators for multicyclic control of helicopter vibration

NASA Technical Reports Server (NTRS)

Johnson, W.

1982-01-01

A class of algorithms for the multicyclic control of helicopter vibration and loads is derived and discussed. This class is characterized by a linear, quasi-static, frequency-domain model of the helicopter response to control; identification of the helicopter model by least-squared-error or Kalman filter methods; and a minimum variance or quadratic performance function controller. Previous research on such controllers is reviewed. The derivations and discussions cover the helicopter model; the identification problem, including both off-line and on-line (recursive) algorithms; the control problem, including both open-loop and closed-loop feedback; and the various regulator configurations possible within this class. Conclusions from analysis and numerical simulations of the regulators provide guidance in the design and selection of algorithms for further development, including wind tunnel and flight tests.

Tuning and performance evaluation of PID controller for superheater steam temperature control of 200 MW boiler using gain phase assignment algorithm

NASA Astrophysics Data System (ADS)

Begum, A. Yasmine; Gireesh, N.

2018-04-01

In superheater, steam temperature is controlled in a cascade control loop. The cascade control loop consists of PI and PID controllers. To improve the superheater steam temperature control the controller's gains in a cascade control loop has to be tuned efficiently. The mathematical model of the superheater is derived by sets of nonlinear partial differential equations. The tuning methods taken for study here are designed for delay plus first order transfer function model. Hence from the dynamical model of the superheater, a FOPTD model is derived using frequency response method. Then by using Chien-Hrones-Reswick Tuning Algorithm and Gain-Phase Assignment Algorithm optimum controller gains has been found out based on the least value of integral time weighted absolute error.

Multi-Objective Control Optimization for Greenhouse Environment Using Evolutionary Algorithms

PubMed Central

Hu, Haigen; Xu, Lihong; Wei, Ruihua; Zhu, Bingkun

2011-01-01

This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an Evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production. PMID:22163927

Application of Static Var Compensator (SVC) With PI Controller for Grid Integration of Wind Farm Using Harmony Search

NASA Astrophysics Data System (ADS)

Keshta, H. E.; Ali, A. A.; Saied, E. M.; Bendary, F. M.

2016-10-01

Large-scale integration of wind turbine generators (WTGs) may have significant impacts on power system operation with respect to system frequency and bus voltages. This paper studies the effect of Static Var Compensator (SVC) connected to wind energy conversion system (WECS) on voltage profile and the power generated from the induction generator (IG) in wind farm. Also paper presents, a dynamic reactive power compensation using Static Var Compensator (SVC) at the a point of interconnection of wind farm while static compensation (Fixed Capacitor Bank) is unable to prevent voltage collapse. Moreover, this paper shows that using advanced optimization techniques based on artificial intelligence (AI) such as Harmony Search Algorithm (HS) and Self-Adaptive Global Harmony Search Algorithm (SGHS) instead of a Conventional Control Method to tune the parameters of PI controller for SVC and pitch angle. Also paper illustrates that the performance of the system with controllers based on AI is improved under different operating conditions. MATLAB/Simulink based simulation is utilized to demonstrate the application of SVC in wind farm integration. It is also carried out to investigate the enhancement in performance of the WECS achieved with a PI Controller tuned by Harmony Search Algorithm as compared to a Conventional Control Method.

Fuzzy Logic-Based Audio Pattern Recognition

NASA Astrophysics Data System (ADS)

Malcangi, M.

2008-11-01

Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

Tuning of active vibration controllers for ACTEX by genetic algorithm

NASA Astrophysics Data System (ADS)

Kwak, Moon K.; Denoyer, Keith K.

1999-06-01

This paper is concerned with the optimal tuning of digitally programmable analog controllers on the ACTEX-1 smart structures flight experiment. The programmable controllers for each channel include a third order Strain Rate Feedback (SRF) controller, a fifth order SRF controller, a second order Positive Position Feedback (PPF) controller, and a fourth order PPF controller. Optimal manual tuning of several control parameters can be a difficult task even though the closed-loop control characteristics of each controller are well known. Hence, the automatic tuning of individual control parameters using Genetic Algorithms is proposed in this paper. The optimal control parameters of each control law are obtained by imposing a constraint on the closed-loop frequency response functions using the ACTEX mathematical model. The tuned control parameters are then uploaded to the ACTEX electronic control electronics and experiments on the active vibration control are carried out in space. The experimental results on ACTEX will be presented.

Educational Tool for Optimal Controller Tuning Using Evolutionary Strategies

ERIC Educational Resources Information Center

Carmona Morales, D.; Jimenez-Hornero, J. E.; Vazquez, F.; Morilla, F.

2012-01-01

In this paper, an optimal tuning tool is presented for control structures based on multivariable proportional-integral-derivative (PID) control, using genetic algorithms as an alternative to traditional optimization algorithms. From an educational point of view, this tool provides students with the necessary means to consolidate their knowledge on…

Tuning algorithms for fractional order internal model controllers for time delay processes

NASA Astrophysics Data System (ADS)

Muresan, Cristina I.; Dutta, Abhishek; Dulf, Eva H.; Pinar, Zehra; Maxim, Anca; Ionescu, Clara M.

2016-03-01

This paper presents two tuning algorithms for fractional-order internal model control (IMC) controllers for time delay processes. The two tuning algorithms are based on two specific closed-loop control configurations: the IMC control structure and the Smith predictor structure. In the latter, the equivalency between IMC and Smith predictor control structures is used to tune a fractional-order IMC controller as the primary controller of the Smith predictor structure. Fractional-order IMC controllers are designed in both cases in order to enhance the closed-loop performance and robustness of classical integer order IMC controllers. The tuning procedures are exemplified for both single-input-single-output as well as multivariable processes, described by first-order and second-order transfer functions with time delays. Different numerical examples are provided, including a general multivariable time delay process. Integer order IMC controllers are designed in each case, as well as fractional-order IMC controllers. The simulation results show that the proposed fractional-order IMC controller ensures an increased robustness to modelling uncertainties. Experimental results are also provided, for the design of a multivariable fractional-order IMC controller in a Smith predictor structure for a quadruple-tank system.

Self tuning control of wind-diesel power systems

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

Mufti, M.D.; Balasubramanian, R.; Tripathy, S.C.

1995-12-31

This paper proposes some effective self-tuning control strategies for isolated Wind-Diesel power generation systems. Detailed modeling and studies on both single-input single-output (SISO) as well as multi-input multi-output (MIMO) self tuning regulators, applied to a typical system, are reported. Further, the effect of introducing a Super-conducting Magnetic Energy Storage (SMES) unit on the system performance has been investigated. The MIMO self-tuning regulator controlling the hybrid system and the SMES in a coordinated manner exhibits the best performance.

Multivariable PID controller design tuning using bat algorithm for activated sludge process

NASA Astrophysics Data System (ADS)

Atikah Nor’Azlan, Nur; Asmiza Selamat, Nur; Mat Yahya, Nafrizuan

2018-04-01

The designing of a multivariable PID control for multi input multi output is being concerned with this project by applying four multivariable PID control tuning which is Davison, Penttinen-Koivo, Maciejowski and Proposed Combined method. The determination of this study is to investigate the performance of selected optimization technique to tune the parameter of MPID controller. The selected optimization technique is Bat Algorithm (BA). All the MPID-BA tuning result will be compared and analyzed. Later, the best MPID-BA will be chosen in order to determine which techniques are better based on the system performances in terms of transient response.

Seismic design of passive tuned mass damper parameters using active control algorithm

NASA Astrophysics Data System (ADS)

Chang, Chia-Ming; Shia, Syuan; Lai, Yong-An

2018-07-01

Tuned mass dampers are a widely-accepted control method to effectively reduce the vibrations of tall buildings. A tuned mass damper employs a damped harmonic oscillator with specific dynamic characteristics, thus the response of structures can be regulated by the additive dynamics. The additive dynamics are, however, similar to the feedback control system in active control. Therefore, the objective of this study is to develop a new tuned mass damper design procedure based on the active control algorithm, i.e., the H2/LQG control. This design facilitates the similarity of feedback control in the active control algorithm to determine the spring and damper in a tuned mass damper. Given a mass ratio between the damper and structure, the stiffness and damping coefficient of the tuned mass damper are derived by minimizing the response objective function of the primary structure, where the structural properties are known. Varying a single weighting in this objective function yields the optimal TMD design when the minimum peak in the displacement transfer function of the structure with the TMD is met. This study examines various objective functions as well as derives the associated equations to compute the stiffness and damping coefficient. The relationship between the primary structure and optimal tuned mass damper is parametrically studied. Performance is evaluated by exploring the h2-and h∞-norms of displacements and accelerations of the primary structure. In time-domain analysis, the damping effectiveness of the tune mass damper controlled structures is investigated under impulse excitation. Structures with the optimal tuned mass dampers are also assessed under seismic excitation. As a result, the proposed design procedure produces an effective tuned mass damper to be employed in a structure against earthquakes.

Refinement and evaluation of helicopter real-time self-adaptive active vibration controller algorithms

NASA Technical Reports Server (NTRS)

Davis, M. W.

1984-01-01

A Real-Time Self-Adaptive (RTSA) active vibration controller was used as the framework in developing a computer program for a generic controller that can be used to alleviate helicopter vibration. Based upon on-line identification of system parameters, the generic controller minimizes vibration in the fuselage by closed-loop implementation of higher harmonic control in the main rotor system. The new generic controller incorporates a set of improved algorithms that gives the capability to readily define many different configurations by selecting one of three different controller types (deterministic, cautious, and dual), one of two linear system models (local and global), and one or more of several methods of applying limits on control inputs (external and/or internal limits on higher harmonic pitch amplitude and rate). A helicopter rotor simulation analysis was used to evaluate the algorithms associated with the alternative controller types as applied to the four-bladed H-34 rotor mounted on the NASA Ames Rotor Test Apparatus (RTA) which represents the fuselage. After proper tuning all three controllers provide more effective vibration reduction and converge more quickly and smoothly with smaller control inputs than the initial RTSA controller (deterministic with external pitch-rate limiting). It is demonstrated that internal limiting of the control inputs a significantly improves the overall performance of the deterministic controller.

Induction motor speed drive improvement using fuzzy IP-self-tuning controller. A real time implementation.

PubMed

Lokriti, Abdesslam; Salhi, Issam; Doubabi, Said; Zidani, Youssef

2013-05-01

An IP-self-tuning controller tuned by a fuzzy adjustor, is proposed to improve induction machine speed control. The interest of such controller is the possibility to adjust only one gain, instead of two gains for the case of the PI-self-tuning controllers commonly used in the literature. This paper presents simulation and experimental results. These latter were obtained by practical implementation on a DSPace 1104 board of three different speed controllers (the classical IP, the fuzzy-like-PI and the IP-self-tuning), for a 1.5KW induction machine. The paper presents different tests used to compare the performances of the proposed controller to the two others in terms of computation time, tracking performances and disturbances rejection. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Continuous Firefly Algorithm for Optimal Tuning of Pid Controller in Avr System

NASA Astrophysics Data System (ADS)

Bendjeghaba, Omar

2014-01-01

This paper presents a tuning approach based on Continuous firefly algorithm (CFA) to obtain the proportional-integral- derivative (PID) controller parameters in Automatic Voltage Regulator system (AVR). In the tuning processes the CFA is iterated to reach the optimal or the near optimal of PID controller parameters when the main goal is to improve the AVR step response characteristics. Conducted simulations show the effectiveness and the efficiency of the proposed approach. Furthermore the proposed approach can improve the dynamic of the AVR system. Compared with particle swarm optimization (PSO), the new CFA tuning method has better control system performance in terms of time domain specifications and set-point tracking.

A composite self tuning strategy for fuzzy control of dynamic systems

NASA Technical Reports Server (NTRS)

Shieh, C.-Y.; Nair, Satish S.

1992-01-01

The feature of self learning makes fuzzy logic controllers attractive in control applications. This paper proposes a strategy to tune the fuzzy logic controller on-line by tuning the data base as well as the rule base. The structure of the controller is outlined and preliminary results are presented using simulation studies.

Fault tolerant control of multivariable processes using auto-tuning PID controller.

PubMed

Yu, Ding-Li; Chang, T K; Yu, Ding-Wen

2005-02-01

Fault tolerant control of dynamic processes is investigated in this paper using an auto-tuning PID controller. A fault tolerant control scheme is proposed composing an auto-tuning PID controller based on an adaptive neural network model. The model is trained online using the extended Kalman filter (EKF) algorithm to learn system post-fault dynamics. Based on this model, the PID controller adjusts its parameters to compensate the effects of the faults, so that the control performance is recovered from degradation. The auto-tuning algorithm for the PID controller is derived with the Lyapunov method and therefore, the model predicted tracking error is guaranteed to converge asymptotically. The method is applied to a simulated two-input two-output continuous stirred tank reactor (CSTR) with various faults, which demonstrate the applicability of the developed scheme to industrial processes.

Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Azmi, Nur Iffah Mohamed; Arifin Mat Piah, Kamal; Yusoff, Wan Azhar Wan; Romlay, Fadhlur Rahman Mohd

2018-03-01

Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey- Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the particle velocity limit and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposed PSO- PID with the Variable Weight Grey-Taguchi DOE and the classical Ziegler-Nichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Furthermore, the physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning method responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PID parameter by applying the PSO-PID algorithm together with the Variable Weight Grey-Taguchi DOE method as a tuning method in the hydraulic positioning system.

Comparative study of a learning fuzzy PID controller and a self-tuning controller.

PubMed

Kazemian, H B

2001-01-01

The self-organising fuzzy controller is an extension of the rule-based fuzzy controller with an additional learning capability. The self-organising fuzzy (SOF) is used as a master controller to readjust conventional PID gains at the actuator level during the system operation, copying the experience of a human operator. The application of the self-organising fuzzy PID (SOF-PID) controller to a 2-link non-linear revolute-joint robot-arm is studied using path tracking trajectories at the setpoint. For the purpose of comparison, the same experiments are repeated by using the self-tuning controller subject to the same data supplied at the setpoint. For the path tracking experiments, the output trajectories of the SOF-PID controller followed the specified path closer and smoother than the self-tuning controller.

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.

PSO algorithm enhanced with Lozi Chaotic Map - Tuning experiment

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

Pluhacek, Michal; Senkerik, Roman; Zelinka, Ivan

2015-03-10

In this paper it is investigated the effect of tuning of control parameters of the Lozi Chaotic Map employed as a chaotic pseudo-random number generator for the particle swarm optimization algorithm. Three different benchmark functions are selected from the IEEE CEC 2013 competition benchmark set. The Lozi map is extensively tuned and the performance of PSO is evaluated.

Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization.

PubMed

Odili, Julius Beneoluchi; Mohmad Kahar, Mohd Nizam; Noraziah, A

2017-01-01

In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system's gain overshoot and steady steady state errors. Using the ABO algorithm where each buffalo location in the herd is a candidate solution to the Proportional-Integral-Derivative parameters was very helpful in addressing these two areas of concern. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the ABO when compared with the performance of Genetic Algorithm PID (GA-PID), Particle-Swarm Optimization PID (PSO-PID), Ant Colony Optimization PID (ACO-PID), PID, Bacteria-Foraging Optimization PID (BFO-PID) etc makes ABO-PID a good addition to solving PID Controller tuning problems using metaheuristics.

Adaptive Identification and Control of Flow-Induced Cavity Oscillations

NASA Technical Reports Server (NTRS)

Kegerise, M. A.; Cattafesta, L. N.; Ha, C.

2002-01-01

Progress towards an adaptive self-tuning regulator (STR) for the cavity tone problem is discussed in this paper. Adaptive system identification algorithms were applied to an experimental cavity-flow tested as a prerequisite to control. In addition, a simple digital controller and a piezoelectric bimorph actuator were used to demonstrate multiple tone suppression. The control tests at Mach numbers of 0.275, 0.40, and 0.60 indicated approx. = 7dB tone reductions at multiple frequencies. Several different adaptive system identification algorithms were applied at a single freestream Mach number of 0.275. Adaptive finite-impulse response (FIR) filters of orders up to N = 100 were found to be unsuitable for modeling the cavity flow dynamics. Adaptive infinite-impulse response (IIR) filters of comparable order better captured the system dynamics. Two recursive algorithms, the least-mean square (LMS) and the recursive-least square (RLS), were utilized to update the adaptive filter coefficients. Given the sample-time requirements imposed by the cavity flow dynamics, the computational simplicity of the least mean squares (LMS) algorithm is advantageous for real-time control.

Direct cortical control of 3D neuroprosthetic devices.

PubMed

Taylor, Dawn M; Tillery, Stephen I Helms; Schwartz, Andrew B

2002-06-07

Three-dimensional (3D) movement of neuroprosthetic devices can be controlled by the activity of cortical neurons when appropriate algorithms are used to decode intended movement in real time. Previous studies assumed that neurons maintain fixed tuning properties, and the studies used subjects who were unaware of the movements predicted by their recorded units. In this study, subjects had real-time visual feedback of their brain-controlled trajectories. Cell tuning properties changed when used for brain-controlled movements. By using control algorithms that track these changes, subjects made long sequences of 3D movements using far fewer cortical units than expected. Daily practice improved movement accuracy and the directional tuning of these units.

Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning

PubMed Central

Kok, Kai Yit; Rajendran, Parvathy

2016-01-01

The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost. PMID:26943630

Design of an iterative auto-tuning algorithm for a fuzzy PID controller

NASA Astrophysics Data System (ADS)

Saeed, Bakhtiar I.; Mehrdadi, B.

2012-05-01

Since the first application of fuzzy logic in the field of control engineering, it has been extensively employed in controlling a wide range of applications. The human knowledge on controlling complex and non-linear processes can be incorporated into a controller in the form of linguistic terms. However, with the lack of analytical design study it is becoming more difficult to auto-tune controller parameters. Fuzzy logic controller has several parameters that can be adjusted, such as: membership functions, rule-base and scaling gains. Furthermore, it is not always easy to find the relation between the type of membership functions or rule-base and the controller performance. This study proposes a new systematic auto-tuning algorithm to fine tune fuzzy logic controller gains. A fuzzy PID controller is proposed and applied to several second order systems. The relationship between the closed-loop response and the controller parameters is analysed to devise an auto-tuning method. The results show that the proposed method is highly effective and produces zero overshoot with enhanced transient response. In addition, the robustness of the controller is investigated in the case of parameter changes and the results show a satisfactory performance.

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

Adaptive Self Tuning

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

Peterson, Matthew; Draelos, Timothy; Knox, Hunter

2017-05-02

The AST software includes numeric methods to 1) adjust STA/LTA signal detector trigger level (TL) values and 2) filter detections for a network of sensors. AST adapts TL values to the current state of the environment by leveraging cooperation within a neighborhood of sensors. The key metric that guides the dynamic tuning is consistency of each sensor with its nearest neighbors: TL values are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The AST algorithm adapts in near real-time to changing conditions in an attempt tomore » automatically self-tune a signal detector to identify (detect) only signals from events of interest.« less

Tuning-free controller to accurately regulate flow rates in a microfluidic network

NASA Astrophysics Data System (ADS)

Heo, Young Jin; Kang, Junsu; Kim, Min Jun; Chung, Wan Kyun

2016-03-01

We describe a control algorithm that can improve accuracy and stability of flow regulation in a microfluidic network that uses a conventional pressure pump system. The algorithm enables simultaneous and independent control of fluid flows in multiple micro-channels of a microfluidic network, but does not require any model parameters or tuning process. We investigate robustness and optimality of the proposed control algorithm and those are verified by simulations and experiments. In addition, the control algorithm is compared with a conventional PID controller to show that the proposed control algorithm resolves critical problems induced by the PID control. The capability of the control algorithm can be used not only in high-precision flow regulation in the presence of disturbance, but in some useful functions for lab-on-a-chip devices such as regulation of volumetric flow rate, interface position control of two laminar flows, valveless flow switching, droplet generation and particle manipulation. We demonstrate those functions and also suggest further potential biological applications which can be accomplished by the proposed control framework.

Tuning-free controller to accurately regulate flow rates in a microfluidic network

PubMed Central

Heo, Young Jin; Kang, Junsu; Kim, Min Jun; Chung, Wan Kyun

2016-01-01

We describe a control algorithm that can improve accuracy and stability of flow regulation in a microfluidic network that uses a conventional pressure pump system. The algorithm enables simultaneous and independent control of fluid flows in multiple micro-channels of a microfluidic network, but does not require any model parameters or tuning process. We investigate robustness and optimality of the proposed control algorithm and those are verified by simulations and experiments. In addition, the control algorithm is compared with a conventional PID controller to show that the proposed control algorithm resolves critical problems induced by the PID control. The capability of the control algorithm can be used not only in high-precision flow regulation in the presence of disturbance, but in some useful functions for lab-on-a-chip devices such as regulation of volumetric flow rate, interface position control of two laminar flows, valveless flow switching, droplet generation and particle manipulation. We demonstrate those functions and also suggest further potential biological applications which can be accomplished by the proposed control framework. PMID:26987587

State-space self-tuner for on-line adaptive control

NASA Technical Reports Server (NTRS)

Shieh, L. S.

1994-01-01

Dynamic systems, such as flight vehicles, satellites and space stations, operating in real environments, constantly face parameter and/or structural variations owing to nonlinear behavior of actuators, failure of sensors, changes in operating conditions, disturbances acting on the system, etc. In the past three decades, adaptive control has been shown to be effective in dealing with dynamic systems in the presence of parameter uncertainties, structural perturbations, random disturbances and environmental variations. Among the existing adaptive control methodologies, the state-space self-tuning control methods, initially proposed by us, are shown to be effective in designing advanced adaptive controllers for multivariable systems. In our approaches, we have embedded the standard Kalman state-estimation algorithm into an online parameter estimation algorithm. Thus, the advanced state-feedback controllers can be easily established for digital adaptive control of continuous-time stochastic multivariable systems. A state-space self-tuner for a general multivariable stochastic system has been developed and successfully applied to the space station for on-line adaptive control. Also, a technique for multistage design of an optimal momentum management controller for the space station has been developed and reported in. Moreover, we have successfully developed various digital redesign techniques which can convert a continuous-time controller to an equivalent digital controller. As a result, the expensive and unreliable continuous-time controller can be implemented using low-cost and high performance microprocessors. Recently, we have developed a new hybrid state-space self tuner using a new dual-rate sampling scheme for on-line adaptive control of continuous-time uncertain systems.

Neural Network-Based Self-Tuning PID Control for Underwater Vehicles

PubMed Central

Hernández-Alvarado, Rodrigo; García-Valdovinos, Luis Govinda; Salgado-Jiménez, Tomás; Gómez-Espinosa, Alfonso; Fonseca-Navarro, Fernando

2016-01-01

For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme. PMID:27608018

Neural Network-Based Self-Tuning PID Control for Underwater Vehicles.

PubMed

Hernández-Alvarado, Rodrigo; García-Valdovinos, Luis Govinda; Salgado-Jiménez, Tomás; Gómez-Espinosa, Alfonso; Fonseca-Navarro, Fernando

2016-09-05

For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme.

A genetic algorithms approach for altering the membership functions in fuzzy logic controllers

NASA Technical Reports Server (NTRS)

Shehadeh, Hana; Lea, Robert N.

1992-01-01

Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.

Structures vibration control via Tuned Mass Dampers using a co-evolution Coral Reefs Optimization algorithm

NASA Astrophysics Data System (ADS)

Salcedo-Sanz, S.; Camacho-Gómez, C.; Magdaleno, A.; Pereira, E.; Lorenzana, A.

2017-04-01

In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures within a single population of solutions. The proposed approach is able to solve the TMD design and location problem, by exploiting the combination of different types of searching mechanisms. This promotes a powerful evolutionary-like algorithm for optimization problems, which is shown to be very effective in this particular problem of TMDs tuning. The proposed algorithm's performance has been evaluated and compared with several reference algorithms in two building models with two and four floors, respectively.

Design of a self-tuning regulator for temperature control of a polymerization reactor.

PubMed

Vasanthi, D; Pranavamoorthy, B; Pappa, N

2012-01-01

The temperature control of a polymerization reactor described by Chylla and Haase, a control engineering benchmark problem, is used to illustrate the potential of adaptive control design by employing a self-tuning regulator concept. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. The conventional cascade control provides a robust operation, but often lacks in control performance concerning the required strict temperature tolerances. The self-tuning control concept presented in this contribution solves the problem. This design calculates a trajectory for the cooling jacket temperature in order to follow a predefined trajectory of the reactor temperature. The reaction heat and the heat transfer coefficient in the energy balance are estimated online by using an unscented Kalman filter (UKF). Two simple physically motivated relations are employed, which allow the non-delayed estimation of both quantities. Simulation results under model uncertainties show the effectiveness of the self-tuning control concept. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Programmable logic controller implementation of an auto-tuned predictive control based on minimal plant information.

PubMed

Valencia-Palomo, G; Rossiter, J A

2011-01-01

This paper makes two key contributions. First, it tackles the issue of the availability of constrained predictive control for low-level control loops. Hence, it describes how the constrained control algorithm is embedded in an industrial programmable logic controller (PLC) using the IEC 61131-3 programming standard. Second, there is a definition and implementation of a novel auto-tuned predictive controller; the key novelty is that the modelling is based on relatively crude but pragmatic plant information. Laboratory experiment tests were carried out in two bench-scale laboratory systems to prove the effectiveness of the combined algorithm and hardware solution. For completeness, the results are compared with a commercial proportional-integral-derivative (PID) controller (also embedded in the PLC) using the most up to date auto-tuning rules. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

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

Adaptive control with self-tuning for non-invasive beat-by-beat blood pressure measurement.

PubMed

Nogawa, Masamichi; Ogawa, Mitsuhiro; Yamakoshi, Takehiro; Tanaka, Shinobu; Yamakoshi, Ken-ichi

2011-01-01

Up to now, we have successfully carried out the non-invasive beat-by-beat measurement of blood pressure (BP) in the root of finger, superficial temporal and radial artery based on the volume-compensation technique with reasonable accuracy. The present study concerns with improvement of control method for this beat-by-beat BP measurement. The measurement system mainly consists of a partial pressurization cuff with a pair of LED and photo-diode for the detection of arterial blood volume, and a digital self-tuning control method. Using healthy subjects, the performance and accuracy of this system were evaluated through comparison experiments with the system using a conventional empirically tuned PID controller. The significant differences of BP measured in finger artery were not showed in systolic (SBP), p=0.52, and diastolic BP (DBP), p=0.35. With the advantage of the adaptive control with self-tuning method, which can tune the control parameters without disturbing the control system, the application area of the non-invasive beat-by-beat measurement method will be broadened.

A generalized leaky FxLMS algorithm for tuning the waterbed effect of feedback active noise control systems

NASA Astrophysics Data System (ADS)

Wu, Lifu; Qiu, Xiaojun; Guo, Yecai

2018-06-01

To tune the noise amplification in the feedback system caused by the waterbed effect effectively, an adaptive algorithm is proposed in this paper by replacing the scalar leaky factor of the leaky FxLMS algorithm with a real symmetric Toeplitz matrix. The elements in the matrix are calculated explicitly according to the noise amplification constraints, which are defined based on a simple but efficient method. Simulations in an ANC headphone application demonstrate that the proposed algorithm can adjust the frequency band of noise amplification more effectively than the FxLMS algorithm and the leaky FxLMS algorithm.

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.

Self-tuning regulator for an interacting CSTR process

NASA Astrophysics Data System (ADS)

Rajendra Mungale, Niraj; Upadhyay, Akshay; Jaganatha Pandian, B.

2017-11-01

In the paper we have laid emphasis on STR that is Self Tuning Regulator and its application for an interacting process. CSTR has a great importance in Chemical Process when we deal with controlling different parameters of a process using CSTR. Basically CSTR is used to maintain a constant liquid temperature in the process. The proposed method called self-tuning regulator, is a different scheme where process parameters are updated and the controller parameters are obtained from the solution of a design problem. The paper deals with STR and methods associated with it.

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.

Robot trajectory tracking with self-tuning predicted control

NASA Technical Reports Server (NTRS)

Cui, Xianzhong; Shin, Kang G.

1988-01-01

A controller that combines self-tuning prediction and control is proposed for robot trajectory tracking. The controller has two feedback loops: one is used to minimize the prediction error, and the other is designed to make the system output track the set point input. Because the velocity and position along the desired trajectory are given and the future output of the system is predictable, a feedforward loop can be designed for robot trajectory tracking with self-tuning predicted control (STPC). Parameters are estimated online to account for the model uncertainty and the time-varying property of the system. The authors describe the principle of STPC, analyze the system performance, and discuss the simplification of the robot dynamic equations. To demonstrate its utility and power, the controller is simulated for a Stanford arm.

An efficient hybrid approach for multiobjective optimization of water distribution systems

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.

2014-05-01

An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (Ω). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.

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.

Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules.

PubMed

Zhang, Zhen; Ma, Cheng; Zhu, Rong

2016-10-14

High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial-temporal complexity. This paper presents a multi-input multi-output (MIMO) self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional-integral-derivative (PID) neural network (FCPIDNN) and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions.

Optimizing of a high-order digital filter using PSO algorithm

NASA Astrophysics Data System (ADS)

Xu, Fuchun

2018-04-01

A self-adaptive high-order digital filter, which offers opportunity to simplify the process of tuning parameters and further improve the noise performance, is presented in this paper. The parameters of traditional digital filter are mainly tuned by complex calculation, whereas this paper presents a 5th order digital filter to obtain outstanding performance and the parameters of the proposed filter are optimized by swarm intelligent algorithm. Simulation results with respect to the proposed 5th order digital filter, SNR>122dB and the noise floor under -170dB are obtained in frequency range of [5-150Hz]. In further simulation, the robustness of the proposed 5th order digital is analyzed.

Model transformations for state-space self-tuning control of multivariable stochastic systems

NASA Technical Reports Server (NTRS)

Shieh, Leang S.; Bao, Yuan L.; Coleman, Norman P.

1988-01-01

The design of self-tuning controllers for multivariable stochastic systems is considered analytically. A long-division technique for finding the similarity transformation matrix and transforming the estimated left MFD to the right MFD is developed; the derivation is given in detail, and the procedures involved are briefly characterized.

Performance costs when emotion tunes inappropriate cognitive abilities: implications for mental resources and behavior.

PubMed

Storbeck, Justin

2012-08-01

Emotion tunes cognition, such that approach-motivated positive states promote verbal cognition, whereas withdrawal-motivated negative states promote spatial cognition (Gray, 2001). The current research examined whether self-control resources become depleted and influence subsequent behavior when emotion tunes an inappropriate cognitive tendency. In 2 experiments, either an approach-motivated positive state or a withdrawal-motivated negative state was induced, and then participants completed a verbal or a spatial working memory task creating conditions of emotion-cognition alignment (e.g., approach/verbal) or misalignment (e.g., approach/spatial). A control condition was also included. To examine behavioral costs due to depleted self-control resources, participants completed either a Stroop task (Stroop, 1935; Experiment 1) or a Black/White implicit association test (IAT; Greenwald, McGhee, & Schwartz, 1998; Experiment 2). Participants in the misalignment conditions performed worse on the Stroop task, and they were worse at controlling their implicit attitude biases on the IAT. Thus, when emotion tunes inappropriate cognitive tendencies for one's current environment, self-control resources become depleted, impairing behavioral control. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Quaternion error-based optimal control applied to pinpoint landing

NASA Astrophysics Data System (ADS)

Ghiglino, Pablo

Accurate control techniques for pinpoint planetary landing - i.e., the goal of achieving landing errors in the order of 100m for unmanned missions - is a complex problem that have been tackled in different ways in the available literature. Among other challenges, this kind of control is also affected by the well known trade-off in UAV control that for complex underlying models the control is sub-optimal, while optimal control is applied to simplifed models. The goal of this research has been the development new control algorithms that would be able to tackle these challenges and the result are two novel optimal control algorithms namely: OQTAL and HEX2OQTAL. These controllers share three key properties that are thoroughly proven and shown in this thesis; stability, accuracy and adaptability. Stability is rigorously demonstrated for both controllers. Accuracy is shown in results of comparing these novel controllers with other industry standard algorithms in several different scenarios: there is a gain in accuracy of at least 15% for each controller, and in many cases much more than that. A new tuning algorithm based on swarm heuristics optimisation was developed as well as part of this research in order to tune in an online manner the standard Proportional-Integral-Derivative (PID) controllers used for benchmarking. Finally, adaptability of these controllers can be seen as a combination of four elements: mathematical model extensibility, cost matrices tuning, reduced computation time required and finally no prior knowledge of the navigation or guidance strategies needed. Further simulations in real planetary landing trajectories has shown that these controllers have the capacity of achieving landing errors in the order of pinpoint landing requirements, making them not only very precise UAV controllers, but also potential candidates for pinpoint landing unmanned missions.

The experimental results of a self tuning adaptive controller using online frequency identification. [for Galileo spacecraft

NASA Technical Reports Server (NTRS)

Chiang, W.-W.; Cannon, R. H., Jr.

1985-01-01

A fourth-order laboratory dynamic system featuring very low structural damping and a noncolocated actuator-sensor pair has been used to test a novel real-time adaptive controller, implemented in a minicomputer, which consists of a state estimator, a set of state feedback gains, and a frequency-locked loop for real-time parameter identification. The adaptation algorithm employed can correct controller error and stabilize the system for more than 50 percent variation in the plant's natural frequency, compared with a 10 percent stability margin in frequency variation for a fixed gain controller having the same performance as the nominal plant condition. The very rapid convergence achievable by this adaptive system is demonstrated experimentally, and proven with simple, root-locus methods.

A Self-Tuning Kalman Filter for Autonomous Spacecraft Navigation

NASA Technical Reports Server (NTRS)

Truong, Son H.

1998-01-01

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman Filter and Global Positioning System (GPS) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. Current techniques of Kalman filtering, however, still rely on manual tuning from analysts, and cannot help in optimizing autonomy without compromising accuracy and performance. This paper presents an approach to produce a high accuracy autonomous navigation system fully integrated with the flight system. The resulting system performs real-time state estimation by using an Extended Kalman Filter (EKF) implemented with high-fidelity state dynamics model, as does the GPS Enhanced Orbit Determination Experiment (GEODE) system developed by the NASA Goddard Space Flight Center. Augmented to the EKF is a sophisticated neural-fuzzy system, which combines the explicit knowledge representation of fuzzy logic with the learning power of neural networks. The fuzzy-neural system performs most of the self-tuning capability and helps the navigation system recover from estimation errors. The core requirement is a method of state estimation that handles uncertainties robustly, capable of identifying estimation problems, flexible enough to make decisions and adjustments to recover from these problems, and compact enough to run on flight hardware. The resulting system can be extended to support geosynchronous spacecraft and high-eccentricity orbits. Mathematical methodology, systems and operations concepts, and implementation of a system prototype are presented in this paper. Results from the use of the prototype to evaluate optimal control algorithms implemented are discussed. Test data and major control issues (e.g., how to define specific roles for fuzzy logic to support the self-learning capability) are also discussed. In addition, architecture of a complete end-to-end candidate flight system that provides navigation with highly autonomous control using data from GPS is presented.

Design of optimised backstepping controller for the synchronisation of chaotic Colpitts oscillator using shark smell algorithm

NASA Astrophysics Data System (ADS)

Fouladi, Ehsan; Mojallali, Hamed

2018-01-01

In this paper, an adaptive backstepping controller has been tuned to synchronise two chaotic Colpitts oscillators in a master-slave configuration. The parameters of the controller are determined using shark smell optimisation (SSO) algorithm. Numerical results are presented and compared with those of particle swarm optimisation (PSO) algorithm. Simulation results show better performance in terms of accuracy and convergence for the proposed optimised method compared to PSO optimised controller or any non-optimised backstepping controller.

Adaptive control in series load PWM induction heating inverters

NASA Astrophysics Data System (ADS)

Szelitzky, Tibor; Henrietta Dulf, Eva

2013-12-01

Permanent variations of the electric properties of the load in induction heating equipment make difficult to control the plant. To overcome these disadvantages, the authors propose a new approach based on adaptive control methods. For real plants it is enough to present desired performances or start-up variables for the controller, from which the algorithms tune the controllers by itself. To present the advantages of the proposed controllers, comparisons are made to a PI controller tuned through Ziegler-Nichols method.

Study of the fractional order proportional integral controller for the permanent magnet synchronous motor based on the differential evolution algorithm.

PubMed

Zheng, Weijia; Pi, Youguo

2016-07-01

A tuning method of the fractional order proportional integral speed controller for a permanent magnet synchronous motor is proposed in this paper. Taking the combination of the integral of time and absolute error and the phase margin as the optimization index, the robustness specification as the constraint condition, the differential evolution algorithm is applied to search the optimal controller parameters. The dynamic response performance and robustness of the obtained optimal controller are verified by motor speed-tracking experiments on the motor speed control platform. Experimental results show that the proposed tuning method can enable the obtained control system to achieve both the optimal dynamic response performance and the robustness to gain variations. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Temperature control in a solar collector field using Filtered Dynamic Matrix Control.

PubMed

Lima, Daniel Martins; Normey-Rico, Julio Elias; Santos, Tito Luís Maia

2016-05-01

This paper presents the output temperature control of a solar collector field of a desalinization plant using the Filtered Dynamic Matrix Control (FDMC). The FDMC is a modified controller based on the Dynamic Matrix Control (DMC), a predictive control strategy widely used in industry. In the FDMC, a filter is used in the prediction error, which allows the modification of the robustness and disturbance rejection characteristics of the original algorithm. The implementation and tuning of the FDMC are simple and maintain the advantages of DMC. Several simulation results using a validated model of the solar plant are presented considering different scenarios. The results are also compared to nonlinear control techniques, showing that FDMC, if properly tuned, can yield similar results to more complex control algorithms. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Effects of computing time delay on real-time control systems

NASA Technical Reports Server (NTRS)

Shin, Kang G.; Cui, Xianzhong

1988-01-01

The reliability of a real-time digital control system depends not only on the reliability of the hardware and software used, but also on the speed in executing control algorithms. The latter is due to the negative effects of computing time delay on control system performance. For a given sampling interval, the effects of computing time delay are classified into the delay problem and the loss problem. Analysis of these two problems is presented as a means of evaluating real-time control systems. As an example, both the self-tuning predicted (STP) control and Proportional-Integral-Derivative (PID) control are applied to the problem of tracking robot trajectories, and their respective effects of computing time delay on control performance are comparatively evaluated. For this example, the STP (PID) controller is shown to outperform the PID (STP) controller in coping with the delay (loss) problem.

Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

PubMed

Chen, Zhihuan; Yuan, Yanbin; Yuan, Xiaohui; Huang, Yuehua; Li, Xianshan; Li, Wenwu

2015-05-01

A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Application of IFT and SPSA to servo system control.

PubMed

Rădac, Mircea-Bogdan; Precup, Radu-Emil; Petriu, Emil M; Preitl, Stefan

2011-12-01

This paper treats the application of two data-based model-free gradient-based stochastic optimization techniques, i.e., iterative feedback tuning (IFT) and simultaneous perturbation stochastic approximation (SPSA), to servo system control. The representative case of controlled processes modeled by second-order systems with an integral component is discussed. New IFT and SPSA algorithms are suggested to tune the parameters of the state feedback controllers with an integrator in the linear-quadratic-Gaussian (LQG) problem formulation. An implementation case study concerning the LQG-based design of an angular position controller for a direct current servo system laboratory equipment is included to highlight the pros and cons of IFT and SPSA from an application's point of view. The comparison of IFT and SPSA algorithms is focused on an insight into their implementation.

Pole-placement Predictive Functional Control for under-damped systems with real numbers algebra.

PubMed

Zabet, K; Rossiter, J A; Haber, R; Abdullah, M

2017-11-01

This paper presents the new algorithm of PP-PFC (Pole-placement Predictive Functional Control) for stable, linear under-damped higher-order processes. It is shown that while conventional PFC aims to get first-order exponential behavior, this is not always straightforward with significant under-damped modes and hence a pole-placement PFC algorithm is proposed which can be tuned more precisely to achieve the desired dynamics, but exploits complex number algebra and linear combinations in order to deliver guarantees of stability and performance. Nevertheless, practical implementation is easier by avoiding complex number algebra and hence a modified formulation of the PP-PFC algorithm is also presented which utilises just real numbers while retaining the key attributes of simple algebra, coding and tuning. The potential advantages are demonstrated with numerical examples and real-time control of a laboratory plant. Copyright © 2017 ISA. All rights reserved.

Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem

NASA Astrophysics Data System (ADS)

Skakov, E. S.; Malysh, V. N.

2018-03-01

The aim of the work is to create an evolutionary method for optimizing the values of the control parameters of metaheuristics of solving the NP-hard facility location problem. A system analysis of the tuning process of optimization algorithms parameters is carried out. The problem of finding the parameters of a metaheuristic algorithm is formulated as a meta-optimization problem. Evolutionary metaheuristic has been chosen to perform the task of meta-optimization. Thus, the approach proposed in this work can be called “meta-metaheuristic”. Computational experiment proving the effectiveness of the procedure of tuning the control parameters of metaheuristics has been performed.

Multivariable Control Law Design for the AFTI/F-16 with a Failed Control Surface Using a Parameter-Adaptive Controller.

DTIC Science & Technology

1987-12-01

Appendix D: Macro Listings D-1 Appendix E: MATRIXx Simulation E-1 Bibiliography Vita iv e List of Figures Figure Page 1-1 Self -Tuning Regulator 6 2-1 AFTI...Command 59 4-25 Yaw Rate Command - Three Pulses 60 4-26 Adaptive Yaw Rate Respose - Three Pulses 61 4-27 Adaptive Pitch Angle Response - Three Pulses 62 4...several types of adaptive controllers (regulators). Three of the simplest controllers are gain scheduling, model reference, and self -tuning

Analytical design equations for self-tuned Class-E power amplifier.

PubMed

Hu, Zhe; Troyk, Philip

2011-01-01

For many emerging neural prosthesis designs that are powered by inductive coupling, their small physical size requires large current in the extracorporeal transmitter coil, and the Class-E power amplifier topology is often used for the transmitter design. Tuning of Class-E circuits for efficient operation is difficult and a self-tuned circuit can facilitate the tuning. The coil current is sensed and used to tune the switching of the transistor switch in the Class-E circuit in order to maintain its high-efficiency operation. Although mathematically complex, the analysis and design procedure for the self-tuned Class-E circuit can be simplified due to the current feedback control, which makes the phase angle between the switching pulse and the coil current predetermined. In this paper explicit analytical design equations are derived and a detailed design procedure is presented and compared with the conventional Class-E design approaches.

Prediction of properties of wheat dough using intelligent deep belief networks

NASA Astrophysics Data System (ADS)

Guha, Paramita; Bhatnagar, Taru; Pal, Ishan; Kamboj, Uma; Mishra, Sunita

2017-11-01

In this paper, the rheological and chemical properties of wheat dough are predicted using deep belief networks. Wheat grains are stored at controlled environmental conditions. The internal parameters of grains viz., protein, fat, carbohydrates, moisture, ash are determined using standard chemical analysis and viscosity of the dough is measured using Rheometer. Here, fat, carbohydrates, moisture, ash and temperature are considered as inputs whereas protein and viscosity are chosen as outputs. The prediction algorithm is developed using deep neural network where each layer is trained greedily using restricted Boltzmann machine (RBM) networks. The overall network is finally fine-tuned using standard neural network technique. In most literature, it has been found that fine-tuning is done using back-propagation technique. In this paper, a new algorithm is proposed in which each layer is tuned using RBM and the final network is fine-tuned using deep neural network (DNN). It has been observed that with the proposed algorithm, errors between the actual and predicted outputs are less compared to the conventional algorithm. Hence, the given network can be considered as beneficial as it predicts the outputs more accurately. Numerical results along with discussions are presented.

Self-organizing radial basis function networks for adaptive flight control and aircraft engine state estimation

NASA Astrophysics Data System (ADS)

Shankar, Praveen

The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network based adaptive controller. While the fixed RBF network based controller which is tuned to compensate for control surface failures fails to achieve the same performance under modeling uncertainty and disturbances, the SORBFN is able to achieve good tracking convergence under all error conditions.

MCMAC-cVT: a novel on-line associative memory based CVT transmission control system.

PubMed

Ang, K K; Quek, C; Wahab, A

2002-03-01

This paper describes a novel application of an associative memory called the Modified Cerebellar Articulation Controller (MCMAC) (Int. J. Artif. Intell. Engng, 10 (1996) 135) in a continuous variable transmission (CVT) control system. It allows the on-line tuning of the associative memory and produces an effective gain-schedule for the automatic selection of the CVT gear ratio. Various control algorithms are investigated to control the CVT gear ratio to maintain the engine speed within a narrow range of efficient operating speed independently of the vehicle velocity. Extensive simulation results are presented to evaluate the control performance of a direct digital PID control algorithm with auto-tuning (Trans. ASME, 64 (1942)) and anti-windup mechanism. In particular, these results are contrasted against the control performance produced using the MCMAC (Int. J. Artif. Intell. Engng, 10 (1996) 135) with momentum, neighborhood learning and Averaged Trapezoidal Output (MCMAC-ATO) as the neural control algorithm for controlling the CVT. Simulation results are presented that show the reduced control fluctuations and improved learning capability of the MCMAC-ATO without incurring greater memory requirement. In particular, MCMAC-ATO is able to learn and control the CVT simultaneously while still maintaining acceptable control performance.

Application of Least Mean Square Algorithms to Spacecraft Vibration Compensation

NASA Technical Reports Server (NTRS)

Woodard , Stanley E.; Nagchaudhuri, Abhijit

1998-01-01

This paper describes the application of the Least Mean Square (LMS) algorithm in tandem with the Filtered-X Least Mean Square algorithm for controlling a science instrument's line-of-sight pointing. Pointing error is caused by a periodic disturbance and spacecraft vibration. A least mean square algorithm is used on-orbit to produce the transfer function between the instrument's servo-mechanism and error sensor. The result is a set of adaptive transversal filter weights tuned to the transfer function. The Filtered-X LMS algorithm, which is an extension of the LMS, tunes a set of transversal filter weights to the transfer function between the disturbance source and the servo-mechanism's actuation signal. The servo-mechanism's resulting actuation counters the disturbance response and thus maintains accurate science instrumental pointing. A simulation model of the Upper Atmosphere Research Satellite is used to demonstrate the algorithms.

Genetic algorithm based active vibration control for a moving flexible smart beam driven by a pneumatic rod cylinder

NASA Astrophysics Data System (ADS)

Qiu, Zhi-cheng; Shi, Ming-li; Wang, Bin; Xie, Zhuo-wei

2012-05-01

A rod cylinder based pneumatic driving scheme is proposed to suppress the vibration of a flexible smart beam. Pulse code modulation (PCM) method is employed to control the motion of the cylinder's piston rod for simultaneous positioning and vibration suppression. Firstly, the system dynamics model is derived using Hamilton principle. Its standard state-space representation is obtained for characteristic analysis, controller design, and simulation. Secondly, a genetic algorithm (GA) is applied to optimize and tune the control gain parameters adaptively based on the specific performance index. Numerical simulations are performed on the pneumatic driving elastic beam system, using the established model and controller with tuned gains by GA optimization process. Finally, an experimental setup for the flexible beam driven by a pneumatic rod cylinder is constructed. Experiments for suppressing vibrations of the flexible beam are conducted. Theoretical analysis, numerical simulation and experimental results demonstrate that the proposed pneumatic drive scheme and the adopted control algorithms are feasible. The large amplitude vibration of the first bending mode can be suppressed effectively.

A simple attitude control of quadrotor helicopter based on Ziegler-Nichols rules for tuning PD parameters.

PubMed

He, ZeFang; Zhao, Long

2014-01-01

An attitude control strategy based on Ziegler-Nichols rules for tuning PD (proportional-derivative) parameters of quadrotor helicopters is presented to solve the problem that quadrotor tends to be instable. This problem is caused by the narrow definition domain of attitude angles of quadrotor helicopters. The proposed controller is nonlinear and consists of a linear part and a nonlinear part. The linear part is a PD controller with PD parameters tuned by Ziegler-Nichols rules and acts on the quadrotor decoupled linear system after feedback linearization; the nonlinear part is a feedback linearization item which converts a nonlinear system into a linear system. It can be seen from the simulation results that the attitude controller proposed in this paper is highly robust, and its control effect is better than the other two nonlinear controllers. The nonlinear parts of the other two nonlinear controllers are the same as the attitude controller proposed in this paper. The linear part involves a PID (proportional-integral-derivative) controller with the PID controller parameters tuned by Ziegler-Nichols rules and a PD controller with the PD controller parameters tuned by GA (genetic algorithms). Moreover, this attitude controller is simple and easy to implement.

Decoupling PI Controller Design for a Normal Conducting RF Cavity Using a Recursive LEVENBERG-MARQUARDT Algorithm

NASA Astrophysics Data System (ADS)

Kwon, Sung-il; Lynch, M.; Prokop, M.

2005-02-01

This paper addresses the system identification and the decoupling PI controller design for a normal conducting RF cavity. Based on the open-loop measurement data of an SNS DTL cavity, the open-loop system's bandwidths and loop time delays are estimated by using batched least square. With the identified system, a PI controller is designed in such a way that it suppresses the time varying klystron droop and decouples the In-phase and Quadrature of the cavity field. The Levenberg-Marquardt algorithm is applied for nonlinear least squares to obtain the optimal PI controller parameters. The tuned PI controller gains are downloaded to the low-level RF system by using channel access. The experiment of the closed-loop system is performed and the performance is investigated. The proposed tuning method is running automatically in real time interface between a host computer with controller hardware through ActiveX Channel Access.

In-hardware demonstration of model-independent adaptive tuning of noisy systems with arbitrary phase drift

DOE PAGES

Scheinker, Alexander; Baily, Scott; Young, Daniel; ...

2014-08-01

In this work, an implementation of a recently developed model-independent adaptive control scheme, for tuning uncertain and time varying systems, is demonstrated on the Los Alamos linear particle accelerator. The main benefits of the algorithm are its simplicity, ability to handle an arbitrary number of components without increased complexity, and the approach is extremely robust to measurement noise, a property which is both analytically proven and demonstrated in the experiments performed. We report on the application of this algorithm for simultaneous tuning of two buncher radio frequency (RF) cavities, in order to maximize beam acceptance into the accelerating electromagnetic fieldmore » cavities of the machine, with the tuning based only on a noisy measurement of the surviving beam current downstream from the two bunching cavities. The algorithm automatically responds to arbitrary phase shift of the cavity phases, automatically re-tuning the cavity settings and maximizing beam acceptance. Because it is model independent it can be utilized for continuous adaptation to time-variation of a large system, such as due to thermal drift, or damage to components, in which the remaining, functional components would be automatically re-tuned to compensate for the failing ones. We start by discussing the general model-independent adaptive scheme and how it may be digitally applied to a large class of multi-parameter uncertain systems, and then present our experimental results.« less

Performance Costs when Emotion Tunes Inappropriate Cognitive Abilities: Implications for Mental Resources and Behavior

ERIC Educational Resources Information Center

Storbeck, Justin

2012-01-01

Emotion tunes cognition, such that approach-motivated positive states promote verbal cognition, whereas withdrawal-motivated negative states promote spatial cognition (Gray, 2001). The current research examined whether self-control resources become depleted and influence subsequent behavior when emotion tunes an inappropriate cognitive tendency.…

Sensor Fusion, Prognostics, Diagnostics and Failure Mode Control for Complex Aerospace Systems

DTIC Science & Technology

2010-10-01

algorithm   and   to   then   tune   the   candidates   individually   using   known   metaheuristics .  As  will  be...parallel. The result of this arrangement is that the processing is a form that is analogous to standard parallel genetic algorithms , and as such...search algorithm then uses the hybrid of fitness data to rank the results. The ETRAS controller is developed using pre-selection, showing that a

Description and performance analysis of a generalized optimal algorithm for aerobraking guidance

NASA Technical Reports Server (NTRS)

Evans, Steven W.; Dukeman, Greg A.

1993-01-01

A practical real-time guidance algorithm has been developed for aerobraking vehicles which nearly minimizes the maximum heating rate, the maximum structural loads, and the post-aeropass delta V requirement for orbit insertion. The algorithm is general and reusable in the sense that a minimum of assumptions are made, thus greatly reducing the number of parameters that must be determined prior to a given mission. A particularly interesting feature is that in-plane guidance performance is tuned by adjusting one mission-dependent, the bank margin; similarly, the out-of-plane guidance performance is tuned by adjusting a plane controller time constant. Other features of the algorithm are simplicity, efficiency and ease of use. The trimmed vehicle with bank angle modulation as the method of trajectory control. Performance of this guidance algorithm is examined by its use in an aerobraking testbed program. The performance inquiry extends to a wide range of entry speeds covering a number of potential mission applications. Favorable results have been obtained with a minimum of development effort, and directions for improvement of performance are indicated.

Tuning of PID controller using optimization techniques for a MIMO process

NASA Astrophysics Data System (ADS)

Thulasi dharan, S.; Kavyarasan, K.; Bagyaveereswaran, V.

2017-11-01

In this paper, two processes were considered one is Quadruple tank process and the other is CSTR (Continuous Stirred Tank Reactor) process. These are majorly used in many industrial applications for various domains, especially, CSTR in chemical plants.At first mathematical model of both the process is to be done followed by linearization of the system due to MIMO process and controllers are the major part to control the whole process to our desired point as per the applications so the tuning of the controller plays a major role among the whole process. For tuning of parameters we use two optimizations techniques like Particle Swarm Optimization, Genetic Algorithm. The above techniques are majorly used in different applications to obtain which gives the best among all, we use these techniques to obtain the best tuned values among many. Finally, we will compare the performance of the each process with both the techniques.

Model predictive control of attitude maneuver of a geostationary flexible satellite based on genetic algorithm

NASA Astrophysics Data System (ADS)

TayyebTaher, M.; Esmaeilzadeh, S. Majid

2017-07-01

This article presents an application of Model Predictive Controller (MPC) to the attitude control of a geostationary flexible satellite. SIMO model has been used for the geostationary satellite, using the Lagrange equations. Flexibility is also included in the modelling equations. The state space equations are expressed in order to simplify the controller. Naturally there is no specific tuning rule to find the best parameters of an MPC controller which fits the desired controller. Being an intelligence method for optimizing problem, Genetic Algorithm has been used for optimizing the performance of MPC controller by tuning the controller parameter due to minimum rise time, settling time, overshoot of the target point of the flexible structure and its mode shape amplitudes to make large attitude maneuvers possible. The model included geosynchronous orbit environment and geostationary satellite parameters. The simulation results of the flexible satellite with attitude maneuver shows the efficiency of proposed optimization method in comparison with LQR optimal controller.

Positive position control of robotic manipulators

NASA Technical Reports Server (NTRS)

Baz, A.; Gumusel, L.

1989-01-01

The present, simple and accurate position-control algorithm, which is applicable to fast-moving and lightly damped robot arms, is based on the positive position feedback (PPF) strategy and relies solely on position sensors to monitor joint angles of robotic arms to furnish stable position control. The optimized tuned filters, in the form of a set of difference equations, manipulate position signals for robotic system performance. Attention is given to comparisons between this PPF-algorithm controller's experimentally ascertained performance characteristics and those of a conventional proportional controller.

Precision-controlled elution of a 82Sr/82Rb generator for cardiac perfusion imaging with positron emission tomography

NASA Astrophysics Data System (ADS)

Klein, R.; Adler, A.; Beanlands, R. S.; de Kemp, R. A.

2007-02-01

A rubidium-82 (82Rb) elution system is described for use with positron emission tomography. Due to the short half-life of 82Rb (76 s), the system physics must be modelled precisely to account for transport delay and the associated activity decay and dispersion. Saline flow is switched between a 82Sr/82Rb generator and a bypass line to achieve a constant-activity elution of 82Rb. Pulse width modulation (PWM) of a solenoid valve is compared to simple threshold control as a means to simulate a proportional valve. A predictive-corrective control (PCC) algorithm is developed which produces a constant-activity elution within the constraints of long feedback delay and short elution time. The system model parameters are adjusted through a self-tuning algorithm to minimize error versus the requested time-activity profile. The system is self-calibrating with 2.5% repeatability, independent of generator activity and elution flow rate. Accurate 30 s constant-activity elutions of 10-70% of the total generator activity are achieved using both control methods. The combined PWM-PCC method provides significant improvement in precision and accuracy of the requested elution profiles. The 82Rb elution system produces accurate and reproducible constant-activity elution profiles of 82Rb activity, independent of parent 82Sr activity in the generator. More reproducible elution profiles may improve the quality of clinical and research PET perfusion studies using 82Rb.

Precision-controlled elution of a 82Sr/82Rb generator for cardiac perfusion imaging with positron emission tomography.

PubMed

Klein, R; Adler, A; Beanlands, R S; Dekemp, R A

2007-02-07

A rubidium-82 ((82)Rb) elution system is described for use with positron emission tomography. Due to the short half-life of (82)Rb (76 s), the system physics must be modelled precisely to account for transport delay and the associated activity decay and dispersion. Saline flow is switched between a (82)Sr/(82)Rb generator and a bypass line to achieve a constant-activity elution of (82)Rb. Pulse width modulation (PWM) of a solenoid valve is compared to simple threshold control as a means to simulate a proportional valve. A predictive-corrective control (PCC) algorithm is developed which produces a constant-activity elution within the constraints of long feedback delay and short elution time. The system model parameters are adjusted through a self-tuning algorithm to minimize error versus the requested time-activity profile. The system is self-calibrating with 2.5% repeatability, independent of generator activity and elution flow rate. Accurate 30 s constant-activity elutions of 10-70% of the total generator activity are achieved using both control methods. The combined PWM-PCC method provides significant improvement in precision and accuracy of the requested elution profiles. The (82)Rb elution system produces accurate and reproducible constant-activity elution profiles of (82)Rb activity, independent of parent (82)Sr activity in the generator. More reproducible elution profiles may improve the quality of clinical and research PET perfusion studies using (82)Rb.

A Simple Attitude Control of Quadrotor Helicopter Based on Ziegler-Nichols Rules for Tuning PD Parameters

PubMed Central

He, ZeFang

2014-01-01

An attitude control strategy based on Ziegler-Nichols rules for tuning PD (proportional-derivative) parameters of quadrotor helicopters is presented to solve the problem that quadrotor tends to be instable. This problem is caused by the narrow definition domain of attitude angles of quadrotor helicopters. The proposed controller is nonlinear and consists of a linear part and a nonlinear part. The linear part is a PD controller with PD parameters tuned by Ziegler-Nichols rules and acts on the quadrotor decoupled linear system after feedback linearization; the nonlinear part is a feedback linearization item which converts a nonlinear system into a linear system. It can be seen from the simulation results that the attitude controller proposed in this paper is highly robust, and its control effect is better than the other two nonlinear controllers. The nonlinear parts of the other two nonlinear controllers are the same as the attitude controller proposed in this paper. The linear part involves a PID (proportional-integral-derivative) controller with the PID controller parameters tuned by Ziegler-Nichols rules and a PD controller with the PD controller parameters tuned by GA (genetic algorithms). Moreover, this attitude controller is simple and easy to implement. PMID:25614879

Motion Cueing Algorithm Development: New Motion Cueing Program Implementation and Tuning

NASA Technical Reports Server (NTRS)

Houck, Jacob A. (Technical Monitor); Telban, Robert J.; Cardullo, Frank M.; Kelly, Lon C.

2005-01-01

A computer program has been developed for the purpose of driving the NASA Langley Research Center Visual Motion Simulator (VMS). This program includes two new motion cueing algorithms, the optimal algorithm and the nonlinear algorithm. A general description of the program is given along with a description and flowcharts for each cueing algorithm, and also descriptions and flowcharts for subroutines used with the algorithms. Common block variable listings and a program listing are also provided. The new cueing algorithms have a nonlinear gain algorithm implemented that scales each aircraft degree-of-freedom input with a third-order polynomial. A description of the nonlinear gain algorithm is given along with past tuning experience and procedures for tuning the gain coefficient sets for each degree-of-freedom to produce the desired piloted performance. This algorithm tuning will be needed when the nonlinear motion cueing algorithm is implemented on a new motion system in the Cockpit Motion Facility (CMF) at the NASA Langley Research Center.

Comparative Analysis of Particle Swarm and Differential Evolution via Tuning on Ultrasmall Titanium Oxide Nanoclusters

NASA Astrophysics Data System (ADS)

Inclan, Eric; Lassester, Jack; Geohegan, David; Yoon, Mina

Optimization algorithms (OA) coupled with numerical methods enable researchers to identify and study (meta) stable nanoclusters without the control restrictions of empirical methods. An algorithm's performance is governed by two factors: (1) its compatibility with an objective function, (2) the dimension of a design space, which increases with cluster size. Although researchers often tune an algorithm's user-defined parameters (UDP), tuning is not guaranteed to improve performance. In this research, Particle Swarm (PSO) and Differential Evolution (DE), are compared by tuning their UDP in a multi-objective optimization environment (MOE). Combined with a Kolmogorov Smirnov test for statistical significance, the MOE enables the study of the Pareto Front (PF), made of the UDP settings that trade-off between best performance in energy minimization (``effectiveness'') based on force-field potential energy, and best convergence rate (``efficiency''). By studying the PF, this research finds that UDP values frequently suggested in the literature do not provide best effectiveness for these methods. Additionally, monotonic convergence is found to significantly improve efficiency without sacrificing effectiveness for very small systems, suggesting better compatibility. Work is supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division.

An implicit iterative algorithm with a tuning parameter for Itô Lyapunov matrix equations

NASA Astrophysics Data System (ADS)

Zhang, Ying; Wu, Ai-Guo; Sun, Hui-Jie

2018-01-01

In this paper, an implicit iterative algorithm is proposed for solving a class of Lyapunov matrix equations arising in Itô stochastic linear systems. A tuning parameter is introduced in this algorithm, and thus the convergence rate of the algorithm can be changed. Some conditions are presented such that the developed algorithm is convergent. In addition, an explicit expression is also derived for the optimal tuning parameter, which guarantees that the obtained algorithm achieves its fastest convergence rate. Finally, numerical examples are employed to illustrate the effectiveness of the given algorithm.

TCR tuning of T cell subsets.

PubMed

Cho, Jae-Ho; Sprent, Jonathan

2018-05-01

After selection in the thymus, the post-thymic T cell compartments comprise heterogenous subsets of naive and memory T cells that make continuous T cell receptor (TCR) contact with self-ligands bound to major histocompatibility complex (MHC) molecules. T cell recognition of self-MHC ligands elicits covert TCR signaling and is particularly important for controlling survival of naive T cells. Such tonic TCR signaling is tightly controlled and maintains the cells in a quiescent state to avoid autoimmunity. Here, we review how naive and memory T cells are differentially tuned and wired for TCR sensitivity to self and foreign ligands. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The hierarchical expert tuning of PID controllers using tools of soft computing.

PubMed

Karray, F; Gueaieb, W; Al-Sharhan, S

2002-01-01

We present soft computing-based results pertaining to the hierarchical tuning process of PID controllers located within the control loop of a class of nonlinear systems. The results are compared with PID controllers implemented either in a stand alone scheme or as a part of conventional gain scheduling structure. This work is motivated by the increasing need in the industry to design highly reliable and efficient controllers for dealing with regulation and tracking capabilities of complex processes characterized by nonlinearities and possibly time varying parameters. The soft computing-based controllers proposed are hybrid in nature in that they integrate within a well-defined hierarchical structure the benefits of hard algorithmic controllers with those having supervisory capabilities. The controllers proposed also have the distinct features of learning and auto-tuning without the need for tedious and computationally extensive online systems identification schemes.

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

A Novel Approach to Implement Takagi-Sugeno Fuzzy Models.

PubMed

Chang, Chia-Wen; Tao, Chin-Wang

2017-09-01

This paper proposes new algorithms based on the fuzzy c-regressing model algorithm for Takagi-Sugeno (T-S) fuzzy modeling of the complex nonlinear systems. A fuzzy c-regression state model (FCRSM) algorithm is a T-S fuzzy model in which the functional antecedent and the state-space-model-type consequent are considered with the available input-output data. The antecedent and consequent forms of the proposed FCRSM consists mainly of two advantages: one is that the FCRSM has low computation load due to only one input variable is considered in the antecedent part; another is that the unknown system can be modeled to not only the polynomial form but also the state-space form. Moreover, the FCRSM can be extended to FCRSM-ND and FCRSM-Free algorithms. An algorithm FCRSM-ND is presented to find the T-S fuzzy state-space model of the nonlinear system when the input-output data cannot be precollected and an assumed effective controller is available. In the practical applications, the mathematical model of controller may be hard to be obtained. In this case, an online tuning algorithm, FCRSM-FREE, is designed such that the parameters of a T-S fuzzy controller and the T-S fuzzy state model of an unknown system can be online tuned simultaneously. Four numerical simulations are given to demonstrate the effectiveness of the proposed approach.

Cognitive programs: software for attention's executive

PubMed Central

Tsotsos, John K.; Kruijne, Wouter

2014-01-01

What are the computational tasks that an executive controller for visual attention must solve? This question is posed in the context of the Selective Tuning model of attention. The range of required computations go beyond top-down bias signals or region-of-interest determinations, and must deal with overt and covert fixations, process timing and synchronization, information routing, memory, matching control to task, spatial localization, priming, and coordination of bottom-up with top-down information. During task execution, results must be monitored to ensure the expected results. This description includes the kinds of elements that are common in the control of any kind of complex machine or system. We seek a mechanistic integration of the above, in other words, algorithms that accomplish control. Such algorithms operate on representations, transforming a representation of one kind into another, which then forms the input to yet another algorithm. Cognitive Programs (CPs) are hypothesized to capture exactly such representational transformations via stepwise sequences of operations. CPs, an updated and modernized offspring of Ullman's Visual Routines, impose an algorithmic structure to the set of attentional functions and play a role in the overall shaping of attentional modulation of the visual system so that it provides its best performance. This requires that we consider the visual system as a dynamic, yet general-purpose processor tuned to the task and input of the moment. This differs dramatically from the almost universal cognitive and computational views, which regard vision as a passively observing module to which simple questions about percepts can be posed, regardless of task. Differing from Visual Routines, CPs explicitly involve the critical elements of Visual Task Executive (vTE), Visual Attention Executive (vAE), and Visual Working Memory (vWM). Cognitive Programs provide the software that directs the actions of the Selective Tuning model of visual attention. PMID:25505430

Optimal Self-Tuning PID Controller Based on Low Power Consumption for a Server Fan Cooling System.

PubMed

Lee, Chengming; Chen, Rongshun

2015-05-20

Recently, saving the cooling power in servers by controlling the fan speed has attracted considerable attention because of the increasing demand for high-density servers. This paper presents an optimal self-tuning proportional-integral-derivative (PID) controller, combining a PID neural network (PIDNN) with fan-power-based optimization in the transient-state temperature response in the time domain, for a server fan cooling system. Because the thermal model of the cooling system is nonlinear and complex, a server mockup system simulating a 1U rack server was constructed and a fan power model was created using a third-order nonlinear curve fit to determine the cooling power consumption by the fan speed control. PIDNN with a time domain criterion is used to tune all online and optimized PID gains. The proposed controller was validated through experiments of step response when the server operated from the low to high power state. The results show that up to 14% of a server's fan cooling power can be saved if the fan control permits a slight temperature response overshoot in the electronic components, which may provide a time-saving strategy for tuning the PID controller to control the server fan speed during low fan power consumption.

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.

Implementation of Automatic Focusing Algorithms for a Computer Vision System with Camera Control.

DTIC Science & Technology

1983-08-15

obtainable from real data, rather than relying on a stock database. Often, computer vision and image processing algorithms become subconsciously tuned to...two coils on the same mount structure. Since it was not possible to reprogram the binary system, we turned to the POPEYE system for both its grey

HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN

EPA Science Inventory

While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...

A Distributed Transmission Rate Adjustment Algorithm in Heterogeneous CSMA/CA Networks

PubMed Central

Xie, Shuanglong; Low, Kay Soon; Gunawan, Erry

2015-01-01

Distributed transmission rate tuning is important for a wide variety of IEEE 802.15.4 network applications such as industrial network control systems. Such systems often require each node to sustain certain throughput demand in order to guarantee the system performance. It is thus essential to determine a proper transmission rate that can meet the application requirement and compensate for network imperfections (e.g., packet loss). Such a tuning in a heterogeneous network is difficult due to the lack of modeling techniques that can deal with the heterogeneity of the network as well as the network traffic changes. In this paper, a distributed transmission rate tuning algorithm in a heterogeneous IEEE 802.15.4 CSMA/CA network is proposed. Each node uses the results of clear channel assessment (CCA) to estimate the busy channel probability. Then a mathematical framework is developed to estimate the on-going heterogeneous traffics using the busy channel probability at runtime. Finally a distributed algorithm is derived to tune the transmission rate of each node to accurately meet the throughput requirement. The algorithm does not require modifications on IEEE 802.15.4 MAC layer and it has been experimentally implemented and extensively tested using TelosB nodes with the TinyOS protocol stack. The results reveal that the algorithm is accurate and can satisfy the throughput demand. Compared with existing techniques, the algorithm is fully distributed and thus does not require any central coordination. With this property, it is able to adapt to traffic changes and re-adjust the transmission rate to the desired level, which cannot be achieved using the traditional modeling techniques. PMID:25822140

Improved model reduction and tuning of fractional-order PI(λ)D(μ) controllers for analytical rule extraction with genetic programming.

PubMed

Das, Saptarshi; Pan, Indranil; Das, Shantanu; Gupta, Amitava

2012-03-01

Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional-integral-derivative (PID) and fractional-order (FO) PI(λ)D(μ) controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H(2)-norm-based reduced parameter modeling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and PI(λ)D(μ) controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

A conformal mapping based fractional order approach for sub-optimal tuning of PID controllers with guaranteed dominant pole placement

NASA Astrophysics Data System (ADS)

Saha, Suman; Das, Saptarshi; Das, Shantanu; Gupta, Amitava

2012-09-01

A novel conformal mapping based fractional order (FO) methodology is developed in this paper for tuning existing classical (Integer Order) Proportional Integral Derivative (PID) controllers especially for sluggish and oscillatory second order systems. The conventional pole placement tuning via Linear Quadratic Regulator (LQR) method is extended for open loop oscillatory systems as well. The locations of the open loop zeros of a fractional order PID (FOPID or PIλDμ) controller have been approximated in this paper vis-à-vis a LQR tuned conventional integer order PID controller, to achieve equivalent integer order PID control system. This approach eases the implementation of analog/digital realization of a FOPID controller with its integer order counterpart along with the advantages of fractional order controller preserved. It is shown here in the paper that decrease in the integro-differential operators of the FOPID/PIλDμ controller pushes the open loop zeros of the equivalent PID controller towards greater damping regions which gives a trajectory of the controller zeros and dominant closed loop poles. This trajectory is termed as "M-curve". This phenomena is used to design a two-stage tuning algorithm which reduces the existing PID controller's effort in a significant manner compared to that with a single stage LQR based pole placement method at a desired closed loop damping and frequency.

Robust tuning of robot control systems

NASA Technical Reports Server (NTRS)

Minis, I.; Uebel, M.

1992-01-01

The computed torque control problem is examined for a robot arm with flexible, geared, joint drive systems which are typical in many industrial robots. The standard computed torque algorithm is not directly applicable to this class of manipulators because of the dynamics introduced by the joint drive system. The proposed approach to computed torque control combines a computed torque algorithm with torque controller at each joint. Three such control schemes are proposed. The first scheme uses the joint torque control system currently implemented on the robot arm and a novel form of the computed torque algorithm. The other two use the standard computed torque algorithm and a novel model following torque control system based on model following techniques. Standard tasks and performance indices are used to evaluate the performance of the controllers. Both numerical simulations and experiments are used in evaluation. The study shows that all three proposed systems lead to improved tracking performance over a conventional PD controller.

Atom based grain extraction and measurement of geometric properties

NASA Astrophysics Data System (ADS)

Martine La Boissonière, Gabriel; Choksi, Rustum

2018-04-01

We introduce an accurate, self-contained and automatic atom based numerical algorithm to characterize grain distributions in two dimensional Phase Field Crystal (PFC) simulations. We compare the method with hand segmented and known test grain distributions to show that the algorithm is able to extract grains and measure their area, perimeter and other geometric properties with high accuracy. Four input parameters must be set by the user and their influence on the results is described. The method is currently tuned to extract data from PFC simulations in the hexagonal lattice regime but the framework may be extended to more general problems.

Model-Free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning

NASA Astrophysics Data System (ADS)

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2017-04-01

This paper proposes the combination of two model-free controller tuning techniques, namely linear virtual reference feedback tuning (VRFT) and nonlinear state-feedback Q-learning, referred to as a new mixed VRFT-Q learning approach. VRFT is first used to find stabilising feedback controller using input-output experimental data from the process in a model reference tracking setting. Reinforcement Q-learning is next applied in the same setting using input-state experimental data collected under perturbed VRFT to ensure good exploration. The Q-learning controller learned with a batch fitted Q iteration algorithm uses two neural networks, one for the Q-function estimator and one for the controller, respectively. The VRFT-Q learning approach is validated on position control of a two-degrees-of-motion open-loop stable multi input-multi output (MIMO) aerodynamic system (AS). Extensive simulations for the two independent control channels of the MIMO AS show that the Q-learning controllers clearly improve performance over the VRFT controllers.

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

A comparison of force control algorithms for robots in contact with flexible environments

NASA Technical Reports Server (NTRS)

Wilfinger, Lee S.

1992-01-01

In order to perform useful tasks, the robot end-effector must come into contact with its environment. For such tasks, force feedback is frequently used to control the interaction forces. Control of these forces is complicated by the fact that the flexibility of the environment affects the stability of the force control algorithm. Because of the wide variety of different materials present in everyday environments, it is necessary to gain an understanding of how environmental flexibility affects the stability of force control algorithms. This report presents the theory and experimental results of two force control algorithms: Position Accommodation Control and Direct Force Servoing. The implementation of each of these algorithms on a two-arm robotic test bed located in the Center for Intelligent Robotic Systems for Space Exploration (CIRSSE) is discussed in detail. The behavior of each algorithm when contacting materials of different flexibility is experimentally determined. In addition, several robustness improvements to the Direct Force Servoing algorithm are suggested and experimentally verified. Finally, a qualitative comparison of the force control algorithms is provided, along with a description of a general tuning process for each control method.

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.

Robust PD Sway Control of a Lifted Load for a Crane Using a Genetic Algorithm

NASA Astrophysics Data System (ADS)

Kawada, Kazuo; Sogo, Hiroyuki; Yamamoto, Toru; Mada, Yasuhiro

PID control schemes still continue to be widely used for most industrial control systems. This is mainly because PID controllers have simple control structures, and are simple to maintain and tune. However, it is difficult to find a set of suitable control parameters in the case of time-varying and/or nonlinear systems. For such a problem, the robust controller has been proposed.Although it is important to choose the suitable nominal model in designing the robust controller, it is not usually easy.In this paper, a new robust PD controller design scheme is proposed, which utilizes a genetic algorithm.

Cascade generalized predictive control strategy for boiler drum level.

PubMed

Xu, Min; Li, Shaoyuan; Cai, Wenjian

2005-07-01

This paper proposes a cascade model predictive control scheme for boiler drum level control. By employing generalized predictive control structures for both inner and outer loops, measured and unmeasured disturbances can be effectively rejected, and drum level at constant load is maintained. In addition, nonminimum phase characteristic and system constraints in both loops can be handled effectively by generalized predictive control algorithms. Simulation results are provided to show that cascade generalized predictive control results in better performance than that of well tuned cascade proportional integral differential controllers. The algorithm has also been implemented to control a 75-MW boiler plant, and the results show an improvement over conventional control schemes.

Strain-Tuning Atomic Substitution in Two-Dimensional Atomic Crystals.

PubMed

Li, Honglai; Liu, Hongjun; Zhou, Linwei; Wu, Xueping; Pan, Yuhao; Ji, Wei; Zheng, Biyuan; Zhang, Qinglin; Zhuang, Xiujuan; Zhu, Xiaoli; Wang, Xiao; Duan, Xiangfeng; Pan, Anlian

2018-05-22

Atomic substitution offers an important route to achieve compositionally engineered two-dimensional nanostructures and their heterostructures. Despite the recent research progress, the fundamental understanding of the reaction mechanism has still remained unclear. Here, we reveal the atomic substitution mechanism of two-dimensional atomic layered materials. We found that the atomic substitution process depends on the varying lattice constant (strain) in monolayer crystals, dominated by two strain-tuning (self-promoted and self-limited) mechanisms using density functional theory calculations. These mechanisms were experimentally confirmed by the controllable realization of a graded substitution ratio in the monolayers by controlling the substitution temperature and time and further theoretically verified by kinetic Monte Carlo simulations. The strain-tuning atomic substitution processes are of general importance to other two-dimensional layered materials, which offers an interesting route for tailoring electronic and optical properties of these materials.

Self-Tuning Adaptive-Controller Using Online Frequency Identification

NASA Technical Reports Server (NTRS)

Chiang, W. W.; Cannon, R. H., Jr.

1985-01-01

A real time adaptive controller was designed and tested successfully on a fourth order laboratory dynamic system which features very low structural damping and a noncolocated actuator sensor pair. The controller, implemented in a digital minicomputer, consists of a state estimator, a set of state feedback gains, and a frequency locked loop (FLL) for real time parameter identification. The FLL can detect the closed loop natural frequency of the system being controlled, calculate the mismatch between a plant parameter and its counterpart in the state estimator, and correct the estimator parameter in real time. The adaptation algorithm can correct the controller error and stabilize the system for more than 50% variation in the plant natural frequency, compared with a 10% stability margin in frequency variation for a fixed gain controller having the same performance at the nominal plant condition. After it has locked to the correct plant frequency, the adaptive controller works as well as the fixed gain controller does when there is no parameter mismatch. The very rapid convergence of this adaptive system is demonstrated experimentally, and can also be proven with simple root locus methods.

An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy

NASA Astrophysics Data System (ADS)

Hu, Han; Ding, Yulin; Zhu, Qing; Wu, Bo; Lin, Hui; Du, Zhiqiang; Zhang, Yeting; Zhang, Yunsheng

2014-06-01

The filtering of point clouds is a ubiquitous task in the processing of airborne laser scanning (ALS) data; however, such filtering processes are difficult because of the complex configuration of the terrain features. The classical filtering algorithms rely on the cautious tuning of parameters to handle various landforms. To address the challenge posed by the bundling of different terrain features into a single dataset and to surmount the sensitivity of the parameters, in this study, we propose an adaptive surface filter (ASF) for the classification of ALS point clouds. Based on the principle that the threshold should vary in accordance to the terrain smoothness, the ASF embeds bending energy, which quantitatively depicts the local terrain structure to self-adapt the filter threshold automatically. The ASF employs a step factor to control the data pyramid scheme in which the processing window sizes are reduced progressively, and the ASF gradually interpolates thin plate spline surfaces toward the ground with regularization to handle noise. Using the progressive densification strategy, regularization and self-adaption, both performance improvement and resilience to parameter tuning are achieved. When tested against the benchmark datasets provided by ISPRS, the ASF performs the best in comparison with all other filtering methods, yielding an average total error of 2.85% when optimized and 3.67% when using the same parameter set.

Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization.

PubMed

Wong, Ieong; Liu, Wenjia; Ho, Chih-Ming; Ding, Xianting

2017-06-01

Differential evolution (DE) has been applied extensively in drug combination optimization studies in the past decade. It allows for identification of desired drug combinations with minimal experimental effort. This article proposes an adaptive population-sizing method for the DE algorithm. Our new method presents improvements in terms of efficiency and convergence over the original DE algorithm and constant stepwise population reduction-based DE algorithm, which would lead to a reduced number of cells and animals required to identify an optimal drug combination. The method continuously adjusts the reduction of the population size in accordance with the stage of the optimization process. Our adaptive scheme limits the population reduction to occur only at the exploitation stage. We believe that continuously adjusting for a more effective population size during the evolutionary process is the major reason for the significant improvement in the convergence speed of the DE algorithm. The performance of the method is evaluated through a set of unimodal and multimodal benchmark functions. In combining with self-adaptive schemes for mutation and crossover constants, this adaptive population reduction method can help shed light on the future direction of a completely parameter tune-free self-adaptive DE algorithm.

Self-Tuning Threshold Method for Real-Time Gait Phase Detection Based on Ground Contact Forces Using FSRs.

PubMed

Tang, Jing; Zheng, Jianbin; Wang, Yang; Yu, Lie; Zhan, Enqi; Song, Qiuzhi

2018-02-06

This paper presents a novel methodology for detecting the gait phase of human walking on level ground. The previous threshold method (TM) sets a threshold to divide the ground contact forces (GCFs) into on-ground and off-ground states. However, the previous methods for gait phase detection demonstrate no adaptability to different people and different walking speeds. Therefore, this paper presents a self-tuning triple threshold algorithm (STTTA) that calculates adjustable thresholds to adapt to human walking. Two force sensitive resistors (FSRs) were placed on the ball and heel to measure GCFs. Three thresholds (i.e., high-threshold, middle-threshold andlow-threshold) were used to search out the maximum and minimum GCFs for the self-adjustments of thresholds. The high-threshold was the main threshold used to divide the GCFs into on-ground and off-ground statuses. Then, the gait phases were obtained through the gait phase detection algorithm (GPDA), which provides the rules that determine calculations for STTTA. Finally, the STTTA reliability is determined by comparing the results between STTTA and Mariani method referenced as the timing analysis module (TAM) and Lopez-Meyer methods. Experimental results show that the proposed method can be used to detect gait phases in real time and obtain high reliability when compared with the previous methods in the literature. In addition, the proposed method exhibits strong adaptability to different wearers walking at different walking speeds.

An algorithm for the design and tuning of RF accelerating structures with variable cell lengths

NASA Astrophysics Data System (ADS)

Lal, Shankar; Pant, K. K.

2018-05-01

An algorithm is proposed for the design of a π mode standing wave buncher structure with variable cell lengths. It employs a two-parameter, multi-step approach for the design of the structure with desired resonant frequency and field flatness. The algorithm, along with analytical scaling laws for the design of the RF power coupling slot, makes it possible to accurately design the structure employing a freely available electromagnetic code like SUPERFISH. To compensate for machining errors, a tuning method has been devised to achieve desired RF parameters for the structure, which has been qualified by the successful tuning of a 7-cell buncher to π mode frequency of 2856 MHz with field flatness <3% and RF coupling coefficient close to unity. The proposed design algorithm and tuning method have demonstrated the feasibility of developing an S-band accelerating structure for desired RF parameters with a relatively relaxed machining tolerance of ∼ 25 μm. This paper discusses the algorithm for the design and tuning of an RF accelerating structure with variable cell lengths.

Tuning fuzzy PD and PI controllers using reinforcement learning.

PubMed

Boubertakh, Hamid; Tadjine, Mohamed; Glorennec, Pierre-Yves; Labiod, Salim

2010-10-01

In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi-Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen's implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

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.

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.

Designing of a self-adaptive digital filter using genetic algorithm

NASA Astrophysics Data System (ADS)

Geng, Xuemei; Li, Hongguang; Xu, Chi

2018-04-01

This paper presents a novel methodology applying non-linear model for closed loop Sigma-Delta modulator that is based on genetic algorithm, which offers opportunity to simplify the process of tuning parameters and further improve the noise performance. The proposed Sigma-Delta modulator is able to quickly and efficiently design high performance, high order, closed loop that are robust to sensor fabrication tolerances. Simulation results with respect to the proposed Sigma-Delta modulator, SNR>122dB and the noise floor under -170dB are obtained in frequency range of [5-150Hz]. In further simulation, the robustness of the proposed Sigma-Delta modulator is analyzed.

Grey Wolf based control for speed ripple reduction at low speed operation of PMSM drives.

PubMed

Djerioui, Ali; Houari, Azeddine; Ait-Ahmed, Mourad; Benkhoris, Mohamed-Fouad; Chouder, Aissa; Machmoum, Mohamed

2018-03-01

Speed ripple at low speed-high torque operation of Permanent Magnet Synchronous Machine (PMSM) drives is considered as one of the major issues to be treated. The presented work proposes an efficient PMSM speed controller based on Grey Wolf (GW) algorithm to ensure a high-performance control for speed ripple reduction at low speed operation. The main idea of the proposed control algorithm is to propose a specific objective function in order to incorporate the advantage of fast optimization process of the GW optimizer. The role of GW optimizer is to find the optimal input controls that satisfy the speed tracking requirements. The synthesis methodology of the proposed control algorithm is detailed and the feasibility and performances of the proposed speed controller is confirmed by simulation and experimental results. The GW algorithm is a model-free controller and the parameters of its objective function are easy to be tuned. The GW controller is compared to PI one on real test bench. Then, the superiority of the first algorithm is highlighted. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Learning and tuning fuzzy logic controllers through reinforcements

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.; Khedkar, Pratap

1992-01-01

This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

Parameter estimation by Differential Search Algorithm from horizontal loop electromagnetic (HLEM) data

NASA Astrophysics Data System (ADS)

Alkan, Hilal; Balkaya, Çağlayan

2018-02-01

We present an efficient inversion tool for parameter estimation from horizontal loop electromagnetic (HLEM) data using Differential Search Algorithm (DSA) which is a swarm-intelligence-based metaheuristic proposed recently. The depth, dip, and origin of a thin subsurface conductor causing the anomaly are the parameters estimated by the HLEM method commonly known as Slingram. The applicability of the developed scheme was firstly tested on two synthetically generated anomalies with and without noise content. Two control parameters affecting the convergence characteristic to the solution of the algorithm were tuned for the so-called anomalies including one and two conductive bodies, respectively. Tuned control parameters yielded more successful statistical results compared to widely used parameter couples in DSA applications. Two field anomalies measured over a dipping graphitic shale from Northern Australia were then considered, and the algorithm provided the depth estimations being in good agreement with those of previous studies and drilling information. Furthermore, the efficiency and reliability of the results obtained were investigated via probability density function. Considering the results obtained, we can conclude that DSA characterized by the simple algorithmic structure is an efficient and promising metaheuristic for the other relatively low-dimensional geophysical inverse problems. Finally, the researchers after being familiar with the content of developed scheme displaying an easy to use and flexible characteristic can easily modify and expand it for their scientific optimization problems.

Magnetic induction of hyperthermia by a modified self-learning fuzzy temperature controller

NASA Astrophysics Data System (ADS)

Wang, Wei-Cheng; Tai, Cheng-Chi

2017-07-01

The aim of this study involved developing a temperature controller for magnetic induction hyperthermia (MIH). A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The MIH system generated an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating had few side effects when it was extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with a gain tuning mechanism was implemented to obtain high control performance in a wide range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. In this study, the proposed SLFLC was compared to the classical self-tuning fuzzy logic controller and fuzzy model reference learning control. Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicated that the proposed controller showed greater robustness and excellent adaptability with respect to the temperature control of the MIH system.

Target Capturing Control for Space Robots with Unknown Mass Properties: A Self-Tuning Method Based on Gyros and Cameras.

PubMed

Li, Zhenyu; Wang, Bin; Liu, Hong

2016-08-30

Satellite capturing with free-floating space robots is still a challenging task due to the non-fixed base and unknown mass property issues. In this paper gyro and eye-in-hand camera data are adopted as an alternative choice for solving this problem. For this improved system, a new modeling approach that reduces the complexity of system control and identification is proposed. With the newly developed model, the space robot is equivalent to a ground-fixed manipulator system. Accordingly, a self-tuning control scheme is applied to handle such a control problem including unknown parameters. To determine the controller parameters, an estimator is designed based on the least-squares technique for identifying the unknown mass properties in real time. The proposed method is tested with a credible 3-dimensional ground verification experimental system, and the experimental results confirm the effectiveness of the proposed control scheme.

Target Capturing Control for Space Robots with Unknown Mass Properties: A Self-Tuning Method Based on Gyros and Cameras

PubMed Central

Li, Zhenyu; Wang, Bin; Liu, Hong

2016-01-01

Satellite capturing with free-floating space robots is still a challenging task due to the non-fixed base and unknown mass property issues. In this paper gyro and eye-in-hand camera data are adopted as an alternative choice for solving this problem. For this improved system, a new modeling approach that reduces the complexity of system control and identification is proposed. With the newly developed model, the space robot is equivalent to a ground-fixed manipulator system. Accordingly, a self-tuning control scheme is applied to handle such a control problem including unknown parameters. To determine the controller parameters, an estimator is designed based on the least-squares technique for identifying the unknown mass properties in real time. The proposed method is tested with a credible 3-dimensional ground verification experimental system, and the experimental results confirm the effectiveness of the proposed control scheme. PMID:27589748

A satellite digital controller or 'play that PID tune again, Sam'. [Position, Integral, Derivative feedback control algorithm for design strategy

NASA Technical Reports Server (NTRS)

Seltzer, S. M.

1976-01-01

The problem discussed is to design a digital controller for a typical satellite. The controlled plant is considered to be a rigid body acting in a plane. The controller is assumed to be a digital computer which, when combined with the proposed control algorithm, can be represented as a sampled-data system. The objective is to present a design strategy and technique for selecting numerical values for the control gains (assuming position, integral, and derivative feedback) and the sample rate. The technique is based on the parameter plane method and requires that the system be amenable to z-transform analysis.

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.

A global bioheat model with self-tuning optimal regulation of body temperature using Hebbian feedback covariance learning.

PubMed

Ong, M L; Ng, E Y K

2005-12-01

In the lower brain, body temperature is continually being regulated almost flawlessly despite huge fluctuations in ambient and physiological conditions that constantly threaten the well-being of the body. The underlying control problem defining thermal homeostasis is one of great enormity: Many systems and sub-systems are involved in temperature regulation and physiological processes are intrinsically complex and intertwined. Thus the defining control system has to take into account the complications of nonlinearities, system uncertainties, delayed feedback loops as well as internal and external disturbances. In this paper, we propose a self-tuning adaptive thermal controller based upon Hebbian feedback covariance learning where the system is to be regulated continually to best suit its environment. This hypothesis is supported in part by postulations of the presence of adaptive optimization behavior in biological systems of certain organisms which face limited resources vital for survival. We demonstrate the use of Hebbian feedback covariance learning as a possible self-adaptive controller in body temperature regulation. The model postulates an important role of Hebbian covariance adaptation as a means of reinforcement learning in the thermal controller. The passive system is based on a simplified 2-node core and shell representation of the body, where global responses are captured. Model predictions are consistent with observed thermoregulatory responses to conditions of exercise and rest, and heat and cold stress. An important implication of the model is that optimal physiological behaviors arising from self-tuning adaptive regulation in the thermal controller may be responsible for the departure from homeostasis in abnormal states, e.g., fever. This was previously unexplained using the conventional "set-point" control theory.

Self-Tuning Impact Damper for Rotating Blades

NASA Technical Reports Server (NTRS)

Pufy, Kirsten P. (Inventor); Brown, Gerald V. (Inventor); Bagley, Ronald L. (Inventor)

2004-01-01

A self-tuning impact damper is disclosed that absorbs and dissipates vibration energy in the blades of rotors in compressors and/or turbines thereby dramatically extending their service life and operational readiness. The self-tuning impact damper uses the rotor speed to tune the resonant frequency of a rattling mass to an engine order excitation frequency. The rating mass dissipates energy through collisions between the rattling mass and the walls of a cavity of the self-tuning impact damper, as well as though friction between the rattling mass and the base of the cavity. In one embodiment, the self-tuning impact damper has a ball-in-trough configuration with tire ball serving as the rattling mass.

Optimal placement of tuning masses on truss structures by genetic algorithms

NASA Technical Reports Server (NTRS)

Ponslet, Eric; Haftka, Raphael T.; Cudney, Harley H.

1993-01-01

Optimal placement of tuning masses, actuators and other peripherals on large space structures is a combinatorial optimization problem. This paper surveys several techniques for solving this problem. The genetic algorithm approach to the solution of the placement problem is described in detail. An example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms. The relative efficiencies of different codings are compared using the results of a large number of optimization runs.

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

Baart, T. A.; Vandersypen, L. M. K.; Kavli Institute of Nanoscience, Delft University of Technology, P.O. Box 5046, 2600 GA Delft

We report the computer-automated tuning of gate-defined semiconductor double quantum dots in GaAs heterostructures. We benchmark the algorithm by creating three double quantum dots inside a linear array of four quantum dots. The algorithm sets the correct gate voltages for all the gates to tune the double quantum dots into the single-electron regime. The algorithm only requires (1) prior knowledge of the gate design and (2) the pinch-off value of the single gate T that is shared by all the quantum dots. This work significantly alleviates the user effort required to tune multiple quantum dot devices.

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.

Testing a simple control law to reduce broadband frequency harmonic vibrations using semi-active tuned mass dampers

NASA Astrophysics Data System (ADS)

Moutinho, Carlos

2015-05-01

This paper is focused on the control problems related to semi-active tuned mass dampers (TMDs) used to reduce harmonic vibrations, specially involving civil structures. A simplified version of the phase control law is derived and its effectiveness is investigated and evaluated. The objective is to improve the functioning of control systems of this type by simplifying the measurement process and reducing the number of variables involved, making the control system more feasible and reliable. Because the control law is of ON/OFF type, combined with appropriate trigger conditions, the activity of the actuation system may be significantly reduced, which may be of few seconds a day in many practical cases, increasing the durability of the device and reducing its maintenance. Moreover, due to the ability of the control system to command the motion of the inertial mass, the semi-active TMD is relatively insensitive to its initial tuning, resulting in the capability of self-tuning and in the possibility of controlling several vibration modes of a structure over a significant broadband frequency.

Scanning electron microscope fine tuning using four-bar piezoelectric actuated mechanism

NASA Astrophysics Data System (ADS)

Hatamleh, Khaled S.; Khasawneh, Qais A.; Al-Ghasem, Adnan; Jaradat, Mohammad A.; Sawaqed, Laith; Al-Shabi, Mohammad

2018-01-01

Scanning Electron Microscopes are extensively used for accurate micro/nano images exploring. Several strategies have been proposed to fine tune those microscopes in the past few years. This work presents a new fine tuning strategy of a scanning electron microscope sample table using four bar piezoelectric actuated mechanisms. The introduced paper presents an algorithm to find all possible inverse kinematics solutions of the proposed mechanism. In addition, another algorithm is presented to search for the optimal inverse kinematic solution. Both algorithms are used simultaneously by means of a simulation study to fine tune a scanning electron microscope sample table through a pre-specified circular or linear path of motion. Results of the study shows that, proposed algorithms were able to minimize the power required to drive the piezoelectric actuated mechanism by a ratio of 97.5% for all simulated paths of motion when compared to general non-optimized solution.

Enhanced pid vs model predictive control applied to bldc motor

NASA Astrophysics Data System (ADS)

Gaya, M. S.; Muhammad, Auwal; Aliyu Abdulkadir, Rabiu; Salim, S. N. S.; Madugu, I. S.; Tijjani, Aminu; Aminu Yusuf, Lukman; Dauda Umar, Ibrahim; Khairi, M. T. M.

2018-01-01

BrushLess Direct Current (BLDC) motor is a multivariable and highly complex nonlinear system. Variation of internal parameter values with environment or reference signal increases the difficulty in controlling the BLDC effectively. Advanced control strategies (like model predictive control) often have to be integrated to satisfy the control desires. Enhancing or proper tuning of a conventional algorithm results in achieving the desired performance. This paper presents a performance comparison of Enhanced PID and Model Predictive Control (MPC) applied to brushless direct current motor. The simulation results demonstrated that the PSO-PID is slightly better than the PID and MPC in tracking the trajectory of the reference signal. The proposed scheme could be useful algorithms for the system.

Trajectory following and stabilization control of fully actuated AUV using inverse kinematics and self-tuning fuzzy PID.

PubMed

Hammad, Mohanad M; Elshenawy, Ahmed K; El Singaby, M I

2017-01-01

In this work a design for self-tuning non-linear Fuzzy Proportional Integral Derivative (FPID) controller is presented to control position and speed of Multiple Input Multiple Output (MIMO) fully-actuated Autonomous Underwater Vehicles (AUV) to follow desired trajectories. Non-linearity that results from the hydrodynamics and the coupled AUV dynamics makes the design of a stable controller a very difficult task. In this study, the control scheme in a simulation environment is validated using dynamic and kinematic equations for the AUV model and hydrodynamic damping equations. An AUV configuration with eight thrusters and an inverse kinematic model from a previous work is utilized in the simulation. In the proposed controller, Mamdani fuzzy rules are used to tune the parameters of the PID. Nonlinear fuzzy Gaussian membership functions are selected to give better performance and response in the non-linear system. A control architecture with two feedback loops is designed such that the inner loop is for velocity control and outer loop is for position control. Several test scenarios are executed to validate the controller performance including different complex trajectories with and without injection of ocean current disturbances. A comparison between the proposed FPID controller and the conventional PID controller is studied and shows that the FPID controller has a faster response to the reference signal and more stable behavior in a disturbed non-linear environment.

Trajectory following and stabilization control of fully actuated AUV using inverse kinematics and self-tuning fuzzy PID

PubMed Central

Elshenawy, Ahmed K.; El Singaby, M.I.

2017-01-01

In this work a design for self-tuning non-linear Fuzzy Proportional Integral Derivative (FPID) controller is presented to control position and speed of Multiple Input Multiple Output (MIMO) fully-actuated Autonomous Underwater Vehicles (AUV) to follow desired trajectories. Non-linearity that results from the hydrodynamics and the coupled AUV dynamics makes the design of a stable controller a very difficult task. In this study, the control scheme in a simulation environment is validated using dynamic and kinematic equations for the AUV model and hydrodynamic damping equations. An AUV configuration with eight thrusters and an inverse kinematic model from a previous work is utilized in the simulation. In the proposed controller, Mamdani fuzzy rules are used to tune the parameters of the PID. Nonlinear fuzzy Gaussian membership functions are selected to give better performance and response in the non-linear system. A control architecture with two feedback loops is designed such that the inner loop is for velocity control and outer loop is for position control. Several test scenarios are executed to validate the controller performance including different complex trajectories with and without injection of ocean current disturbances. A comparison between the proposed FPID controller and the conventional PID controller is studied and shows that the FPID controller has a faster response to the reference signal and more stable behavior in a disturbed non-linear environment. PMID:28683071

Linear Quadratic Gaussian Controller Design Using a Graphical User Interface: Application to the Beam-Waveguide Antennas

NASA Astrophysics Data System (ADS)

Maneri, E.; Gawronski, W.

1999-10-01

The linear quadratic Gaussian (LQG) design algorithms described in [2] and [5] have been used in the controller design of JPL's beam-waveguide [5] and 70-m [6] antennas. This algorithm significantly improves tracking precision in a windy environment. This article describes the graphical user interface (GUI) software for the design LQG controllers. It consists of two parts: the basic LQG design and the fine-tuning of the basic design using a constrained optimization algorithm. The presented GUI was developed to simplify the design process, to make the design process user-friendly, and to enable design of an LQG controller for one with a limited control engineering background. The user is asked to manipulate the GUI sliders and radio buttons to watch the antenna performance. Simple rules are given at the GUI display.

Light-Directed Tuning of Plasmon Resonances via Plasmon-Induced Polymerization Using Hot Electrons

PubMed Central

2017-01-01

The precise morphology of nanoscale gaps between noble-metal nanostructures controls their resonant wavelengths. Here we show photocatalytic plasmon-induced polymerization can locally enlarge the gap size and tune the plasmon resonances. We demonstrate light-directed programmable tuning of plasmons can be self-limiting. Selective control of polymer growth around individual plasmonic nanoparticles is achieved, with simultaneous real-time monitoring of the polymerization process in situ using dark-field spectroscopy. Even without initiators present, we show light-triggered chain growth of various monomers, implying plasmon initiation of free radicals via hot-electron transfer to monomers at the Au surface. This concept not only provides a programmable way to fine-tune plasmons for many applications but also provides a window on polymer chemistry at the sub-nanoscale. PMID:28670601

Residential roof condition assessment system using deep learning

NASA Astrophysics Data System (ADS)

Wang, Fan; Kerekes, John P.; Xu, Zhuoyi; Wang, Yandong

2018-01-01

The emergence of high resolution (HR) and ultra high resolution (UHR) airborne remote sensing imagery is enabling humans to move beyond traditional land cover analysis applications to the detailed characterization of surface objects. A residential roof condition assessment method using techniques from deep learning is presented. The proposed method operates on individual roofs and divides the task into two stages: (1) roof segmentation, followed by (2) condition classification of the segmented roof regions. As the first step in this process, a self-tuning method is proposed to segment the images into small homogeneous areas. The segmentation is initialized with simple linear iterative clustering followed by deep learned feature extraction and region merging, with the optimal result selected by an unsupervised index, Q. After the segmentation, a pretrained residual network is fine-tuned on the augmented roof segments using a proposed k-pixel extension technique for classification. The effectiveness of the proposed algorithm was demonstrated on both HR and UHR imagery collected by EagleView over different study sites. The proposed algorithm has yielded promising results and has outperformed traditional machine learning methods using hand-crafted features.

An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems.

PubMed

Nandola, Naresh N; Rivera, Daniel E

2013-01-01

We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty.

An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems

PubMed Central

Nandola, Naresh N.; Rivera, Daniel E.

2013-01-01

We consider an improved model predictive control (MPC) formulation for linear hybrid systems described by mixed logical dynamical (MLD) models. The algorithm relies on a multiple-degree-of-freedom parametrization that enables the user to adjust the speed of setpoint tracking, measured disturbance rejection and unmeasured disturbance rejection independently in the closed-loop system. Consequently, controller tuning is more flexible and intuitive than relying on objective function weights (such as move suppression) traditionally used in MPC schemes. The controller formulation is motivated by the needs of non-traditional control applications that are suitably described by hybrid production-inventory systems. Two applications are considered in this paper: adaptive, time-varying interventions in behavioral health, and inventory management in supply chains under conditions of limited capacity. In the adaptive intervention application, a hypothetical intervention inspired by the Fast Track program, a real-life preventive intervention for reducing conduct disorder in at-risk children, is examined. In the inventory management application, the ability of the algorithm to judiciously alter production capacity under conditions of varying demand is presented. These case studies demonstrate that MPC for hybrid systems can be tuned for desired performance under demanding conditions involving noise and uncertainty. PMID:24348004

Resonance frequency control of RF normal conducting cavity using gradient estimator of reflected power

NASA Astrophysics Data System (ADS)

Leewe, R.; Shahriari, Z.; Moallem, M.

2017-10-01

Control of the natural resonance frequency of an RF cavity is essential for accelerator structures due to their high cavity sensitivity to internal and external vibrations and the dependency of resonant frequency on temperature changes. Due to the relatively high radio frequencies involved (MHz to GHz), direct measurement of the resonant frequency for real-time control is not possible by using conventional microcontroller hardware. So far, all operational cavities are tuned using phase comparison techniques. The temperature dependent phase measurements render this technique labor and time intensive. To eliminate the phase measurement, reduce man hours and speed up cavity start up time, this paper presents a control theme that relies solely on the reflected power measurement. The control algorithm for the nonlinear system is developed through Lyapunov's method. The controller stabilizes the resonance frequency of the cavity using a nonlinear control algorithm in combination with a gradient estimation method. Experimental results of the proposed system on a test cavity show that the resonance frequency can be tuned to its optimum operating point while the start up time of a single cavity and the accompanied man hours are significantly decreased. A test result of the fully commissioned control system on one of TRIUMF's DTL tanks verifies its performance under real environmental conditions.

A soft computing-based approach to optimise queuing-inventory control problem

NASA Astrophysics Data System (ADS)

Alaghebandha, Mohammad; Hajipour, Vahid

2015-04-01

In this paper, a multi-product continuous review inventory control problem within batch arrival queuing approach (MQr/M/1) is developed to find the optimal quantities of maximum inventory. The objective function is to minimise summation of ordering, holding and shortage costs under warehouse space, service level and expected lost-sales shortage cost constraints from retailer and warehouse viewpoints. Since the proposed model is Non-deterministic Polynomial-time hard, an efficient imperialist competitive algorithm (ICA) is proposed to solve the model. To justify proposed ICA, both ganetic algorithm and simulated annealing algorithm are utilised. In order to determine the best value of algorithm parameters that result in a better solution, a fine-tuning procedure is executed. Finally, the performance of the proposed ICA is analysed using some numerical illustrations.

Adaptive Self-Tuning Networks

NASA Astrophysics Data System (ADS)

Knox, H. A.; Draelos, T.; Young, C. J.; Lawry, B.; Chael, E. P.; Faust, A.; Peterson, M. G.

2015-12-01

The quality of automatic detections from seismic sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. Yet, achieving superior automatic detection of seismic events is closely related to these parameters. We present an automated sensor tuning (AST) system that learns near-optimal parameter settings for each event type using neuro-dynamic programming (reinforcement learning) trained with historic data. AST learns to test the raw signal against all event-settings and automatically self-tunes to an emerging event in real-time. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Reducing false alarms early in the seismic pipeline processing will have a significant impact on this goal. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections in seismic waveforms from a network of stations, we show that AST increases the probability of detection while decreasing false alarms.

Bioengineered-inorganic nanosystems for nanophotonics and bio-nanotechnology

NASA Astrophysics Data System (ADS)

Leong, Kirsty; Zin, Melvin T.; Ma, Hong; Huang, Fei; Sarikaya, Mehmet; Jen, Alex K.

2008-08-01

Here we nanoengineered tunable quantum dot and cationic conjugated polymer nanoarrays based on surface plasmon enhanced fluorescence where we achieved a 15-fold and 25-fold increase in their emission intensities, respectively. These peptide mediated hybrid systems were fabricated by horizontally tuning the localized surface plasmon resonance of gold nanoarrays and laterally tuning the distance of the fluorophore from the metal surface. This approach permits a comprehensive control both laterally (i.e., lithographically defined gold nanoarrays) and vertically (i.e., QD/CCP-metal distance) of the collectively behaving QD-NP and CP-NP assemblies by way of biomolecular recognition. The highest photoluminescence was achieved when the quantum dots and cationic conjugated polymers were self-assembled at a distance of 16.00 nm and 18.50 nm from the metal surface, respectively. Specifically, we demonstrated the spectral tuning of plasmon resonant metal nanoarrays and the self-assembly of protein-functionalized QDs/CCPs in a step-wise fashion with a concomitant incremental increase in separation from the metal surface through biotin-streptavidin spacer units. These well-controlled self-assembled patterned arrays provide highly organized architectures for improving optoelectronic devices and/or increasing the sensitivity of bio-chemical sensors.

Self-tuning pressure-feedback control by pole placement for vibration reduction of excavator with independent metering fluid power system

NASA Astrophysics Data System (ADS)

Ding, Ruqi; Xu, Bing; Zhang, Junhui; Cheng, Min

2017-08-01

Independent metering control systems are promising fluid power technologies compared with traditional valve controlled systems. By breaking the mechanical coupling between the inlet and outlet, the meter-out valve can open as large as possible to reduce energy consumptions. However, the lack of damping in outlet causes stronger vibrations. To address the problem, the paper designs a hybrid control method combining dynamic pressure-feedback and active damping control. The innovation resides in the optimization of damping by introducing pressure feedback to make trade-offs between high stability and fast response. To achieve this goal, the dynamic response pertaining to the control parameters consisting of feedback gain and cut-off frequency, are analyzed via pole-zero locations. Accordingly, these parameters are tuned online in terms of guaranteed dominant pole placement such that the optimal damping can be accurately captured under a considerable variation of operating conditions. The experiment is deployed in a mini-excavator. The results pertaining to different control parameters confirm the theoretical expectations via pole-zero locations. By using proposed self-tuning controller, the vibrations are almost eliminated after only one overshoot for different operation conditions. The overshoots are also reduced with less decrease of the response time. In addition, the energy-saving capability of independent metering system is still not affected by the improvement of controllability.

A robust hybrid fuzzy-simulated annealing-intelligent water drops approach for tuning a distribution static compensator nonlinear controller in a distribution system

NASA Astrophysics Data System (ADS)

Bagheri Tolabi, Hajar; Hosseini, Rahil; Shakarami, Mahmoud Reza

2016-06-01

This article presents a novel hybrid optimization approach for a nonlinear controller of a distribution static compensator (DSTATCOM). The DSTATCOM is connected to a distribution system with the distributed generation units. The nonlinear control is based on partial feedback linearization. Two proportional-integral-derivative (PID) controllers regulate the voltage and track the output in this control system. In the conventional scheme, the trial-and-error method is used to determine the PID controller coefficients. This article uses a combination of a fuzzy system, simulated annealing (SA) and intelligent water drops (IWD) algorithms to optimize the parameters of the controllers. The obtained results reveal that the response of the optimized controlled system is effectively improved by finding a high-quality solution. The results confirm that using the tuning method based on the fuzzy-SA-IWD can significantly decrease the settling and rising times, the maximum overshoot and the steady-state error of the voltage step response of the DSTATCOM. The proposed hybrid tuning method for the partial feedback linearizing (PFL) controller achieved better regulation of the direct current voltage for the capacitor within the DSTATCOM. Furthermore, in the event of a fault the proposed controller tuned by the fuzzy-SA-IWD method showed better performance than the conventional controller or the PFL controller without optimization by the fuzzy-SA-IWD method with regard to both fault duration and clearing times.

Velocity control of servo systems using an integral retarded algorithm.

PubMed

Ramírez, Adrián; Garrido, Rubén; Mondié, Sabine

2015-09-01

This paper presents a design technique for the delay-based controller called Integral Retarded (IR), and its applications to velocity control of servo systems. Using spectral analysis, the technique yields a tuning strategy for the IR by assigning a triple real dominant root for the closed-loop system. This result ultimately guarantees a desired exponential decay rate σ(d) while achieving the IR tuning as explicit function of σ(d) and system parameters. The intentional introduction of delay allows using noisy velocity measurements without additional filtering. The structure of the controller is also able to avoid velocity measurements by using instead position information. The IR is compared to a classical PI, both tested in a laboratory prototype. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Simulated Annealing-based Optimal Proportional-Integral-Derivative (PID) Controller Design: A Case Study on Nonlinear Quadcopter Dynamics

NASA Astrophysics Data System (ADS)

Nemirsky, Kristofer Kevin

In this thesis, the history and evolution of rotor aircraft with simulated annealing-based PID application were reviewed and quadcopter dynamics are presented. The dynamics of a quadcopter were then modeled, analyzed, and linearized. A cascaded loop architecture with PID controllers was used to stabilize the plant dynamics, which was improved upon through the application of simulated annealing (SA). A Simulink model was developed to test the controllers and verify the functionality of the proposed control system design. In addition, the data that the Simulink model provided were compared with flight data to present the validity of derived dynamics as a proper mathematical model representing the true dynamics of the quadcopter system. Then, the SA-based global optimization procedure was applied to obtain optimized PID parameters. It was observed that the tuned gains through the SA algorithm produced a better performing PID controller than the original manually tuned one. Next, we investigated the uncertain dynamics of the quadcopter setup. After adding uncertainty to the gyroscopic effects associated with pitch-and-roll rate dynamics, the controllers were shown to be robust against the added uncertainty. A discussion follows to summarize SA-based algorithm PID controller design and performance outcomes. Lastly, future work on SA application on multi-input-multi-output (MIMO) systems is briefly discussed.

Fractional order PID controller for improvement of PMSM speed control in aerospace applications

NASA Astrophysics Data System (ADS)

Saraji, Ali Motalebi; Ghanbari, Mahmood

2014-12-01

Because of the benefits reduced size, cost and maintenance, noise, CO2 emissions and increased control flexibility and precision, to meet these expectations, electrical equipment increasingly utilize in modern aircraft systems and aerospace industry rather than conventional mechanic, hydraulic, and pneumatic power systems. Electric motor drives are capable of converting electrical power to drive actuators, pumps, compressors, and other subsystems at variable speeds. In the past decades, permanent magnet synchronous motor (PMSM) and brushless dc (BLDC) motor were investigated for aerospace applications such as aircraft actuators. In this paper, the fractional-order PID controller is used in the design of speed loop of PMSM speed control system. Having more parameters for tuning fractional order PID controller lead to good performance ratio to integer order. This good performance is shown by comparison fractional order PID controller with the conventional PI and tuned PID controller by Genetic algorithm in MATLAB soft wear.

ANUBIS: artificial neuromodulation using a Bayesian inference system.

PubMed

Smith, Benjamin J H; Saaj, Chakravarthini M; Allouis, Elie

2013-01-01

Gain tuning is a crucial part of controller design and depends not only on an accurate understanding of the system in question, but also on the designer's ability to predict what disturbances and other perturbations the system will encounter throughout its operation. This letter presents ANUBIS (artificial neuromodulation using a Bayesian inference system), a novel biologically inspired technique for automatically tuning controller parameters in real time. ANUBIS is based on the Bayesian brain concept and modifies it by incorporating a model of the neuromodulatory system comprising four artificial neuromodulators. It has been applied to the controller of EchinoBot, a prototype walking rover for Martian exploration. ANUBIS has been implemented at three levels of the controller; gait generation, foot trajectory planning using Bézier curves, and foot trajectory tracking using a terminal sliding mode controller. We compare the results to a similar system that has been tuned using a multilayer perceptron. The use of Bayesian inference means that the system retains mathematical interpretability, unlike other intelligent tuning techniques, which use neural networks, fuzzy logic, or evolutionary algorithms. The simulation results show that ANUBIS provides significant improvements in efficiency and adaptability of the three controller components; it allows the robot to react to obstacles and uncertainties faster than the system tuned with the MLP, while maintaining stability and accuracy. As well as advancing rover autonomy, ANUBIS could also be applied to other situations where operating conditions are likely to change or cannot be accurately modeled in advance, such as process control. In addition, it demonstrates one way in which neuromodulation could fit into the Bayesian brain framework.

Open architecture CMM motion controller

NASA Astrophysics Data System (ADS)

Chang, David; Spence, Allan D.; Bigg, Steve; Heslip, Joe; Peterson, John

2001-12-01

Although initially the only Coordinate Measuring Machine (CMM) sensor available was a touch trigger probe, technological advances in sensors and computing have greatly increased the variety of available inspection sensors. Non-contact laser digitizers and analog scanning touch probes require very well tuned CMM motion control, as well as an extensible, open architecture interface. This paper describes the implementation of a retrofit CMM motion controller designed for open architecture interface to a variety of sensors. The controller is based on an Intel Pentium microcomputer and a Servo To Go motion interface electronics card. Motor amplifiers, safety, and additional interface electronics are housed in a separate enclosure. Host Signal Processing (HSP) is used for the motion control algorithm. Compared to the usual host plus DSP architecture, single CPU HSP simplifies integration with the various sensors, and implementation of software geometric error compensation. Motion control tuning is accomplished using a remote computer via 100BaseTX Ethernet. A Graphical User Interface (GUI) is used to enter geometric error compensation data, and to optimize the motion control tuning parameters. It is shown that this architecture achieves the required real time motion control response, yet is much easier to extend to additional sensors.

Controllers, observers, and applications thereof

NASA Technical Reports Server (NTRS)

Gao, Zhiqiang (Inventor); Zhou, Wankun (Inventor); Miklosovic, Robert (Inventor); Radke, Aaron (Inventor); Zheng, Qing (Inventor)

2011-01-01

Controller scaling and parameterization are described. Techniques that can be improved by employing the scaling and parameterization include, but are not limited to, controller design, tuning and optimization. The scaling and parameterization methods described here apply to transfer function based controllers, including PID controllers. The parameterization methods also apply to state feedback and state observer based controllers, as well as linear active disturbance rejection (ADRC) controllers. Parameterization simplifies the use of ADRC. A discrete extended state observer (DESO) and a generalized extended state observer (GESO) are described. They improve the performance of the ESO and therefore ADRC. A tracking control algorithm is also described that improves the performance of the ADRC controller. A general algorithm is described for applying ADRC to multi-input multi-output systems. Several specific applications of the control systems and processes are disclosed.

Optimum Parameters of a Tuned Liquid Column Damper in a Wind Turbine Subject to Stochastic Load

NASA Astrophysics Data System (ADS)

Alkmim, M. H.; de Morais, M. V. G.; Fabro, A. T.

2017-12-01

Parameter optimization for tuned liquid column dampers (TLCD), a class of passive structural control, have been previously proposed in the literature for reducing vibration in wind turbines, and several other applications. However, most of the available work consider the wind excitation as either a deterministic harmonic load or random load with white noise spectra. In this paper, a global direct search optimization algorithm to reduce vibration of a tuned liquid column damper (TLCD), a class of passive structural control device, is presented. The objective is to find optimized parameters for the TLCD under stochastic load from different wind power spectral density. A verification is made considering the analytical solution of undamped primary system under white noise excitation by comparing with result from the literature. Finally, it is shown that different wind profiles can significantly affect the optimum TLCD parameters.

Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV.

PubMed

Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

2016-05-09

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.

A CPS Based Optimal Operational Control System for Fused Magnesium Furnace

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

Chai, Tian-you; Wu, Zhi-wei; Wang, Hong

Fused magnesia smelting for fused magnesium furnace (FMF) is an energy intensive process with high temperature and comprehensive complexities. Its operational index namely energy consumption per ton (ECPT) is defined as the consumed electrical energy per ton of acceptable quality and is difficult to measure online. Moreover, the dynamics of ECPT cannot be precisely modelled mathematically. The model parameters of the three-phase currents of the electrodes such as the molten pool level, its variation rate and resistance are uncertain and nonlinear functions of the changes in both the smelting process and the raw materials composition. In this paper, an integratedmore » optimal operational control algorithm proposed is composed of a current set-point control, a current switching control and a self-optimized tuning mechanism. The tight conjoining of and coordination between the computational resources including the integrated optimal operational control, embedded software, industrial cloud, wireless communication and the physical resources of FMF constitutes a cyber-physical system (CPS) based embedded optimal operational control system. Successful application of this system has been made for a production line with ten fused magnesium furnaces in a factory in China, leading to a significant reduced ECPT.« less

Comparison of Low-Thrust Control Laws for Application in Planetocentric Space

NASA Technical Reports Server (NTRS)

Falck, Robert D.; Sjauw, Waldy K.; Smith, David A.

2014-01-01

Recent interest at NASA for the application of solar electric propulsion for the transfer of significant payloads in cislunar space has led to the development of high-fidelity simulations of such missions. With such transfers involving transfer times on the order of months, simulation time can be significant. In the past, the examination of such missions typically began with the use of lower-fidelity trajectory optimization tools such as SEPSPOT to develop and tune guidance laws which delivered optimal or near- optimal trajectories, where optimal is generally defined as minimizing propellant expenditure or time of flight. The transfer of these solutions to a high-fidelity simulation is typically an iterative process whereby the initial solution may nearly, but not precisely, meet mission objectives. Further tuning of the guidance algorithm is typically necessary when accounting for high-fidelity perturbations such as those due to more detailed gravity models, secondary-body effects, solar radiation pressure, etc. While trajectory optimization is a useful method for determining optimal performance metrics, algorithms which deliver nearly optimal performance with minimal tuning are an attractive alternative.

Naturally Inspired Firefly Controller For Stabilization Of Double Inverted Pendulum

NASA Astrophysics Data System (ADS)

Srikanth, Kavirayani; Nagesh, Gundavarapu

2015-12-01

A double inverted pendulum plant as an established model that is analyzed as part of this work was tested under the influence of time delay, where the controller was fine tuned using a firefly algorithm taking into considering the fitness function of variation of the cart position and to minimize the cart position displacement and still stabilize it effectively. The naturally inspired algorithm which imitates the fireflies definitely is an energy efficient method owing to the inherent logic of the way the fireflies respond collectively and has shown that critical time delays makes the system healthy.

A novel gene network inference algorithm using predictive minimum description length approach.

PubMed

Chaitankar, Vijender; Ghosh, Preetam; Perkins, Edward J; Gong, Ping; Deng, Youping; Zhang, Chaoyang

2010-05-28

Reverse engineering of gene regulatory networks using information theory models has received much attention due to its simplicity, low computational cost, and capability of inferring large networks. One of the major problems with information theory models is to determine the threshold which defines the regulatory relationships between genes. The minimum description length (MDL) principle has been implemented to overcome this problem. The description length of the MDL principle is the sum of model length and data encoding length. A user-specified fine tuning parameter is used as control mechanism between model and data encoding, but it is difficult to find the optimal parameter. In this work, we proposed a new inference algorithm which incorporated mutual information (MI), conditional mutual information (CMI) and predictive minimum description length (PMDL) principle to infer gene regulatory networks from DNA microarray data. In this algorithm, the information theoretic quantities MI and CMI determine the regulatory relationships between genes and the PMDL principle method attempts to determine the best MI threshold without the need of a user-specified fine tuning parameter. The performance of the proposed algorithm was evaluated using both synthetic time series data sets and a biological time series data set for the yeast Saccharomyces cerevisiae. The benchmark quantities precision and recall were used as performance measures. The results show that the proposed algorithm produced less false edges and significantly improved the precision, as compared to the existing algorithm. For further analysis the performance of the algorithms was observed over different sizes of data. We have proposed a new algorithm that implements the PMDL principle for inferring gene regulatory networks from time series DNA microarray data that eliminates the need of a fine tuning parameter. The evaluation results obtained from both synthetic and actual biological data sets show that the PMDL principle is effective in determining the MI threshold and the developed algorithm improves precision of gene regulatory network inference. Based on the sensitivity analysis of all tested cases, an optimal CMI threshold value has been identified. Finally it was observed that the performance of the algorithms saturates at a certain threshold of data size.

Optimized PID control of depth of hypnosis in anesthesia.

PubMed

Padula, Fabrizio; Ionescu, Clara; Latronico, Nicola; Paltenghi, Massimiliano; Visioli, Antonio; Vivacqua, Giulio

2017-06-01

This paper addresses the use of proportional-integral-derivative controllers for regulating the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable. In fact, introducing an automatic control system might provide significant benefits for the patient in reducing the risk for under- and over-dosing. In this study, the controller parameters are obtained through genetic algorithms by solving a min-max optimization problem. A set of 12 patient models representative of a large population variance is used to test controller robustness. The worst-case performance in the considered population is minimized considering two different scenarios: the induction case and the maintenance case. Our results indicate that including a gain scheduling strategy enables optimal performance for induction and maintenance phases, separately. Using a single tuning to address both tasks may results in a loss of performance up to 102% in the induction phase and up to 31% in the maintenance phase. Further on, it is shown that a suitably designed low-pass filter on the controller output can handle the trade-off between the performance and the noise effect in the control variable. Optimally tuned PID controllers provide a fast induction time with an acceptable overshoot and a satisfactory disturbance rejection performance during maintenance. These features make them a very good tool for comparison when other control algorithms are developed. Copyright © 2017 Elsevier B.V. All rights reserved.

Robotic excavator trajectory control using an improved GA based PID controller

NASA Astrophysics Data System (ADS)

Feng, Hao; Yin, Chen-Bo; Weng, Wen-wen; Ma, Wei; Zhou, Jun-jing; Jia, Wen-hua; Zhang, Zi-li

2018-05-01

In order to achieve excellent trajectory tracking performances, an improved genetic algorithm (IGA) is presented to search for the optimal proportional-integral-derivative (PID) controller parameters for the robotic excavator. Firstly, the mathematical model of kinematic and electro-hydraulic proportional control system of the excavator are analyzed based on the mechanism modeling method. On this basis, the actual model of the electro-hydraulic proportional system are established by the identification experiment. Furthermore, the population, the fitness function, the crossover probability and mutation probability of the SGA are improved: the initial PID parameters are calculated by the Ziegler-Nichols (Z-N) tuning method and the initial population is generated near it; the fitness function is transformed to maintain the diversity of the population; the probability of crossover and mutation are adjusted automatically to avoid premature convergence. Moreover, a simulation study is carried out to evaluate the time response performance of the proposed controller, i.e., IGA based PID against the SGA and Z-N based PID controllers with a step signal. It was shown from the simulation study that the proposed controller provides the least rise time and settling time of 1.23 s and 1.81 s, respectively against the other tested controllers. Finally, two types of trajectories are designed to validate the performances of the control algorithms, and experiments are performed on the excavator trajectory control experimental platform. It was demonstrated from the experimental work that the proposed IGA based PID controller improves the trajectory accuracy of the horizontal line and slope line trajectories by 23.98% and 23.64%, respectively in comparison to the SGA tuned PID controller. The results further indicate that the proposed IGA tuning based PID controller is effective for improving the tracking accuracy, which may be employed in the trajectory control of an actual excavator.

Fuzzy logic-based flight control system design

NASA Astrophysics Data System (ADS)

Nho, Kyungmoon

The application of fuzzy logic to aircraft motion control is studied in this dissertation. The self-tuning fuzzy techniques are developed by changing input scaling factors to obtain a robust fuzzy controller over a wide range of operating conditions and nonlinearities for a nonlinear aircraft model. It is demonstrated that the properly adjusted input scaling factors can meet the required performance and robustness in a fuzzy controller. For a simple demonstration of the easy design and control capability of a fuzzy controller, a proportional-derivative (PD) fuzzy control system is compared to the conventional controller for a simple dynamical system. This thesis also describes the design principles and stability analysis of fuzzy control systems by considering the key features of a fuzzy control system including the fuzzification, rule-base and defuzzification. The wing-rock motion of slender delta wings, a linear aircraft model and the six degree of freedom nonlinear aircraft dynamics are considered to illustrate several self-tuning methods employing change in input scaling factors. Finally, this dissertation is concluded with numerical simulation of glide-slope capture in windshear demonstrating the robustness of the fuzzy logic based flight control system.

PID Tuning Using Extremum Seeking

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

Killingsworth, N; Krstic, M

2005-11-15

Although proportional-integral-derivative (PID) controllers are widely used in the process industry, their effectiveness is often limited due to poor tuning. Manual tuning of PID controllers, which requires optimization of three parameters, is a time-consuming task. To remedy this difficulty, much effort has been invested in developing systematic tuning methods. Many of these methods rely on knowledge of the plant model or require special experiments to identify a suitable plant model. Reviews of these methods are given in [1] and the survey paper [2]. However, in many situations a plant model is not known, and it is not desirable to openmore » the process loop for system identification. Thus a method for tuning PID parameters within a closed-loop setting is advantageous. In relay feedback tuning [3]-[5], the feedback controller is temporarily replaced by a relay. Relay feedback causes most systems to oscillate, thus determining one point on the Nyquist diagram. Based on the location of this point, PID parameters can be chosen to give the closed-loop system a desired phase and gain margin. An alternative tuning method, which does not require either a modification of the system or a system model, is unfalsified control [6], [7]. This method uses input-output data to determine whether a set of PID parameters meets performance specifications. An adaptive algorithm is used to update the PID controller based on whether or not the controller falsifies a given criterion. The method requires a finite set of candidate PID controllers that must be initially specified [6]. Unfalsified control for an infinite set of PID controllers has been developed in [7]; this approach requires a carefully chosen input signal [8]. Yet another model-free PID tuning method that does not require opening of the loop is iterative feedback tuning (IFT). IFT iteratively optimizes the controller parameters with respect to a cost function derived from the output signal of the closed-loop system, see [9]. This method is based on the performance of the closed-loop system during a step response experiment [10], [11]. In this article we present a method for optimizing the step response of a closed-loop system consisting of a PID controller and an unknown plant with a discrete version of extremum seeking (ES). Specifically, ES is used to minimize a cost function similar to that used in [10], [11], which quantifies the performance of the PID controller. ES, a non-model-based method, iteratively modifies the arguments (in this application the PID parameters) of a cost function so that the output of the cost function reaches a local minimum or local maximum. In the next section we apply ES to PID controller tuning. We illustrate this technique through simulations comparing the effectiveness of ES to other PID tuning methods. Next, we address the importance of the choice of cost function and consider the effect of controller saturation. Furthermore, we discuss the choice of ES tuning parameters. Finally, we offer some conclusions.« less

Data-based virtual unmodeled dynamics driven multivariable nonlinear adaptive switching control.

PubMed

Chai, Tianyou; Zhang, Yajun; Wang, Hong; Su, Chun-Yi; Sun, Jing

2011-12-01

For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is difficult to handle by the traditional controller designs around their operating points. This paper, however, explores the concepts of controller-driven model and virtual unmodeled dynamics to propose a new design framework. The design consists of two controllers with distinct functions. First, using input and output data, a self-tuning controller is constructed based on a linear controller-driven model. Then the output signals of the controller-driven model are compared with the true outputs of the system to produce so-called virtual unmodeled dynamics. Based on the compensator of the virtual unmodeled dynamics, the second controller based on a nonlinear controller-driven model is proposed. Those two controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features: one offers stabilization function and another provides improved performance. The conditions on the stability and convergence of the closed-loop system are analyzed. Both simulation and experimental tests on a heavily coupled nonlinear twin-tank system are carried out to confirm the effectiveness of the proposed method.

Mode tuning of a simplified string instrument using time-dimensionless state-derivative control

NASA Astrophysics Data System (ADS)

Benacchio, Simon; Chomette, Baptiste; Mamou-Mani, Adrien; Finel, Victor

2015-01-01

In recent years, there has been a growing interest in smart structures, particularly in the field of musical acoustics. Control methods, initially developed to reduce vibration and damage, can be a good way to shift modal parameters of a structure in order to modify its dynamic response. This study focuses on smart musical instruments and aims to modify their radiated sound. This is achieved by controlling the modal parameters of the soundboard of a simplified string instrument. A method combining a pole placement algorithm and a time-dimensionless state-derivative control is used and quickly compared to a usual state control method. Then the effect of the mode tuning on the coupling between the string and the soundboard is experimentally studied. Controlling two vibration modes of the soundboard, its acoustic response and the damping of the third partial of the sound are modified. Finally these effects are listened in the radiated sound.

Parameter optimization of an inerter-based isolator for passive vibration control of Michelangelo's Rondanini Pietà

NASA Astrophysics Data System (ADS)

Siami, A.; Karimi, H. R.; Cigada, A.; Zappa, E.; Sabbioni, E.

2018-01-01

Preserving cultural heritage against earthquake and ambient vibrations can be an attractive topic in the field of vibration control. This paper proposes a passive vibration isolator methodology based on inerters for improving the performance of the isolation system of the famous statue of Michelangelo Buonarroti Pietà Rondanini. More specifically, a five-degree-of-freedom (5DOF) model of the statue and the anti-seismic and anti-vibration base is presented and experimentally validated. The parameters of this model are tuned according to the experimental tests performed on the assembly of the isolator and the structure. Then, the developed model is used to investigate the impact of actuation devices such as tuned mass-damper (TMD) and tuned mass-damper-inerter (TMDI) in vibration reduction of the structure. The effect of implementation of TMDI on the 5DOF model is shown based on physical limitations of the system parameters. Simulation results are provided to illustrate effectiveness of the passive element of TMDI in reduction of the vibration transmitted to the statue in vertical direction. Moreover, the optimal design parameters of the passive system such as frequency and damping coefficient will be calculated using two different performance indexes. The obtained optimal parameters have been evaluated by using two different optimization algorithms: the sequential quadratic programming method and the Firefly algorithm. The results prove significant reduction in the transmitted vibration to the structure in the presence of the proposed tuned TMDI, without imposing a large amount of mass or modification to the structure of the isolator.

Broadband external cavity quantum cascade laser based sensor for gasoline detection

NASA Astrophysics Data System (ADS)

Ding, Junya; He, Tianbo; Zhou, Sheng; Li, Jinsong

2018-02-01

A new type of tunable diode spectroscopy sensor based on an external cavity quantum cascade laser (ECQCL) and a quartz crystal tuning fork (QCTF) were used for quantitative analysis of volatile organic compounds. In this work, the sensor system had been tested on different gasoline sample analysis. For signal processing, the self-established interpolation algorithm and multiple linear regression algorithm model were used for quantitative analysis of major volatile organic compounds in gasoline samples. The results were very consistent with that of the standard spectra taken from the Pacific Northwest National Laboratory (PNNL) database. In future, The ECQCL sensor will be used for trace explosive, chemical warfare agent, and toxic industrial chemical detection and spectroscopic analysis, etc.

Modal characterization of the ASCIE segmented optics testbed: New algorithms and experimental results

NASA Technical Reports Server (NTRS)

Carrier, Alain C.; Aubrun, Jean-Noel

1993-01-01

New frequency response measurement procedures, on-line modal tuning techniques, and off-line modal identification algorithms are developed and applied to the modal identification of the Advanced Structures/Controls Integrated Experiment (ASCIE), a generic segmented optics telescope test-bed representative of future complex space structures. The frequency response measurement procedure uses all the actuators simultaneously to excite the structure and all the sensors to measure the structural response so that all the transfer functions are measured simultaneously. Structural responses to sinusoidal excitations are measured and analyzed to calculate spectral responses. The spectral responses in turn are analyzed as the spectral data become available and, which is new, the results are used to maintain high quality measurements. Data acquisition, processing, and checking procedures are fully automated. As the acquisition of the frequency response progresses, an on-line algorithm keeps track of the actuator force distribution that maximizes the structural response to automatically tune to a structural mode when approaching a resonant frequency. This tuning is insensitive to delays, ill-conditioning, and nonproportional damping. Experimental results show that is useful for modal surveys even in high modal density regions. For thorough modeling, a constructive procedure is proposed to identify the dynamics of a complex system from its frequency response with the minimization of a least-squares cost function as a desirable objective. This procedure relies on off-line modal separation algorithms to extract modal information and on least-squares parameter subset optimization to combine the modal results and globally fit the modal parameters to the measured data. The modal separation algorithms resolved modal density of 5 modes/Hz in the ASCIE experiment. They promise to be useful in many challenging applications.

On the estimation algorithm used in adaptive performance optimization of turbofan engines

NASA Technical Reports Server (NTRS)

Espana, Martin D.; Gilyard, Glenn B.

1993-01-01

The performance seeking control algorithm is designed to continuously optimize the performance of propulsion systems. The performance seeking control algorithm uses a nominal model of the propulsion system and estimates, in flight, the engine deviation parameters characterizing the engine deviations with respect to nominal conditions. In practice, because of measurement biases and/or model uncertainties, the estimated engine deviation parameters may not reflect the engine's actual off-nominal condition. This factor has a necessary impact on the overall performance seeking control scheme exacerbated by the open-loop character of the algorithm. The effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the performance seeking control algorithm to an F100 engine. An equivalence relation between the biases and engine deviation parameters stems from an observability study; therefore, it is undecided whether the estimated engine deviation parameters represent the actual engine deviation or whether they simply reflect the measurement biases. A new algorithm, based on the engine's (steady-state) optimization model, is proposed and tested with flight data. When compared with previous Kalman filter schemes, based on local engine dynamic models, the new algorithm is easier to design and tune and it reduces the computational burden of the onboard computer.

Precision tuning of InAs quantum dot emission wavelength by iterative laser annealing

NASA Astrophysics Data System (ADS)

Dubowski, Jan J.; Stanowski, Radoslaw; Dalacu, Dan; Poole, Philip J.

2018-07-01

Controlling the emission wavelength of quantum dots (QDs) over large surface area wafers is challenging to achieve directly through epitaxial growth methods. We have investigated an innovative post growth laser-based tuning procedure of the emission of self-assembled InAs QDs grown epitaxially on InP (001). A targeted blue shift of the emission is achieved with a series of iterative steps, with photoluminescence diagnostics employed between the steps to monitor the result of intermixing. We demonstrate tuning of the emission wavelength of ensembles of QDs to within approximately ±1 nm, while potentially better precision should be achievable for tuning the emission of individual QDs.

Self-organized magnetic particles to tune the mechanical behavior of a granular system

NASA Astrophysics Data System (ADS)

Cox, Meredith; Wang, Dong; Barés, Jonathan; Behringer, Robert P.

2016-09-01

Above a certain density a granular material jams. This property can be controlled by either tuning a global property, such as the packing fraction or by applying shear strain, or at the micro-scale by tuning grain shape, inter-particle friction or externally controlled organization. Here, we introduce a novel way to change a local granular property by adding a weak anisotropic magnetic interaction between particles. We measure the evolution of the pressure, P, and coordination number, Z, for a packing of 2D photo-elastic disks, subject to uniaxial compression. A fraction R m of the particles have embedded cuboidal magnets. The strength of the magnetic interactions between particles is too weak to have a strong direct effect on P or Z when the system is jammed. However, the magnetic interactions play an important role in the evolution of latent force networks when systems containing a large enough fraction of the particles with magnets are driven through unjammed to jammed states. In this case, a statistically stable network of magnetic chains self-organizes before jamming and overlaps with force chains once jamming occurs, strengthening the granular medium. This property opens a novel way to control mechanical properties of granular materials.

All optical reconfiguration of optomechanical filters.

PubMed

Deotare, Parag B; Bulu, Irfan; Frank, Ian W; Quan, Qimin; Zhang, Yinan; Ilic, Rob; Loncar, Marko

2012-05-22

Reconfigurable optical filters are of great importance for applications in optical communication and information processing. Of particular interest are tuning techniques that take advantage of mechanical deformation of the devices, as they offer wider tuning range. Here we demonstrate reconfiguration of coupled photonic crystal nanobeam cavities by using optical gradient force induced mechanical actuation. Propagating waveguide modes that exist over a wide wavelength range are used to actuate the structures and control the resonance of localized cavity modes. Using this all-optical approach, more than 18 linewidths of tuning range is demonstrated. Using an on-chip temperature self-referencing method, we determine that 20% of the total tuning was due to optomechanical reconfiguration and the rest due to thermo-optic effects. By operating the device at frequencies higher than the thermal cutoff, we show high-speed operation dominated by just optomechanical effects. Independent control of mechanical and optical resonances of our structures is also demonstrated.

Improved Intrapulse Raman Scattering Control via Asymmetric Airy Pulses

NASA Astrophysics Data System (ADS)

Hu, Yi; Tehranchi, Amirhossein; Wabnitz, Stefan; Kashyap, Raman; Chen, Zhigang; Morandotti, Roberto

2015-02-01

We experimentally demonstrate the possibility of tuning the frequency of a laser pulse via the use of an Airy pulse-seeded soliton self-frequency shift. The intrinsically asymmetric nature of Airy pulses, typically featured by either leading or trailing oscillatory tails (relatively to the main lobe), is revealed through the nonlinear generation of both a primary and a secondary Raman soliton self-frequency shift, a phenomenon which is driven by the soliton fission processes. The resulting frequency shift can be carefully controlled by using time-reversed Airy pulses or, alternatively, by applying an offset to the cubic phase modulation used to generate the pulses. When compared with the use of conventional chirped Gaussian pulses, our technique brings about unique advantages in terms of both efficient frequency tuning and feasibility, along with the generation and control of multicolor Raman solitons with enhanced tunability. Our theoretical analysis agrees well with our experimental observations.

Experimental evaluation of leaky least-mean-square algorithms for active noise reduction in communication headsets.

PubMed

Cartes, David A; Ray, Laura R; Collier, Robert D

2002-04-01

An adaptive leaky normalized least-mean-square (NLMS) algorithm has been developed to optimize stability and performance of active noise cancellation systems. The research addresses LMS filter performance issues related to insufficient excitation, nonstationary noise fields, and time-varying signal-to-noise ratio. The adaptive leaky NLMS algorithm is based on a Lyapunov tuning approach in which three candidate algorithms, each of which is a function of the instantaneous measured reference input, measurement noise variance, and filter length, are shown to provide varying degrees of tradeoff between stability and noise reduction performance. Each algorithm is evaluated experimentally for reduction of low frequency noise in communication headsets, and stability and noise reduction performance are compared with that of traditional NLMS and fixed-leakage NLMS algorithms. Acoustic measurements are made in a specially designed acoustic test cell which is based on the original work of Ryan et al. ["Enclosure for low frequency assessment of active noise reducing circumaural headsets and hearing protection," Can. Acoust. 21, 19-20 (1993)] and which provides a highly controlled and uniform acoustic environment. The stability and performance of the active noise reduction system, including a prototype communication headset, are investigated for a variety of noise sources ranging from stationary tonal noise to highly nonstationary measured F-16 aircraft noise over a 20 dB dynamic range. Results demonstrate significant improvements in stability of Lyapunov-tuned LMS algorithms over traditional leaky or nonleaky normalized algorithms, while providing noise reduction performance equivalent to that of the NLMS algorithm for idealized noise fields.

Data-driven approach of CUSUM algorithm in temporal aberrant event detection using interactive web applications.

PubMed

Li, Ye; Whelan, Michael; Hobbs, Leigh; Fan, Wen Qi; Fung, Cecilia; Wong, Kenny; Marchand-Austin, Alex; Badiani, Tina; Johnson, Ian

2016-06-27

In 2014/2015, Public Health Ontario developed disease-specific, cumulative sum (CUSUM)-based statistical algorithms for detecting aberrant increases in reportable infectious disease incidence in Ontario. The objective of this study was to determine whether the prospective application of these CUSUM algorithms, based on historical patterns, have improved specificity and sensitivity compared to the currently used Early Aberration Reporting System (EARS) algorithm, developed by the US Centers for Disease Control and Prevention. A total of seven algorithms were developed for the following diseases: cyclosporiasis, giardiasis, influenza (one each for type A and type B), mumps, pertussis, invasive pneumococcal disease. Historical data were used as baseline to assess known outbreaks. Regression models were used to model seasonality and CUSUM was applied to the difference between observed and expected counts. An interactive web application was developed allowing program staff to directly interact with data and tune the parameters of CUSUM algorithms using their expertise on the epidemiology of each disease. Using these parameters, a CUSUM detection system was applied prospectively and the results were compared to the outputs generated by EARS. The outcome was the detection of outbreaks, or the start of a known seasonal increase and predicting the peak in activity. The CUSUM algorithms detected provincial outbreaks earlier than the EARS algorithm, identified the start of the influenza season in advance of traditional methods, and had fewer false positive alerts. Additionally, having staff involved in the creation of the algorithms improved their understanding of the algorithms and improved use in practice. Using interactive web-based technology to tune CUSUM improved the sensitivity and specificity of detection algorithms.

PID-controller with predictor and auto-tuning algorithm: study of efficiency for thermal plants

NASA Astrophysics Data System (ADS)

Kuzishchin, V. F.; Merzlikina, E. I.; Hoang, Van Va

2017-09-01

The problem of efficiency estimation of an automatic control system (ACS) with a Smith predictor and PID-algorithm for thermal plants is considered. In order to use the predictor, it is proposed to include an auto-tuning module (ATC) into the controller; the module calculates parameters for a second-order plant module with a time delay. The study was conducted using programmable logical controllers (PLC), one of which performed control, ATC, and predictor functions. A simulation model was used as a control plant, and there were two variants of the model: one of them was built on the basis of a separate PLC, and the other was a physical model of a thermal plant in the form of an electrical heater. Analysis of the efficiency of the ACS with the predictor was carried out for several variants of the second order plant model with time delay, and the analysis was performed on the basis of the comparison of transient processes in the system when the set point was changed and when a disturbance influenced the control plant. The recommendations are given on correction of the PID-algorithm parameters when the predictor is used by means of using the correcting coefficient k for the PID parameters. It is shown that, when the set point is changed, the use of the predictor is effective taking into account the parameters correction with k = 2. When the disturbances influence the plant, the use of the predictor is doubtful, because the transient process is too long. The reason for this is that, in the neighborhood of the zero frequency, the amplitude-frequency characteristic (AFC) of the system with the predictor has an ascent in comparison with the AFC of the system without the predictor.

Embedded intelligent adaptive PI controller for an electromechanical system.

PubMed

El-Nagar, Ahmad M

2016-09-01

In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

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.

A cable-driven wrist robotic rehabilitator using a novel torque-field controller for human motion training.

PubMed

Chen, Weihai; Cui, Xiang; Zhang, Jianbin; Wang, Jianhua

2015-06-01

Rehabilitation technologies have great potentials in assisted motion training for stroke patients. Considering that wrist motion plays an important role in arm dexterous manipulation of activities of daily living, this paper focuses on developing a cable-driven wrist robotic rehabilitator (CDWRR) for motion training or assistance to subjects with motor disabilities. The CDWRR utilizes the wrist skeletal joints and arm segments as the supporting structure and takes advantage of cable-driven parallel design to build the system, which brings the properties of flexibility, low-cost, and low-weight. The controller of the CDWRR is designed typically based on a virtual torque-field, which is to plan "assist-as-needed" torques for the spherical motion of wrist responding to the orientation deviation in wrist motion training. The torque-field controller can be customized to different levels of rehabilitation training requirements by tuning the field parameters. Additionally, a rapidly convergent parameter self-identification algorithm is developed to obtain the uncertain parameters automatically for the floating wearable structure of the CDWRR. Finally, experiments on a healthy subject are carried out to demonstrate the performance of the controller and the feasibility of the CDWRR on wrist motion training or assistance.

A cable-driven wrist robotic rehabilitator using a novel torque-field controller for human motion training

NASA Astrophysics Data System (ADS)

Chen, Weihai; Cui, Xiang; Zhang, Jianbin; Wang, Jianhua

2015-06-01

Rehabilitation technologies have great potentials in assisted motion training for stroke patients. Considering that wrist motion plays an important role in arm dexterous manipulation of activities of daily living, this paper focuses on developing a cable-driven wrist robotic rehabilitator (CDWRR) for motion training or assistance to subjects with motor disabilities. The CDWRR utilizes the wrist skeletal joints and arm segments as the supporting structure and takes advantage of cable-driven parallel design to build the system, which brings the properties of flexibility, low-cost, and low-weight. The controller of the CDWRR is designed typically based on a virtual torque-field, which is to plan "assist-as-needed" torques for the spherical motion of wrist responding to the orientation deviation in wrist motion training. The torque-field controller can be customized to different levels of rehabilitation training requirements by tuning the field parameters. Additionally, a rapidly convergent parameter self-identification algorithm is developed to obtain the uncertain parameters automatically for the floating wearable structure of the CDWRR. Finally, experiments on a healthy subject are carried out to demonstrate the performance of the controller and the feasibility of the CDWRR on wrist motion training or assistance.

Meta-Learning Approach for Automatic Parameter Tuning: A Case Study with Educational Datasets

ERIC Educational Resources Information Center

Molina, M. M.; Luna, J. M.; Romero, C.; Ventura, S.

2012-01-01

This paper proposes to the use of a meta-learning approach for automatic parameter tuning of a well-known decision tree algorithm by using past information about algorithm executions. Fourteen educational datasets were analysed using various combinations of parameter values to examine the effects of the parameter values on accuracy classification.…

A Self Adaptive Differential Evolution Algorithm for Global Optimization

NASA Astrophysics Data System (ADS)

Kumar, Pravesh; Pant, Millie

This paper presents a new Differential Evolution algorithm based on hybridization of adaptive control parameters and trigonometric mutation. First we propose a self adaptive DE named ADE where choice of control parameter F and Cr is not fixed at some constant value but is taken iteratively. The proposed algorithm is further modified by applying trigonometric mutation in it and the corresponding algorithm is named as ATDE. The performance of ATDE is evaluated on the set of 8 benchmark functions and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical result shows the competence of the proposed algorithm.

Fractional order PID controller for improvement of PMSM speed control in aerospace applications

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

Saraji, Ali Motalebi; Ghanbari, Mahmood

Because of the benefits reduced size, cost and maintenance, noise, CO2 emissions and increased control flexibility and precision, to meet these expectations, electrical equipment increasingly utilize in modern aircraft systems and aerospace industry rather than conventional mechanic, hydraulic, and pneumatic power systems. Electric motor drives are capable of converting electrical power to drive actuators, pumps, compressors, and other subsystems at variable speeds. In the past decades, permanent magnet synchronous motor (PMSM) and brushless dc (BLDC) motor were investigated for aerospace applications such as aircraft actuators. In this paper, the fractional-order PID controller is used in the design of speed loopmore » of PMSM speed control system. Having more parameters for tuning fractional order PID controller lead to good performance ratio to integer order. This good performance is shown by comparison fractional order PID controller with the conventional PI and tuned PID controller by Genetic algorithm in MATLAB soft wear.« less

Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV

PubMed Central

Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

2016-01-01

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability. PMID:27171084

An expert system to perform on-line controller restructuring for abrupt model changes

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.

1990-01-01

Work in progress on an expert system used to reconfigure and tune airframe/engine control systems on-line in real time in response to battle damage or structural failures is presented. The closed loop system is monitored constantly for changes in structure and performance, the detection of which prompts the expert system to choose and apply a particular control restructuring algorithm based on the type and severity of the damage. Each algorithm is designed to handle specific types of failures and each is applicable only in certain situations. The expert system uses information about the system model to identify the failure and to select the technique best suited to compensate for it. A depth-first search is used to find a solution. Once a new controller is designed and implemented it must be tuned to recover the original closed-loop handling qualities and responsiveness from the degraded system. Ideally, the pilot should not be able to tell the difference between the original and redesigned systems. The key is that the system must have inherent redundancy so that degraded or missing capabilities can be restored by creative use of alternate functionalities. With enough redundancy in the control system, minor battle damage affecting individual control surfaces or actuators, compressor efficiency, etc., can be compensated for such that the closed-loop performance in not noticeably altered. The work is applied to a Black Hawk/T700 system.

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight.

PubMed

Kumar, Manjeet; Rawat, Tarun Kumar; Aggarwal, Apoorva

2017-03-01

In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Dynamic control and information processing in chemical reaction systems by tuning self-organization behavior

NASA Astrophysics Data System (ADS)

Lebiedz, Dirk; Brandt-Pollmann, Ulrich

2004-09-01

Specific external control of chemical reaction systems and both dynamic control and signal processing as central functions in biochemical reaction systems are important issues of modern nonlinear science. For example nonlinear input-output behavior and its regulation are crucial for the maintainance of the life process that requires extensive communication between cells and their environment. An important question is how the dynamical behavior of biochemical systems is controlled and how they process information transmitted by incoming signals. But also from a general point of view external forcing of complex chemical reaction processes is important in many application areas ranging from chemical engineering to biomedicine. In order to study such control issues numerically, here, we choose a well characterized chemical system, the CO oxidation on Pt(110), which is interesting per se as an externally forced chemical oscillator model. We show numerically that tuning of temporal self-organization by input signals in this simple nonlinear chemical reaction exhibiting oscillatory behavior can in principle be exploited for both specific external control of dynamical system behavior and processing of complex information.

Tunable self-organization of nanocomposite multilayers

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

Chen, C. Q.; Pei, Y. T.; Shaha, K. P.

In this letter we report the controlled growth and microstructural evolution of self-assembled nanocomposite multilayers that are induced by surface ion-impingement. The nanoscale structures together with chemical composition, especially at the growing front, have been investigated with high-resolution transmission electron microscopy. Concurrent ion impingement of growing films produces an amorphous capping layer 3 nm in thickness where spatially modulated phase separation is initiated. It is shown that the modulation of multilayers as controlled by the self-organization of nanocrystallites below the capping layer, can be tuned through the entire film.

A self-fitting hearing aid: need and concept.

PubMed

Convery, Elizabeth; Keidser, Gitte; Dillon, Harvey; Hartley, Lisa

2011-12-01

The need for reliable access to hearing health care services is growing globally, particularly in developing countries and in remotely located, underserved regions in many parts of the developed world. Individuals with hearing loss in these areas are at a significant disadvantage due to the scarcity of local hearing health care professionals and the high cost of hearing aids. Current approaches to making hearing rehabilitation services more readily available to underserved populations include teleaudiology and the provision of amplification devices outside of the traditional provider-client relationship. Both strategies require access to such resources as dedicated equipment and/or specially trained staff. Another possible strategy is a self-fitting hearing aid, a personal amplification device that is equipped with an onboard tone generator to enable user-controlled, automated, in situ audiometry; an onboard prescription to determine the initial hearing aid settings; and a trainable algorithm to enable user-controlled fine-tuning. The device is thus assembled, fitted, and managed by the user without the need for audiological or computer support. This article details the self-fitting concept and its potential application in both developing and developed countries. Potential advantages and disadvantages of such a device are discussed, and considerations for further investigations into the concept are presented. Overall, the concept is considered technologically viable with the main challenges anticipated to be development of clear, simple user instructions and a delivery model that ensures reliable supplies of instant-fit ear tips and batteries.

A Self-Fitting Hearing Aid

PubMed Central

Keidser, Gitte; Dillon, Harvey; Hartley, Lisa

2011-01-01

The need for reliable access to hearing health care services is growing globally, particularly in developing countries and in remotely located, underserved regions in many parts of the developed world. Individuals with hearing loss in these areas are at a significant disadvantage due to the scarcity of local hearing health care professionals and the high cost of hearing aids. Current approaches to making hearing rehabilitation services more readily available to underserved populations include teleaudiology and the provision of amplification devices outside of the traditional provider-client relationship. Both strategies require access to such resources as dedicated equipment and/or specially trained staff. Another possible strategy is a self-fitting hearing aid, a personal amplification device that is equipped with an onboard tone generator to enable user-controlled, automated, in situ audiometry; an onboard prescription to determine the initial hearing aid settings; and a trainable algorithm to enable user-controlled fine-tuning. The device is thus assembled, fitted, and managed by the user without the need for audiological or computer support. This article details the self-fitting concept and its potential application in both developing and developed countries. Potential advantages and disadvantages of such a device are discussed, and considerations for further investigations into the concept are presented. Overall, the concept is considered technologically viable with the main challenges anticipated to be development of clear, simple user instructions and a delivery model that ensures reliable supplies of instant-fit ear tips and batteries. PMID:22143873

Coexisting synchronous and asynchronous states in locally coupled array of oscillators by partial self-feedback control

NASA Astrophysics Data System (ADS)

Bera, Bidesh K.; Ghosh, Dibakar; Parmananda, Punit; Osipov, G. V.; Dana, Syamal K.

2017-07-01

We report the emergence of coexisting synchronous and asynchronous subpopulations of oscillators in one dimensional arrays of identical oscillators by applying a self-feedback control. When a self-feedback is applied to a subpopulation of the array, similar to chimera states, it splits into two/more sub-subpopulations coexisting in coherent and incoherent states for a range of self-feedback strength. By tuning the coupling between the nearest neighbors and the amount of self-feedback in the perturbed subpopulation, the size of the coherent and the incoherent sub-subpopulations in the array can be controlled, although the exact size of them is unpredictable. We present numerical evidence using the Landau-Stuart system and the Kuramoto-Sakaguchi phase model.

Advisory Algorithm for Scheduling Open Sectors, Operating Positions, and Workstations

NASA Technical Reports Server (NTRS)

Bloem, Michael; Drew, Michael; Lai, Chok Fung; Bilimoria, Karl D.

2012-01-01

Air traffic controller supervisors configure available sector, operating position, and work-station resources to safely and efficiently control air traffic in a region of airspace. In this paper, an algorithm for assisting supervisors with this task is described and demonstrated on two sample problem instances. The algorithm produces configuration schedule advisories that minimize a cost. The cost is a weighted sum of two competing costs: one penalizing mismatches between configurations and predicted air traffic demand and another penalizing the effort associated with changing configurations. The problem considered by the algorithm is a shortest path problem that is solved with a dynamic programming value iteration algorithm. The cost function contains numerous parameters. Default values for most of these are suggested based on descriptions of air traffic control procedures and subject-matter expert feedback. The parameter determining the relative importance of the two competing costs is tuned by comparing historical configurations with corresponding algorithm advisories. Two sample problem instances for which appropriate configuration advisories are obvious were designed to illustrate characteristics of the algorithm. Results demonstrate how the algorithm suggests advisories that appropriately utilize changes in airspace configurations and changes in the number of operating positions allocated to each open sector. The results also demonstrate how the advisories suggest appropriate times for configuration changes.

Self-tuning bandpass filter

NASA Technical Reports Server (NTRS)

Deboo, G. J.; Hedlund, R. C. (Inventor)

1973-01-01

An electronic filter is described which simultaneously maintains a constant bandwidth and a constant center frequency gain as the input signal frequency varies, and remains self-tuning to that center frequency over a decade range. The filter utilizes a field effect transistor (FET) as a voltage variable resistance in the bandpass frequency determining circuit. The FET is responsive to a phase detector to achieve self-tuning.

Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy.

PubMed

Liu, Hui; Li, Yingzi; Zhang, Yingxu; Chen, Yifu; Song, Zihang; Wang, Zhenyu; Zhang, Suoxin; Qian, Jianqiang

2018-01-01

Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to the sample topography. This method gets enough information about the output signals of PID controller and tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve good tracking performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sequential Insertion Heuristic with Adaptive Bee Colony Optimisation Algorithm for Vehicle Routing Problem with Time Windows

PubMed Central

Jawarneh, Sana; Abdullah, Salwani

2015-01-01

This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon’s 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results. PMID:26132158

Predictive minimum description length principle approach to inferring gene regulatory networks.

PubMed

Chaitankar, Vijender; Zhang, Chaoyang; Ghosh, Preetam; Gong, Ping; Perkins, Edward J; Deng, Youping

2011-01-01

Reverse engineering of gene regulatory networks using information theory models has received much attention due to its simplicity, low computational cost, and capability of inferring large networks. One of the major problems with information theory models is to determine the threshold that defines the regulatory relationships between genes. The minimum description length (MDL) principle has been implemented to overcome this problem. The description length of the MDL principle is the sum of model length and data encoding length. A user-specified fine tuning parameter is used as control mechanism between model and data encoding, but it is difficult to find the optimal parameter. In this work, we propose a new inference algorithm that incorporates mutual information (MI), conditional mutual information (CMI), and predictive minimum description length (PMDL) principle to infer gene regulatory networks from DNA microarray data. In this algorithm, the information theoretic quantities MI and CMI determine the regulatory relationships between genes and the PMDL principle method attempts to determine the best MI threshold without the need of a user-specified fine tuning parameter. The performance of the proposed algorithm is evaluated using both synthetic time series data sets and a biological time series data set (Saccharomyces cerevisiae). The results show that the proposed algorithm produced fewer false edges and significantly improved the precision when compared to existing MDL algorithm.

A two-phase control algorithm for gear-shifting in a novel multi-speed transmission for electric vehicles

NASA Astrophysics Data System (ADS)

Roozegar, M.; Angeles, J.

2018-05-01

In light of the current low energy-storage capacity of electric batteries, multi-speed transmissions (MSTs) are being considered for applications in electric vehicles (EVs), since MSTs decrease the energy consumption of the EV via gear-shifting. Nonetheless, swiftness and seamlessness are the major concerns in gear-shifting. This study focuses on developing a gear-shifting control scheme for a novel MST designed for EVs. The main advantages of the proposed MST are simplicity and modularity. Firstly, the dynamics model of the transmission is formulated. Then, a two-phase algorithm is proposed for shifting between each two gear ratios, which guarantees a smooth and swift shift. In other words, a separate control set is applied for shifting between each gear pair, which includes two independent PID controllers, tuned using trial-and-error and a genetic algorithm (GA), for the two steps of the algorithm and a switch. A supervisory controller is also employed to choose the proper PID gains, called PID gain-scheduling. Simulation results for various controllers and conditions are reported and compared, indicating that the proposed scheme is highly promising for a desired gear-shifting even in the presence of an unknown external disturbance.

Model-Based Control of a Nonlinear Aircraft Engine Simulation using an Optimal Tuner Kalman Filter Approach

NASA Technical Reports Server (NTRS)

Connolly, Joseph W.; Csank, Jeffrey Thomas; Chicatelli, Amy; Kilver, Jacob

2013-01-01

This paper covers the development of a model-based engine control (MBEC) methodology featuring a self tuning on-board model applied to an aircraft turbofan engine simulation. Here, the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40k) serves as the MBEC application engine. CMAPSS40k is capable of modeling realistic engine performance, allowing for a verification of the MBEC over a wide range of operating points. The on-board model is a piece-wise linear model derived from CMAPSS40k and updated using an optimal tuner Kalman Filter (OTKF) estimation routine, which enables the on-board model to self-tune to account for engine performance variations. The focus here is on developing a methodology for MBEC with direct control of estimated parameters of interest such as thrust and stall margins. Investigations using the MBEC to provide a stall margin limit for the controller protection logic are presented that could provide benefits over a simple acceleration schedule that is currently used in traditional engine control architectures.

Control of large flexible structures - An experiment on the NASA Mini-Mast facility

NASA Technical Reports Server (NTRS)

Hsieh, Chen; Kim, Jae H.; Liu, Ketao; Zhu, Guoming; Skelton, Robert E.

1991-01-01

The output variance constraint controller design procedure is integrated with model reduction by modal cost analysis. A procedure is given for tuning MIMO controller designs to find the maximal rms performance of the actual system. Controller designs based on a finite-element model of the system are compared with controller designs based on an identified model (obtained using the Q-Markov Cover algorithm). The identified model and the finite-element model led to similar closed-loop performance, when tested in the Mini-Mast facility at NASA Langley.

Rain Rate and DSD Retrievals at Kwajalein Atoll

NASA Astrophysics Data System (ADS)

Wolff, David; Marks, David; Tokay, Ali

2010-05-01

The dual-polarization weather radar on Kwajalein Atoll in the Republic of the Marshall Islands (KPOL) is one of the only full-time (24/7) operational S-band dual-polarimetric (DP) radars in the tropics. Using the DP data from KPOL, as well as data from a Joss-Waldvogel disdrometer on Kwajalein Island, algorithms for quality control, as well as calibration of reflectivity and differential reflectivity have been developed and adapted for application in a near real-time operational environment. Observations during light rain and drizzle show that KPOL measurements (since 2006) meet or exceed quality thresholds for these applications (as determined by consensus of the radar community). While the methodology for development of such applications is well documented, tuning of specific algorithms to a particular regime and observed raindrop size distributions requires a comprehensive testing and adjustment period to ensure high quality products. Upon application of these data quality techniques to the KPOL data, we have tested and compared several different rain retrieval algorithms. These include conventional Z-R, DP hybrid techniques, as well as polarimetrically-tuned Z-R described by Bringi et al. 2004. One of the major benefits of the polarimetrically tuned Z-R technique, is its ability to use the DP observations to retrieve key parameters of the drop size distribution (DSD), such as the median drop diameter, and the intercept and shape parameter of the assumed gammaDSD. We will show several such retrievals for different rain systems, as well as their distribution with height below the melting layer. From a physical validation perspective, such DSD parameter retrievals provide an important means to cross-validate microphysical parameterizations in GPM Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) retrieval algorithms.

Analysis of convergence of an evolutionary algorithm with self-adaptation using a stochastic Lyapunov function.

PubMed

Semenov, Mikhail A; Terkel, Dmitri A

2003-01-01

This paper analyses the convergence of evolutionary algorithms using a technique which is based on a stochastic Lyapunov function and developed within the martingale theory. This technique is used to investigate the convergence of a simple evolutionary algorithm with self-adaptation, which contains two types of parameters: fitness parameters, belonging to the domain of the objective function; and control parameters, responsible for the variation of fitness parameters. Although both parameters mutate randomly and independently, they converge to the "optimum" due to the direct (for fitness parameters) and indirect (for control parameters) selection. We show that the convergence velocity of the evolutionary algorithm with self-adaptation is asymptotically exponential, similar to the velocity of the optimal deterministic algorithm on the class of unimodal functions. Although some martingale inequalities have not be proved analytically, they have been numerically validated with 0.999 confidence using Monte-Carlo simulations.

A synergistic method for vibration suppression of an elevator mechatronic system

NASA Astrophysics Data System (ADS)

Knezevic, Bojan Z.; Blanusa, Branko; Marcetic, Darko P.

2017-10-01

Modern elevators are complex mechatronic systems which have to satisfy high performance in precision, safety and ride comfort. Each elevator mechatronic system (EMS) contains a mechanical subsystem which is characterized by its resonant frequency. In order to achieve high performance of the whole system, the control part of the EMS inevitably excites resonant circuits causing the occurrence of vibration. This paper proposes a synergistic solution based on the jerk control and the upgrade of the speed controller with a band-stop filter to restore lost ride comfort and speed control caused by vibration. The band-stop filter eliminates the resonant component from the speed controller spectra and jerk control provides operating of the speed controller in a linear mode as well as increased ride comfort. The original method for band-stop filter tuning based on Goertzel algorithm and Kiefer search algorithm is proposed in this paper. In order to generate the speed reference trajectory which can be defined by different shapes and amplitudes of jerk, a unique generalized model is proposed. The proposed algorithm is integrated in the power drive control algorithm and implemented on the digital signal processor. Through experimental verifications on a scale down prototype of the EMS it has been verified that only synergistic effect of controlling jerk and filtrating the reference torque can completely eliminate vibrations.

A multiple objective optimization approach to quality control

NASA Technical Reports Server (NTRS)

Seaman, Christopher Michael

1991-01-01

The use of product quality as the performance criteria for manufacturing system control is explored. The goal in manufacturing, for economic reasons, is to optimize product quality. The problem is that since quality is a rather nebulous product characteristic, there is seldom an analytic function that can be used as a measure. Therefore standard control approaches, such as optimal control, cannot readily be applied. A second problem with optimizing product quality is that it is typically measured along many dimensions: there are many apsects of quality which must be optimized simultaneously. Very often these different aspects are incommensurate and competing. The concept of optimality must now include accepting tradeoffs among the different quality characteristics. These problems are addressed using multiple objective optimization. It is shown that the quality control problem can be defined as a multiple objective optimization problem. A controller structure is defined using this as the basis. Then, an algorithm is presented which can be used by an operator to interactively find the best operating point. Essentially, the algorithm uses process data to provide the operator with two pieces of information: (1) if it is possible to simultaneously improve all quality criteria, then determine what changes to the process input or controller parameters should be made to do this; and (2) if it is not possible to improve all criteria, and the current operating point is not a desirable one, select a criteria in which a tradeoff should be made, and make input changes to improve all other criteria. The process is not operating at an optimal point in any sense if no tradeoff has to be made to move to a new operating point. This algorithm ensures that operating points are optimal in some sense and provides the operator with information about tradeoffs when seeking the best operating point. The multiobjective algorithm was implemented in two different injection molding scenarios: tuning of process controllers to meet specified performance objectives and tuning of process inputs to meet specified quality objectives. Five case studies are presented.

Parameter selection in limited data cone-beam CT reconstruction using edge-preserving total variation algorithms

NASA Astrophysics Data System (ADS)

Lohvithee, Manasavee; Biguri, Ander; Soleimani, Manuchehr

2017-12-01

There are a number of powerful total variation (TV) regularization methods that have great promise in limited data cone-beam CT reconstruction with an enhancement of image quality. These promising TV methods require careful selection of the image reconstruction parameters, for which there are no well-established criteria. This paper presents a comprehensive evaluation of parameter selection in a number of major TV-based reconstruction algorithms. An appropriate way of selecting the values for each individual parameter has been suggested. Finally, a new adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm is presented, which implements the edge-preserving function for CBCT reconstruction with limited data. The proposed algorithm shows significant robustness compared to three other existing algorithms: ASD-POCS, AwASD-POCS and PCSD. The proposed AwPCSD algorithm is able to preserve the edges of the reconstructed images better with fewer sensitive parameters to tune.

The Design of a Transparent Vertical Multizone Furnace: Application to Thermal Field Tuning and Crystal Growth

NASA Technical Reports Server (NTRS)

Duvual, Walter M. B.; Batur, Celal; Bennett, Robert J.

1998-01-01

We present an innovative design of a vertical transparent multizone furnace which can operate in the temperature range of 25 C to 750 C and deliver thermal gradients of 2 C/cm to 45 C/cm for the commercial applications to crystal growth. The operation of the eight zone furnace is based on a self-tuning temperature control system with a DC power supply for optimal thermal stability. We show that the desired thermal profile over the entire length of the furnace consists of a functional combination of the fundamental thermal profiles for each individual zone obtained by setting the set-point temperature for that zone. The self-tuning system accounts for the zone to zone thermal interactions. The control system operates such that the thermal profile is maintained under thermal load, thus boundary conditions on crystal growth ampoules can be predetermined prior to crystal growth. Temperature profiles for the growth of crystals via directional solidification, vapor transport techniques, and multiple gradient applications are shown to be easily implemented. The unique feature of its transparency and ease of programming thermal profiles make the furnace useful in scientific and commercial applications for determining the optimized process parameters for crystal growth.

100-mW high-power three-section tunable distributed Bragg reflector laser diodes with a real refractive-index-guided self-aligned structure

NASA Astrophysics Data System (ADS)

Takayama, Toru; Mochida, Atsunori; Orita, Kenji; Tamura, Satoshi; Ohnishi, Toshikazu; Yuri, Masaaki; Shimizu, Hirokazu

2002-05-01

High-power (>100mW) 820 nm-band distributed Bragg reflector (DBR) laser diodes (LDs) with stable fundamental transverse mode operation and continuous wavelength tuning characteristics have been developed. To obtain high-power LDs with a stable fundamental transverse mode in 820 nm wavelength range, an AlGaAs narrow stripe (2.0 micrometers ) real refractive-index-guided self-aligned (RISA) structure is utilized. In the RISA structure, the index step between inside and outside the stripe region ((Delta) n) can be precisely controlled in the order of 10-3). To maintain a stable fundamental transverse mode up to an output power over 100 mW, (Delta) n is designed to be 4x10-3. Higher-order transverse modes are effectively suppressed by a narrow stripe geometry. Further, to achieve continuous wavelength tuning capability, the three-section LD structure, which consists of the active (700micrometers ), phase control (300micrometers ), and DBR(500micrometers ) sections, is incorporated. Our DBR LDs show a maximum output power over 200mW with a stable fundamental transverse mode, and wavelength tuning characteristics ((Delta) (lambda) ~2nm) under 100 mW CW operation.

On a Self-Tuning Impact Vibration Damper for Rotating Turbomachinery

NASA Technical Reports Server (NTRS)

Duffy, Kirsten P.; Bagley, Ronald L.; Mehmed, Oral; Choi, Ben (Technical Monitor)

2000-01-01

A self-tuning impact damper is investigated analytically and experimentally as a device to inhibit vibration and increase the fatigue life of rotating components in turbomachinery. High centrifugal loads in rotors can inactivate traditional impact dampers because of friction or misalignment of the damper in the g-field. Giving an impact damper characteristics of an acceleration tuned-mass damper enables the resulting device to maintain damper mass motion and effectiveness during high-g loading. Experimental results presented here verify that this self-tuning impact damper can be designed to follow an engine order line. damping rotor component resonance crossings.

Bi-dimensional null model analysis of presence-absence binary matrices.

PubMed

Strona, Giovanni; Ulrich, Werner; Gotelli, Nicholas J

2018-01-01

Comparing the structure of presence/absence (i.e., binary) matrices with those of randomized counterparts is a common practice in ecology. However, differences in the randomization procedures (null models) can affect the results of the comparisons, leading matrix structural patterns to appear either "random" or not. Subjectivity in the choice of one particular null model over another makes it often advisable to compare the results obtained using several different approaches. Yet, available algorithms to randomize binary matrices differ substantially in respect to the constraints they impose on the discrepancy between observed and randomized row and column marginal totals, which complicates the interpretation of contrasting patterns. This calls for new strategies both to explore intermediate scenarios of restrictiveness in-between extreme constraint assumptions, and to properly synthesize the resulting information. Here we introduce a new modeling framework based on a flexible matrix randomization algorithm (named the "Tuning Peg" algorithm) that addresses both issues. The algorithm consists of a modified swap procedure in which the discrepancy between the row and column marginal totals of the target matrix and those of its randomized counterpart can be "tuned" in a continuous way by two parameters (controlling, respectively, row and column discrepancy). We show how combining the Tuning Peg with a wise random walk procedure makes it possible to explore the complete null space embraced by existing algorithms. This exploration allows researchers to visualize matrix structural patterns in an innovative bi-dimensional landscape of significance/effect size. We demonstrate the rational and potential of our approach with a set of simulated and real matrices, showing how the simultaneous investigation of a comprehensive and continuous portion of the null space can be extremely informative, and possibly key to resolving longstanding debates in the analysis of ecological matrices. © 2017 The Authors. Ecology, published by Wiley Periodicals, Inc., on behalf of the Ecological Society of America.

Imitative modeling automatic system Control of steam pressure in the main steam collector with the influence on the main Servomotor steam turbine

NASA Astrophysics Data System (ADS)

Andriushin, A. V.; Zverkov, V. P.; Kuzishchin, V. F.; Ryzhkov, O. S.; Sabanin, V. R.

2017-11-01

The research and setting results of steam pressure in the main steam collector “Do itself” automatic control system (ACS) with high-speed feedback on steam pressure in the turbine regulating stage are presented. The ACS setup is performed on the simulation model of the controlled object developed for this purpose with load-dependent static and dynamic characteristics and a non-linear control algorithm with pulse control of the turbine main servomotor. A method for tuning nonlinear ACS with a numerical algorithm for multiparametric optimization and a procedure for separate dynamic adjustment of control devices in a two-loop ACS are proposed and implemented. It is shown that the nonlinear ACS adjusted with the proposed method with the regulators constant parameters ensures reliable and high-quality operation without the occurrence of oscillations in the transient processes the operating range of the turbine loads.

Molecular dynamics of reversible self-healing materials

NASA Astrophysics Data System (ADS)

Madden, Ian; Luijten, Erik

Hydrolyzable polymers have numerous industrial applications as degradable materials. Recent experimental work by Cheng and co-workers has introduced the concept of hindered urea bond (HUB) chemistry to design self-healing systems. Important control parameters are the steric hindrance of the HUB structures, which is used to tune the hydrolytic degradation kinetics, and their density. We employ molecular dynamics simulations of polymeric interfaces to systematically explore the role of these properties in a coarse-grained model, and make direct comparison to experimental data. Our model provides direct insight into the self-healing process, permitting optimization of the control parameters.

Exploiting Fractional Order PID Controller Methods in Improving the Performance of Integer Order PID Controllers: A GA Based Approach

NASA Astrophysics Data System (ADS)

Mukherjee, Bijoy K.; Metia, Santanu

2009-10-01

The paper is divided into three parts. The first part gives a brief introduction to the overall paper, to fractional order PID (PIλDμ) controllers and to Genetic Algorithm (GA). In the second part, first it has been studied how the performance of an integer order PID controller deteriorates when implemented with lossy capacitors in its analog realization. Thereafter it has been shown that the lossy capacitors can be effectively modeled by fractional order terms. Then, a novel GA based method has been proposed to tune the controller parameters such that the original performance is retained even though realized with the same lossy capacitors. Simulation results have been presented to validate the usefulness of the method. Some Ziegler-Nichols type tuning rules for design of fractional order PID controllers have been proposed in the literature [11]. In the third part, a novel GA based method has been proposed which shows how equivalent integer order PID controllers can be obtained which will give performance level similar to those of the fractional order PID controllers thereby removing the complexity involved in the implementation of the latter. It has been shown with extensive simulation results that the equivalent integer order PID controllers more or less retain the robustness and iso-damping properties of the original fractional order PID controllers. Simulation results also show that the equivalent integer order PID controllers are more robust than the normal Ziegler-Nichols tuned PID controllers.

An adaptive control scheme for a flexible manipulator

NASA Technical Reports Server (NTRS)

Yang, T. C.; Yang, J. C. S.; Kudva, P.

1987-01-01

The problem of controlling a single link flexible manipulator is considered. A self-tuning adaptive control scheme is proposed which consists of a least squares on-line parameter identification of an equivalent linear model followed by a tuning of the gains of a pole placement controller using the parameter estimates. Since the initial parameter values for this model are assumed unknown, the use of arbitrarily chosen initial parameter estimates in the adaptive controller would result in undesirable transient effects. Hence, the initial stage control is carried out with a PID controller. Once the identified parameters have converged, control is transferred to the adaptive controller. Naturally, the relevant issues in this scheme are tests for parameter convergence and minimization of overshoots during control switch-over. To demonstrate the effectiveness of the proposed scheme, simulation results are presented with an analytical nonlinear dynamic model of a single link flexible manipulator.

Advanced Control Considerations for Turbofan Engine Design

NASA Technical Reports Server (NTRS)

Connolly, Joseph W.; Csank, Jeffrey T.; Chicatelli, Amy

2016-01-01

This paper covers the application of a model-based engine control (MBEC) methodology featuring a self tuning on-board model for an aircraft turbofan engine simulation. The nonlinear engine model is capable of modeling realistic engine performance, allowing for a verification of the advanced control methodology over a wide range of operating points and life cycle conditions. The on-board model is a piece-wise linear model derived from the nonlinear engine model and updated using an optimal tuner Kalman Filter estimation routine, which enables the on-board model to self-tune to account for engine performance variations. MBEC is used here to show how advanced control architectures can improve efficiency during the design phase of a turbofan engine by reducing conservative operability margins. The operability margins that can be reduced, such as stall margin, can expand the engine design space and offer potential for efficiency improvements. Application of MBEC architecture to a nonlinear engine simulation is shown to reduce the thrust specific fuel consumption by approximately 1% over the baseline design, while maintaining safe operation of the engine across the flight envelope.

Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks.

PubMed

Demirhan, Ayşe; Toru, Mustafa; Guler, Inan

2015-07-01

Robust brain magnetic resonance (MR) segmentation algorithms are critical to analyze tissues and diagnose tumor and edema in a quantitative way. In this study, we present a new tissue segmentation algorithm that segments brain MR images into tumor, edema, white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The detection of the healthy tissues is performed simultaneously with the diseased tissues because examining the change caused by the spread of tumor and edema on healthy tissues is very important for treatment planning. We used T1, T2, and FLAIR MR images of 20 subjects suffering from glial tumor. We developed an algorithm for stripping the skull before the segmentation process. The segmentation is performed using self-organizing map (SOM) that is trained with unsupervised learning algorithm and fine-tuned with learning vector quantization (LVQ). Unlike other studies, we developed an algorithm for clustering the SOM instead of using an additional network. Input feature vector is constructed with the features obtained from stationary wavelet transform (SWT) coefficients. The results showed that average dice similarity indexes are 91% for WM, 87% for GM, 96% for CSF, 61% for tumor, and 77% for edema.

Extremal Optimization: Methods Derived from Co-Evolution

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

Boettcher, S.; Percus, A.G.

1999-07-13

We describe a general-purpose method for finding high-quality solutions to hard optimization problems, inspired by self-organized critical models of co-evolution such as the Bak-Sneppen model. The method, called Extremal Optimization, successively eliminates extremely undesirable components of sub-optimal solutions, rather than ''breeding'' better components. In contrast to Genetic Algorithms which operate on an entire ''gene-pool'' of possible solutions, Extremal Optimization improves on a single candidate solution by treating each of its components as species co-evolving according to Darwinian principles. Unlike Simulated Annealing, its non-equilibrium approach effects an algorithm requiring few parameters to tune. With only one adjustable parameter, its performance provesmore » competitive with, and often superior to, more elaborate stochastic optimization procedures. We demonstrate it here on two classic hard optimization problems: graph partitioning and the traveling salesman problem.« less

Independent tuning of excitonic emission energy and decay time in single semiconductor quantum dots

NASA Astrophysics Data System (ADS)

Höfer, B.; Zhang, J.; Wildmann, J.; Zallo, E.; Trotta, R.; Ding, F.; Rastelli, A.; Schmidt, O. G.

2017-04-01

Independent tuning of emission energy and decay time of neutral excitons confined in single self-assembled In(Ga)As/GaAs quantum dots is achieved by simultaneously employing vertical electric fields and lateral biaxial strain fields. By locking the emission energy via a closed-loop feedback on the piezoelectric actuator used to control the strain in the quantum dot, we continuously decrease the decay time of an exciton from 1.4 to 0.7 ns. Both perturbations are fully electrically controlled and their combination offers a promising route to engineer the indistinguishability of photons emitted from spatially separated single photon sources.

Adaptive control of turbulence intensity is accelerated by frugal flow sampling.

PubMed

Quinn, Daniel B; van Halder, Yous; Lentink, David

2017-11-01

The aerodynamic performance of vehicles and animals, as well as the productivity of turbines and energy harvesters, depends on the turbulence intensity of the incoming flow. Previous studies have pointed at the potential benefits of active closed-loop turbulence control. However, it is unclear what the minimal sensory and algorithmic requirements are for realizing this control. Here we show that very low-bandwidth anemometers record sufficient information for an adaptive control algorithm to converge quickly. Our online Newton-Raphson algorithm tunes the turbulence in a recirculating wind tunnel by taking readings from an anemometer in the test section. After starting at 9% turbulence intensity, the algorithm converges on values ranging from 10% to 45% in less than 12 iterations within 1% accuracy. By down-sampling our measurements, we show that very-low-bandwidth anemometers record sufficient information for convergence. Furthermore, down-sampling accelerates convergence by smoothing gradients in turbulence intensity. Our results explain why low-bandwidth anemometers in engineering and mechanoreceptors in biology may be sufficient for adaptive control of turbulence intensity. Finally, our analysis suggests that, if certain turbulent eddy sizes are more important to control than others, frugal adaptive control schemes can be particularly computationally effective for improving performance. © 2017 The Author(s).

Adaptive MPC based on MIMO ARX-Laguerre model.

PubMed

Ben Abdelwahed, Imen; Mbarek, Abdelkader; Bouzrara, Kais

2017-03-01

This paper proposes a method for synthesizing an adaptive predictive controller using a reduced complexity model. This latter is given by the projection of the ARX model on Laguerre bases. The resulting model is entitled MIMO ARX-Laguerre and it is characterized by an easy recursive representation. The adaptive predictive control law is computed based on multi-step-ahead finite-element predictors, identified directly from experimental input/output data. The model is tuned in each iteration by an online identification algorithms of both model parameters and Laguerre poles. The proposed approach avoids time consuming numerical optimization algorithms associated with most common linear predictive control strategies, which makes it suitable for real-time implementation. The method is used to synthesize and test in numerical simulations adaptive predictive controllers for the CSTR process benchmark. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Influence of growth flux solvent on anneal-tuning of ground states in CaFe2As2

NASA Astrophysics Data System (ADS)

Roncaioli, Connor; Drye, Tyler; Saha, Shanta R.; Paglione, Johnpierre

2018-04-01

The effects of anneal-tuning of single-crystalline samples of CaFe2As2 synthesized via a molten Sn-flux method are investigated using x-ray diffraction, chemical composition, electrical transport, and magnetic susceptibility measurements in order to understand the role of growth conditions on the resultant phase diagram. Previous studies of CaFe2As2 crystals synthesized using a self-flux (FeAs) method revealed an ability to tune the structural and magnetic properties of this system by control of post-synthesis annealing conditions, resulting in an ambient pressure phase diagram that spans from tetragonal/orthorhombic antiferromagnetism to the collapsed tetragonal phase of this system. In this work, we compare previous results to those obtained on crystals synthesized via Sn flux, finding similar tunability in both self- and Sn-flux cases, but less sensitivity to annealing temperatures in the latter case, resulting in a temperature-shifted phase diagram.

Observability-Based Guidance and Sensor Placement

NASA Astrophysics Data System (ADS)

Hinson, Brian T.

Control system performance is highly dependent on the quality of sensor information available. In a growing number of applications, however, the control task must be accomplished with limited sensing capabilities. This thesis addresses these types of problems from a control-theoretic point-of-view, leveraging system nonlinearities to improve sensing performance. Using measures of observability as an information quality metric, guidance trajectories and sensor distributions are designed to improve the quality of sensor information. An observability-based sensor placement algorithm is developed to compute optimal sensor configurations for a general nonlinear system. The algorithm utilizes a simulation of the nonlinear system as the source of input data, and convex optimization provides a scalable solution method. The sensor placement algorithm is applied to a study of gyroscopic sensing in insect wings. The sensor placement algorithm reveals information-rich areas on flexible insect wings, and a comparison to biological data suggests that insect wings are capable of acting as gyroscopic sensors. An observability-based guidance framework is developed for robotic navigation with limited inertial sensing. Guidance trajectories and algorithms are developed for range-only and bearing-only navigation that improve navigation accuracy. Simulations and experiments with an underwater vehicle demonstrate that the observability measure allows tuning of the navigation uncertainty.

Algorithms to automate gap-dependent integral tuning for the 2.8-meter long horizontal field undulator with a dynamic force compensation mechanism

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

Xu, Joseph Z., E-mail: x@anl.gov; Vasserman, Isaac; Strelnikov, Nikita

2016-07-27

A 2.8-meter long horizontal field prototype undulator with a dynamic force compensation mechanism has been developed and tested at the Advanced Photon Source (APS) at Argonne National Laboratory (Argonne). The magnetic tuning of the undulator integrals has been automated and accomplished by applying magnetic shims. A detailed description of the algorithms and performance is reported.

Self-assembled tunable networks of sticky colloidal particles

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

Demortiere, Arnaud; Snezhko, Oleksiy Alexey; Sapozhnikov, Maksim

Self-assembled tunable networks of microscopic polymer fibers ranging from wavy colloidal "fur" to highly interconnected networks are created from polymer systems and an applied electric field. The networks emerge via dynamic self-assembly in an alternating (ac) electric field from a non-aqueous suspension of "sticky" polymeric colloidal particles with a controlled degree of polymerization. The resulting architectures are tuned by the frequency and amplitude of the electric field and surface properties of the particles.

Adaptive control method for core power control in TRIGA Mark II reactor

NASA Astrophysics Data System (ADS)

Sabri Minhat, Mohd; Selamat, Hazlina; Subha, Nurul Adilla Mohd

2018-01-01

The 1MWth Reactor TRIGA PUSPATI (RTP) Mark II type has undergone more than 35 years of operation. The existing core power control uses feedback control algorithm (FCA). It is challenging to keep the core power stable at the desired value within acceptable error bands to meet the safety demand of RTP due to the sensitivity of nuclear research reactor operation. Currently, the system is not satisfied with power tracking performance and can be improved. Therefore, a new design core power control is very important to improve the current performance in tracking and regulate reactor power by control the movement of control rods. In this paper, the adaptive controller and focus on Model Reference Adaptive Control (MRAC) and Self-Tuning Control (STC) were applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, adaptive controller model, and control rods selection programming. The mathematical models of the reactor core were based on point kinetics model, thermal hydraulic models, and reactivity models. The adaptive control model was presented using Lyapunov method to ensure stable close loop system and STC Generalised Minimum Variance (GMV) Controller was not necessary to know the exact plant transfer function in designing the core power control. The performance between proposed adaptive control and FCA will be compared via computer simulation and analysed the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.

Performance Analysis of Fuzzy-PID Controller for Blood Glucose Regulation in Type-1 Diabetic Patients.

PubMed

Yadav, Jyoti; Rani, Asha; Singh, Vijander

2016-12-01

This paper presents Fuzzy-PID (FPID) control scheme for a blood glucose control of type 1 diabetic subjects. A new metaheuristic Cuckoo Search Algorithm (CSA) is utilized to optimize the gains of FPID controller. CSA provides fast convergence and is capable of handling global optimization of continuous nonlinear systems. The proposed controller is an amalgamation of fuzzy logic and optimization which may provide an efficient solution for complex problems like blood glucose control. The task is to maintain normal glucose levels in the shortest possible time with minimum insulin dose. The glucose control is achieved by tuning the PID (Proportional Integral Derivative) and FPID controller with the help of Genetic Algorithm and CSA for comparative analysis. The designed controllers are tested on Bergman minimal model to control the blood glucose level in the facets of parameter uncertainties, meal disturbances and sensor noise. The results reveal that the performance of CSA-FPID controller is superior as compared to other designed controllers.

Self organization of exotic oil-in-oil phases driven by tunable electrohydrodynamics

PubMed Central

Varshney, Atul; Ghosh, Shankar; Bhattacharya, S.; Yethiraj, Anand

2012-01-01

Self organization of large-scale structures in nature - either coherent structures like crystals, or incoherent dynamic structures like clouds - is governed by long-range interactions. In many problems, hydrodynamics and electrostatics are the source of such long-range interactions. The tuning of electrostatic interactions has helped to elucidate when coherent crystalline structures or incoherent amorphous structures form in colloidal systems. However, there is little understanding of self organization in situations where both electrostatic and hydrodynamic interactions are present. We present a minimal two-component oil-in-oil model system where we can control the strength and lengthscale of the electrohydrodynamic interactions by tuning the amplitude and frequency of the imposed electric field. As a function of the hydrodynamic lengthscale, we observe a rich phenomenology of exotic structure and dynamics, from incoherent cloud-like structures and chaotic droplet dynamics, to polyhedral droplet phases, to coherent droplet arrays. PMID:23071902

A neural network based implementation of an MPC algorithm applied in the control systems of electromechanical plants

NASA Astrophysics Data System (ADS)

Marusak, Piotr M.; Kuntanapreeda, Suwat

2018-01-01

The paper considers application of a neural network based implementation of a model predictive control (MPC) control algorithm to electromechanical plants. Properties of such control plants implicate that a relatively short sampling time should be used. However, in such a case, finding the control value numerically may be too time-consuming. Therefore, the current paper tests the solution based on transforming the MPC optimization problem into a set of differential equations whose solution is the same as that of the original optimization problem. This set of differential equations can be interpreted as a dynamic neural network. In such an approach, the constraints can be introduced into the optimization problem with relative ease. Moreover, the solution of the optimization problem can be obtained faster than when the standard numerical quadratic programming routine is used. However, a very careful tuning of the algorithm is needed to achieve this. A DC motor and an electrohydraulic actuator are taken as illustrative examples. The feasibility and effectiveness of the proposed approach are demonstrated through numerical simulations.

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.

Optimization of hydraulic turbine governor parameters based on WPA

NASA Astrophysics Data System (ADS)

Gao, Chunyang; Yu, Xiangyang; Zhu, Yong; Feng, Baohao

2018-01-01

The parameters of hydraulic turbine governor directly affect the dynamic characteristics of the hydraulic unit, thus affecting the regulation capacity and the power quality of power grid. The governor of conventional hydropower unit is mainly PID governor with three adjustable parameters, which are difficult to set up. In order to optimize the hydraulic turbine governor, this paper proposes wolf pack algorithm (WPA) for intelligent tuning since the good global optimization capability of WPA. Compared with the traditional optimization method and PSO algorithm, the results show that the PID controller designed by WPA achieves a dynamic quality of hydraulic system and inhibits overshoot.

Gravitational Mechanisms to Self-Tune the Cosmological Constant: Obstructions and Ways Forward

NASA Astrophysics Data System (ADS)

Niedermann, Florian; Padilla, Antonio

2017-12-01

Gravitational models of self-tuning are those in which vacuum energy has no observable effect on spacetime curvature, even though it is a priori unsuppressed below the cutoff. We complement Weinberg's no-go theorem by studying field-theoretic completions of self-adjustment allowing for broken translations as well as other generalizations, and identify new obstructions. Our analysis uses a very general Källén-Lehmann spectral representation of the exchange amplitude for conserved sources of energy-momentum and exploits unitarity and Lorentz invariance to show that a transition from self-tuning of long wavelength sources to near general relativity (GR) on shorter scales is generically not possible. We search for novel ways around our obstructions and highlight two interesting possibilities. The first is an example of a unitary field configuration on anti-de Sitter space with the desired transition from self-tuning to GR. A second example is motivated by vacuum energy sequestering.

Full-order Luenberger observer based on fuzzy-logic control for sensorless field-oriented control of a single-sided linear induction motor.

PubMed

Holakooie, Mohammad Hosein; Ojaghi, Mansour; Taheri, Asghar

2016-01-01

This paper investigates sensorless indirect field oriented control (IFOC) of SLIM with full-order Luenberger observer. The dynamic equations of SLIM are first elaborated to draw full-order Luenberger observer with some simplifying assumption. The observer gain matrix is derived from conventional procedure so that observer poles are proportional to SLIM poles to ensure the stability of system for wide range of linear speed. The operation of observer is significantly impressed by adaptive scheme. A fuzzy logic control (FLC) is proposed as adaptive scheme to estimate linear speed using speed tuning signal. The parameters of FLC are tuned using an off-line method through chaotic optimization algorithm (COA). The performance of the proposed observer is verified by both numerical simulation and real-time hardware-in-the-loop (HIL) implementation. Moreover, a detailed comparative study among proposed and other speed observers is obtained under different operation conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A Fuzzy Technique for Performing Lateral-Axis Formation Flight Navigation Using Wingtip Vortices

NASA Technical Reports Server (NTRS)

Hanson, Curtis E.

2003-01-01

Close formation flight involving aerodynamic coupling through wingtip vortices shows significant promise to improve the efficiency of cooperative aircraft operations. Impediments to the application of this technology include internship communication required to establish precise relative positioning. This report proposes a method for estimating the lateral relative position between two aircraft in close formation flight through real-time estimates of the aerodynamic effects imparted by the leading airplane on the trailing airplane. A fuzzy algorithm is developed to map combinations of vortex-induced drag and roll effects to relative lateral spacing. The algorithm is refined using self-tuning techniques to provide lateral relative position estimates accurate to 14 in., well within the requirement to maintain significant levels of drag reduction. The fuzzy navigation algorithm is integrated with a leader-follower formation flight autopilot in a two-ship F/A-18 simulation with no intership communication modeled. It is shown that in the absence of measurements from the leading airplane the algorithm provides sufficient estimation of lateral formation spacing for the autopilot to maintain stable formation flight within the vortex. Formation autopilot trim commands are used to estimate vortex effects for the algorithm. The fuzzy algorithm is shown to operate satisfactorily with anticipated levels of input uncertainties.

Adaptive-randomised self-calibration of electro-mechanical shutters for space imaging

NASA Astrophysics Data System (ADS)

De Cecco, Mariolino; Debei, Stefano; Zaccariotto, Mirco; Pertile, Marco

2006-11-01

This work describes the self-calibration of a high-precision open-loop mechanism. The self-calibration method is applied to a mechanical shutter for space applications, which was launched onboard the ESA-ROSETTA mission (launch: 2 March 2004). It is based on an adaptive 'model reference' and a 'randomised' search method which may be generalised to applications in which high performance and functionality are strongly interconnected. The method makes use of an adaptive 'model-reference' control approach [K.J. Astrom, B. Wittenmark, On self-tuning regulators Automatica 9 (1973) 185-199 [16]; K.J. Astrom, Theory and application of adaptive control, in: Proceedings of the Eighth IFAC World Conference, Kyoto, Japan, 1981 [17]; D.E. Seborg, S.L. Shah, T.F. Edgar, Adaptive control strategies for process control, AIChE Journal 6(32) (1986) 881-895 [18

Geostationary Lightning Mapper: Lessons Learned from Post Launch Test

NASA Astrophysics Data System (ADS)

Edgington, S.; Tillier, C. E.; Demroff, H.; VanBezooijen, R.; Christian, H. J., Jr.; Bitzer, P. M.

2017-12-01

Pre-launch calibration and algorithm design for the GOES Geostationary Lightning Mapper resulted in a successful and trouble-free on-orbit activation and post-launch test sequence. Within minutes of opening the GLM aperture door on January 4th, 2017, lightning was detected across the entire field of view. During the six-month post-launch test period, numerous processing parameters on board the instrument and in the ground processing algorithms were fine-tuned. Demonstrated on-orbit performance exceeded pre-launch predictions. We provide an overview of the ground calibration sequence, on-orbit tuning of the instrument, tuning of the ground processing algorithms (event filtering and navigation). We also touch on new insights obtained from analysis of a large and growing archive of raw GLM data, containing 3e8 flash detections derived from over 1e10 full-disk images of the Earth.

Evaluation of Various Radar Data Quality Control Algorithms Based on Accumulated Radar Rainfall Statistics

NASA Technical Reports Server (NTRS)

Robinson, Michael; Steiner, Matthias; Wolff, David B.; Ferrier, Brad S.; Kessinger, Cathy; Einaudi, Franco (Technical Monitor)

2000-01-01

The primary function of the TRMM Ground Validation (GV) Program is to create GV rainfall products that provide basic validation of satellite-derived precipitation measurements for select primary sites. A fundamental and extremely important step in creating high-quality GV products is radar data quality control. Quality control (QC) processing of TRMM GV radar data is based on some automated procedures, but the current QC algorithm is not fully operational and requires significant human interaction to assure satisfactory results. Moreover, the TRMM GV QC algorithm, even with continuous manual tuning, still can not completely remove all types of spurious echoes. In an attempt to improve the current operational radar data QC procedures of the TRMM GV effort, an intercomparison of several QC algorithms has been conducted. This presentation will demonstrate how various radar data QC algorithms affect accumulated radar rainfall products. In all, six different QC algorithms will be applied to two months of WSR-88D radar data from Melbourne, Florida. Daily, five-day, and monthly accumulated radar rainfall maps will be produced for each quality-controlled data set. The QC algorithms will be evaluated and compared based on their ability to remove spurious echoes without removing significant precipitation. Strengths and weaknesses of each algorithm will be assessed based on, their abilit to mitigate both erroneous additions and reductions in rainfall accumulation from spurious echo contamination and true precipitation removal, respectively. Contamination from individual spurious echo categories will be quantified to further diagnose the abilities of each radar QC algorithm. Finally, a cost-benefit analysis will be conducted to determine if a more automated QC algorithm is a viable alternative to the current, labor-intensive QC algorithm employed by TRMM GV.

Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

NASA Technical Reports Server (NTRS)

Hayashi, Isao; Nomura, Hiroyoshi; Wakami, Noboru

1991-01-01

Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

Motion Cueing Algorithm Development: Initial Investigation and Redesign of the Algorithms

NASA Technical Reports Server (NTRS)

Telban, Robert J.; Wu, Weimin; Cardullo, Frank M.; Houck, Jacob A. (Technical Monitor)

2000-01-01

In this project four motion cueing algorithms were initially investigated. The classical algorithm generated results with large distortion and delay and low magnitude. The NASA adaptive algorithm proved to be well tuned with satisfactory performance, while the UTIAS adaptive algorithm produced less desirable results. Modifications were made to the adaptive algorithms to reduce the magnitude of undesirable spikes. The optimal algorithm was found to have the potential for improved performance with further redesign. The center of simulator rotation was redefined. More terms were added to the cost function to enable more tuning flexibility. A new design approach using a Fortran/Matlab/Simulink setup was employed. A new semicircular canals model was incorporated in the algorithm. With these changes results show the optimal algorithm has some advantages over the NASA adaptive algorithm. Two general problems observed in the initial investigation required solutions. A nonlinear gain algorithm was developed that scales the aircraft inputs by a third-order polynomial, maximizing the motion cues while remaining within the operational limits of the motion system. A braking algorithm was developed to bring the simulator to a full stop at its motion limit and later release the brake to follow the cueing algorithm output.

Artificial intelligence-based computer modeling tools for controlling slag foaming in electric arc furnaces

NASA Astrophysics Data System (ADS)

Wilson, Eric Lee

Due to increased competition in a world economy, steel companies are currently interested in developing techniques that will allow for the improvement of the steelmaking process, either by increasing output efficiency or by improving the quality of their product, or both. Slag foaming is one practice that has been shown to contribute to both these goals. However, slag foaming is highly dynamic and difficult to model or control. This dissertation describes an effort to use artificial intelligence-based tools (genetic algorithms, fuzzy logic, and neural networks) to both model and control the slag foaming process. Specifically, a neural network is trained and tested on slag foaming data provided by a steel plant. This neural network model is then controlled by a fuzzy logic controller, which in turn is optimized by a genetic algorithm. This tuned controller is then installed at a steel plant and given control be a more efficient slag foaming controller than what was previously used by the steel plant.

Design issues for a reinforcement-based self-learning fuzzy controller

NASA Technical Reports Server (NTRS)

Yen, John; Wang, Haojin; Dauherity, Walter

1993-01-01

Fuzzy logic controllers have some often cited advantages over conventional techniques such as PID control: easy implementation, its accommodation to natural language, the ability to cover wider range of operating conditions and others. One major obstacle that hinders its broader application is the lack of a systematic way to develop and modify its rules and as result the creation and modification of fuzzy rules often depends on try-error or pure experimentation. One of the proposed approaches to address this issue is self-learning fuzzy logic controllers (SFLC) that use reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of self-learning fuzzy controller is highly contingent on the design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for the application to chemical process are discussed and its performance is compared with that of PID and self-tuning fuzzy logic controller.

Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus.

PubMed

Pustozerov, Evgenii; Popova, Polina; Tkachuk, Aleksandra; Bolotko, Yana; Yuldashev, Zafar; Grineva, Elena

2018-01-09

Personalized blood glucose (BG) prediction for diabetes patients is an important goal that is pursued by many researchers worldwide. Despite many proposals, only a few projects are dedicated to the development of complete recommender system infrastructures that incorporate BG prediction algorithms for diabetes patients. The development and implementation of such a system aided by mobile technology is of particular interest to patients with gestational diabetes mellitus (GDM), especially considering the significant importance of quickly achieving adequate BG control for these patients in a short period (ie, during pregnancy) and a typically higher acceptance rate for mobile health (mHealth) solutions for short- to midterm usage. This study was conducted with the objective of developing infrastructure comprising data processing algorithms, BG prediction models, and an appropriate mobile app for patients' electronic record management to guide BG prediction-based personalized recommendations for patients with GDM. A mobile app for electronic diary management was developed along with data exchange and continuous BG signal processing software. Both components were coupled to obtain the necessary data for use in the personalized BG prediction system. Necessary data on meals, BG measurements, and other events were collected via the implemented mobile app and continuous glucose monitoring (CGM) system processing software. These data were used to tune and evaluate the BG prediction model, which included an algorithm for dynamic coefficients tuning. In the clinical study, 62 participants (GDM: n=49; control: n=13) took part in a 1-week monitoring trial during which they used the mobile app to track their meals and self-measurements of BG and CGM system for continuous BG monitoring. The data on 909 food intakes and corresponding postprandial BG curves as well as the set of patients' characteristics (eg, glycated hemoglobin, body mass index [BMI], age, and lifestyle parameters) were selected as inputs for the BG prediction models. The prediction results by the models for BG levels 1 hour after food intake were root mean square error=0.87 mmol/L, mean absolute error=0.69 mmol/L, and mean absolute percentage error=12.8%, which correspond to an adequate prediction accuracy for BG control decisions. The mobile app for the collection and processing of relevant data, appropriate software for CGM system signals processing, and BG prediction models were developed for a recommender system. The developed system may help improve BG control in patients with GDM; this will be the subject of evaluation in a subsequent study. ©Evgenii Pustozerov, Polina Popova, Aleksandra Tkachuk, Yana Bolotko, Zafar Yuldashev, Elena Grineva. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 09.01.2018.

One ring to rule them all: tuning bacteria collective motion via geometric confinement

NASA Astrophysics Data System (ADS)

Giomi, Luca

2016-08-01

Suspensions of swimming bacteria are known to self-organize into turbulent-like flows for sufficiently high density and nutrients concentration. This spectacular example of collective behavior, on which the survival of the colony itself is believed to rely, appears however impossible to control. In a recent experimental and computational study, Wioland et al (2016 New J. Phys. 18 075002) have demonstrated that the collective motion of B. subtilis can be in fact selectively tuned by confining the system into a ring-shaped channel.

PID self tuning control based on Mamdani fuzzy logic control for quadrotor stabilization

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

Priyambodo, Tri Kuntoro, E-mail: mastri@ugm.ac.id; Putra, Agfianto Eko; Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta

Quadrotor as one type of UAV have the ability to perform Vertical Take Off and Landing (VTOL). It allows the Quadrotor to be stationary hovering in the air. PID (Proportional Integral Derivative) control system is one of the control methods that are commonly used. It is usually used to optimize the Quadrotor stabilization at least based on the three Eulerian angles (roll, pitch, and yaw) as input parameters for the control system. The three constants of PID can be obtained in various methods. The simplest method is tuning manually. This method has several weaknesses. For example if the three constantsmore » are not exact, the resulting response will deviate from the desired result. By combining the methods of PID with fuzzy logic systems where human expertise is implemented into the machine language is expected to further optimize the control system.« less

Development of a Self-Calibrated MEMS Gyrocompass for North-Finding and Tracking

NASA Astrophysics Data System (ADS)

Prikhodko, Igor P.

This Ph.D. dissertation presents development of a microelectromechanical (MEMS) gyrocompass for north-finding and north-tracking applications. The central part of this work enabling these applications is control and self-calibration architectures for drift mitigation over thermal environments, validated using a MEMS quadruple mass gyroscope. The thesis contributions are the following: • Adapted and implemented bias and scale-factor drifts compensation algorithm relying on temperature self-sensing for MEMS gyroscopes with high quality factors. The real-time self-compensation reduced a total bias error to 2 °/hr and a scale-factor error to 500 ppm over temperature range of 25 °C to 55 °C (on par with the state-of-the-art). • Adapted and implemented a scale-factor self-calibration algorithm previously employed for macroscale hemispherical resonator gyroscope to MEMS Coriolis vibratory gyroscopes. An accuracy of 100 ppm was demonstrated by simultaneously measuring the true and estimated scale-factors over temperature variations (on par with the state-of-the art). • Demonstrated north-finding accuracy satisfying a typical mission requirement of 4 meter target location error at 1 kilometer stand-off distance (on par with a GPS accuracy). Analyzed north-finding mechanizations trade-offs for MEMS vibratory gyroscopes and demonstrated measurements of the Earth's rotation (15 °/hr). • Demonstrated, for the first time, an angle measuring MEMS gyroscope operation for north-tracking applications in a +/-500 °/s rate range and 100 Hz bandwidth, eliminating both bandwidth and range constraints of conventional open-loop Coriolis vibratory gyroscopes. • Investigated hypothesis that surface-tension driven glass-blowing microfabrication can create highly spherical shells for 3-D MEMS. Without any trimming or tuning of the natural frequencies, a 1 MHz glass-blown 3-D microshell resonator demonstrated a 0.63 % frequency mismatch between two degenerate 4-node wineglass modes. • Multi-axis rotation detection for nuclear magnetic resonance (NMR) gyroscope was proposed and developed. The analysis of cross-axis sensitivities for NMR gyroscope was performed. The framework for the analysis of NMR gyroscope dynamics for both open loop and closed loop modes of operation was developed.

Adaptive Control of Synchronization in Delay-Coupled Heterogeneous Networks of FitzHugh-Nagumo Nodes

NASA Astrophysics Data System (ADS)

Plotnikov, S. A.; Lehnert, J.; Fradkov, A. L.; Schöll, E.

We study synchronization in delay-coupled neural networks of heterogeneous nodes. It is well known that heterogeneities in the nodes hinder synchronization when becoming too large. We show that an adaptive tuning of the overall coupling strength can be used to counteract the effect of the heterogeneity. Our adaptive controller is demonstrated on ring networks of FitzHugh-Nagumo systems which are paradigmatic for excitable dynamics but can also — depending on the system parameters — exhibit self-sustained periodic firing. We show that the adaptively tuned time-delayed coupling enables synchronization even if parameter heterogeneities are so large that excitable nodes coexist with oscillatory ones.

Electric-Field-Induced Energy Tuning of On-Demand Entangled-Photon Emission from Self-Assembled Quantum Dots.

PubMed

Zhang, Jiaxiang; Zallo, Eugenio; Höfer, Bianca; Chen, Yan; Keil, Robert; Zopf, Michael; Böttner, Stefan; Ding, Fei; Schmidt, Oliver G

2017-01-11

We explore a method to achieve electrical control over the energy of on-demand entangled-photon emission from self-assembled quantum dots (QDs). The device used in our work consists of an electrically tunable diode-like membrane integrated onto a piezoactuator, which is capable of exerting a uniaxial stress on QDs. We theoretically reveal that, through application of the quantum-confined Stark effect to QDs by a vertical electric field, the critical uniaxial stress used to eliminate the fine structure splitting of QDs can be linearly tuned. This feature allows experimental realization of a triggered source of energy-tunable entangled-photon emission. Our demonstration represents an important step toward realization of a solid-state quantum repeater using indistinguishable entangled photons in Bell state measurements.

Effectiveness and cost-utility of a guided self-help exercise program for patients treated with total laryngectomy: protocol of a multi-center randomized controlled trial.

PubMed

Jansen, Femke; Cnossen, Ingrid C; Eerenstein, Simone E J; Coupé, Veerle M H; Witte, Birgit I; van Uden-Kraan, Cornelia F; Doornaert, Patricia; Braunius, Weibel W; De Bree, Remco; Hardillo, José A U; Honings, Jimmie; Halmos, György B; Leemans, C René; Verdonck-de Leeuw, Irma M

2016-08-02

Total laryngectomy with or without adjuvant (chemo)radiation often induces speech, swallowing and neck and shoulder problems. Speech, swallowing and shoulder exercises may prevent or diminish these problems. The aim of the present paper is to describe the study, which is designed to investigate the effectiveness and cost-utility of a guided self-help exercise program built into the application "In Tune without Cords" among patients treated with total laryngectomy. Patients, up to 5 years earlier treated with total laryngectomy with or without (chemo)radiation will be recruited for participation in this study. Patients willing to participate will be randomized to the intervention or control group (1:1). Patients in the intervention group will be provided access to a guided self-help exercise program and a self-care education program built into the application "In Tune without Cords". Patients in the control group will only be provided access to the self-care education program. The primary outcome is the difference in swallowing quality (SWAL-QOL) between the intervention and control group. Secondary outcome measures address speech problems (SHI), shoulder disability (SDQ), quality of life (EORTC QLQ-C30, QLQ-H&N35 and EQ-5D), direct and indirect costs (adjusted iMCQ and iPCQ measures) and self-management (PAM). Patients will be asked to complete these outcome measures at baseline, immediately after the intervention or control period (i.e. at 3 months follow-up) and at 6 months follow-up. This randomized controlled trial will provide knowledge on the effectiveness of a guided self-help exercise program for patients treated with total laryngectomy. In addition, information on the value for money of such an exercise program will be provided. If this guided self-help program is (cost)effective for patients treated with total laryngectomy, the next step will be to implement this exercise program in current clinical practice. NTR5255 Protocol version 4 date September 2015.

Programming and Tuning a Quantum Annealing Device to Solve Real World Problems

NASA Astrophysics Data System (ADS)

Perdomo-Ortiz, Alejandro; O'Gorman, Bryan; Fluegemann, Joseph; Smelyanskiy, Vadim

2015-03-01

Solving real-world applications with quantum algorithms requires overcoming several challenges, ranging from translating the computational problem at hand to the quantum-machine language to tuning parameters of the quantum algorithm that have a significant impact on the performance of the device. In this talk, we discuss these challenges, strategies developed to enhance performance, and also a more efficient implementation of several applications. Although we will focus on applications of interest to NASA's Quantum Artificial Intelligence Laboratory, the methods and concepts presented here apply to a broader family of hard discrete optimization problems, including those that occur in many machine-learning algorithms.

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.

Central safety factor and β N control on NSTX-U via beam power and plasma boundary shape modification, using TRANSP for closed loop simulations

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

Boyer, M. D.; Andre, R.; Gates, D. A.

The high-performance operational goals of NSTX-U will require development of advanced feedback control algorithms, including control of ßN and the safety factor profile. In this work, a novel approach to simultaneously controlling ßN and the value of the safety factor on the magnetic axis, q0, through manipulation of the plasma boundary shape and total beam power, is proposed. Simulations of the proposed scheme show promising results and motivate future experimental implementation and eventual integration into a more complex current profile control scheme planned to include actuation of individual beam powers, density, and loop voltage. As part of this work, amore » flexible framework for closed loop simulations within the high-fidelity code TRANSP was developed. The framework, used here to identify control-design-oriented models and to tune and test the proposed controller, exploits many of the predictive capabilities of TRANSP and provides a means for performing control calculations based on user-supplied data (controller matrices, target waveforms, etc.). The flexible framework should enable high-fidelity testing of a variety of control algorithms, thereby reducing the amount of expensive experimental time needed to implement new control algorithms on NSTX-U and other devices.« less

Central safety factor and βN control on NSTX-U via beam power and plasma boundary shape modification, using TRANSP for closed loop simulations

NASA Astrophysics Data System (ADS)

Boyer, M. D.; Andre, R.; Gates, D. A.; Gerhardt, S.; Goumiri, I. R.; Menard, J.

2015-05-01

The high-performance operational goals of NSTX-U will require development of advanced feedback control algorithms, including control of βN and the safety factor profile. In this work, a novel approach to simultaneously controlling βN and the value of the safety factor on the magnetic axis, q0, through manipulation of the plasma boundary shape and total beam power, is proposed. Simulations of the proposed scheme show promising results and motivate future experimental implementation and eventual integration into a more complex current profile control scheme planned to include actuation of individual beam powers, density, and loop voltage. As part of this work, a flexible framework for closed loop simulations within the high-fidelity code TRANSP was developed. The framework, used here to identify control-design-oriented models and to tune and test the proposed controller, exploits many of the predictive capabilities of TRANSP and provides a means for performing control calculations based on user-supplied data (controller matrices, target waveforms, etc). The flexible framework should enable high-fidelity testing of a variety of control algorithms, thereby reducing the amount of expensive experimental time needed to implement new control algorithms on NSTX-U and other devices.

An interactive control algorithm used for equilateral triangle formation with robotic sensors.

PubMed

Li, Xiang; Chen, Hongcai

2014-04-22

This paper describes an interactive control algorithm, called Triangle Formation Algorithm (TFA), used for three neighboring robotic sensors which are distributed randomly to self-organize into and equilateral triangle (E) formation. The algorithm is proposed based on the triangular geometry and considering the actual sensors used in robotics. In particular, the stability of the TFA, which can be executed by robotic sensors independently and asynchronously for E formation, is analyzed in details based on Lyapunov stability theory. Computer simulations are carried out for verifying the effectiveness of the TFA. The analytical results and simulation studies indicate that three neighboring robots employing conventional sensors can self-organize into E formations successfully regardless of their initial distribution using the same TFAs.

An Interactive Control Algorithm Used for Equilateral Triangle Formation with Robotic Sensors

PubMed Central

Li, Xiang; Chen, Hongcai

2014-01-01

This paper describes an interactive control algorithm, called Triangle Formation Algorithm (TFA), used for three neighboring robotic sensors which are distributed randomly to self-organize into and equilateral triangle (E) formation. The algorithm is proposed based on the triangular geometry and considering the actual sensors used in robotics. In particular, the stability of the TFA, which can be executed by robotic sensors independently and asynchronously for E formation, is analyzed in details based on Lyapunov stability theory. Computer simulations are carried out for verifying the effectiveness of the TFA. The analytical results and simulation studies indicate that three neighboring robots employing conventional sensors can self-organize into E formations successfully regardless of their initial distribution using the same TFAs. PMID:24759118

Self-Healing Nanocomposite Hydrogel with Well-Controlled Dynamic Mechanics

NASA Astrophysics Data System (ADS)

Li, Qiaochu; Mishra, Sumeet; Chen, Pangkuan; Tracy, Joseph; Holten-Andersen, Niels

Network dynamics is a crucial factor that determines the macroscopic self-healing rate and efficiency in polymeric hydrogel materials, yet its controllability is seldom studied in most reported self-healing hydrogel systems. Inspired by mussel's adhesion chemistry, we developed a novel approach to assemble inorganic nanoparticles and catechol-decorated PEG polymer into a hydrogel network. When utilized as reversible polymer-particle crosslinks, catechol-metal coordination bonds yield a unique gel network with dynamic mechanics controlled directly by interfacial crosslink structure. Taking advantage of this structure-property relationship at polymer-particle interfaces, we next designed a hierarchically structured hybrid gel with two distinct relaxation timescales. By tuning the relative contribution of the two hierarchical relaxation modes, we are able to finely control the gel's dynamic mechanical behavior from a viscoelastic fluid to a stiff solid, yet preserving its fast self-healing property without the need for external stimuli.

Disformally self-tuning gravity

NASA Astrophysics Data System (ADS)

Emond, William T.; Saffin, Paul M.

2016-03-01

We extend a previous self-tuning analysis of the most general scalar-tensor theory of gravity in four dimensions with second order field equations by considering a generalized coupling to the matter sector. Through allowing a disformal coupling to matter we are able to extend the Fab Four model and construct a new class of theories that are able to tune away the cosmological constant on Friedmann-Lemaitre-Robertson-Walker backgrounds.

Determination of water depth with high-resolution satellite imagery over variable bottom types

USGS Publications Warehouse

Stumpf, Richard P.; Holderied, Kristine; Sinclair, Mark

2003-01-01

A standard algorithm for determining depth in clear water from passive sensors exists; but it requires tuning of five parameters and does not retrieve depths where the bottom has an extremely low albedo. To address these issues, we developed an empirical solution using a ratio of reflectances that has only two tunable parameters and can be applied to low-albedo features. The two algorithms--the standard linear transform and the new ratio transform--were compared through analysis of IKONOS satellite imagery against lidar bathymetry. The coefficients for the ratio algorithm were tuned manually to a few depths from a nautical chart, yet performed as well as the linear algorithm tuned using multiple linear regression against the lidar. Both algorithms compensate for variable bottom type and albedo (sand, pavement, algae, coral) and retrieve bathymetry in water depths of less than 10-15 m. However, the linear transform does not distinguish depths >15 m and is more subject to variability across the studied atolls. The ratio transform can, in clear water, retrieve depths in >25 m of water and shows greater stability between different areas. It also performs slightly better in scattering turbidity than the linear transform. The ratio algorithm is somewhat noisier and cannot always adequately resolve fine morphology (structures smaller than 4-5 pixels) in water depths >15-20 m. In general, the ratio transform is more robust than the linear transform.

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.

A survey of adaptive control technology in robotics

NASA Technical Reports Server (NTRS)

Tosunoglu, S.; Tesar, D.

1987-01-01

Previous work on the adaptive control of robotic systems is reviewed. Although the field is relatively new and does not yet represent a mature discipline, considerable attention has been given to the design of sophisticated robot controllers. Here, adaptive control methods are divided into model reference adaptive systems and self-tuning regulators with further definition of various approaches given in each class. The similarity and distinct features of the designed controllers are delineated and tabulated to enhance comparative review.

Optimal Trajectories Generation in Robotic Fiber Placement Systems

NASA Astrophysics Data System (ADS)

Gao, Jiuchun; Pashkevich, Anatol; Caro, Stéphane

2017-06-01

The paper proposes a methodology for optimal trajectories generation in robotic fiber placement systems. A strategy to tune the parameters of the optimization algorithm at hand is also introduced. The presented technique transforms the original continuous problem into a discrete one where the time-optimal motions are generated by using dynamic programming. The developed strategy for the optimization algorithm tuning allows essentially reducing the computing time and obtaining trajectories satisfying industrial constraints. Feasibilities and advantages of the proposed methodology are confirmed by an application example.

Performance Optimization Control of ECH using Fuzzy Inference Application

NASA Astrophysics Data System (ADS)

Dubey, Abhay Kumar

Electro-chemical honing (ECH) is a hybrid electrolytic precision micro-finishing technology that, by combining physico-chemical actions of electro-chemical machining and conventional honing processes, provides the controlled functional surfaces-generation and fast material removal capabilities in a single operation. Process multi-performance optimization has become vital for utilizing full potential of manufacturing processes to meet the challenging requirements being placed on the surface quality, size, tolerances and production rate of engineering components in this globally competitive scenario. This paper presents an strategy that integrates the Taguchi matrix experimental design, analysis of variances and fuzzy inference system (FIS) to formulate a robust practical multi-performance optimization methodology for complex manufacturing processes like ECH, which involve several control variables. Two methodologies one using a genetic algorithm tuning of FIS (GA-tuned FIS) and another using an adaptive network based fuzzy inference system (ANFIS) have been evaluated for a multi-performance optimization case study of ECH. The actual experimental results confirm their potential for a wide range of machining conditions employed in ECH.

Study of the Power Supply Ripple Effect on teh Dynamics at SPEAR

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

Terebilo, A.; Pellegrini, C.; /UCLA

For long term stability analysis time variation of tunes is important. We have proposed and tested a technique for measuring the magnitude of this variation. This was made possible by using tune extraction algorithms that require small number of turns thus giving an instantaneous tune of the machine. In this paper we demonstrate the measured effect of the tune modulation with 60 Hz power supplies ripple, power line interference from SLAC linac operating at 30 Hz repetition rate, and nonperiodic variation.

Probabilistic Swarm Guidance using Optimal Transport

DTIC Science & Technology

2014-10-10

controlled to collectively exhibit useful emergent behavior [2]–[5]. Similarly, swarms of hundreds to thousands of femtosatellites (100-gram-class...algorithm using inhomo- geneous Markov chains (PSG– IMC ), each agent chooses the tuning parameter (ξjk) based on the Hellinger distance (HD) between the...PGA and PSG– IMC in the next section. B. Simulation Results We now present the setup of this simulation example. The swarm containing m = 5000 agents is

Advanced semi-active engine and transmission mounts: tools for modelling, analysis, design, and tuning

NASA Astrophysics Data System (ADS)

Farjoud, Alireza; Taylor, Russell; Schumann, Eric; Schlangen, Timothy

2014-02-01

This paper is focused on modelling, design, and testing of semi-active magneto-rheological (MR) engine and transmission mounts used in the automotive industry. The purpose is to develop a complete analysis, synthesis, design, and tuning tool that reduces the need for expensive and time-consuming laboratory and field tests. A detailed mathematical model of such devices is developed using multi-physics modelling techniques for physical systems with various energy domains. The model includes all major features of an MR mount including fluid dynamics, fluid track, elastic components, decoupler, rate-dip, gas-charged chamber, MR fluid rheology, magnetic circuit, electronic driver, and control algorithm. Conventional passive hydraulic mounts can also be studied using the same mathematical model. The model is validated using standard experimental procedures. It is used for design and parametric study of mounts; effects of various geometric and material parameters on dynamic response of mounts can be studied. Additionally, this model can be used to test various control strategies to obtain best vibration isolation performance by tuning control parameters. Another benefit of this work is that nonlinear interactions between sub-components of the mount can be observed and investigated. This is not possible by using simplified linear models currently available.

Description of a dual fail operational redundant strapdown inertial measurement unit for integrated avionics systems research

NASA Technical Reports Server (NTRS)

Bryant, W. H.; Morrell, F. R.

1981-01-01

An experimental redundant strapdown inertial measurement unit (RSDIMU) is developed as a link to satisfy safety and reliability considerations in the integrated avionics concept. The unit includes four two degree-of-freedom tuned rotor gyros, and four accelerometers in a skewed and separable semioctahedral array. These sensors are coupled to four microprocessors which compensate sensor errors. These microprocessors are interfaced with two flight computers which process failure detection, isolation, redundancy management, and general flight control/navigation algorithms. Since the RSDIMU is a developmental unit, it is imperative that the flight computers provide special visibility and facility in algorithm modification.

Tuning peptide self-assembly by an in-tether chiral center

PubMed Central

Hu, Kuan; Xiong, Wei; Li, Hu; Zhang, Pei-Yu; Yin, Feng; Zhang, Qianling; Jiang, Fan; Li, Zigang

2018-01-01

The self-assembly of peptides into ordered nanostructures is important for understanding both peptide molecular interactions and nanotechnological applications. However, because of the complexity and various self-assembling pathways of peptide molecules, design of self-assembling helical peptides with high controllability and tunability is challenging. We report a new self-assembling mode that uses in-tether chiral center-induced helical peptides as a platform for tunable peptide self-assembly with good controllability. It was found that self-assembling behavior was governed by in-tether substitutional groups, where chirality determined the formation of helical structures and aromaticity provided the driving force for self-assembly. Both factors were essential for peptide self-assembly to occur. Experiments and theoretical calculations indicate long-range crystal-like packing in the self-assembly, which was stabilized by a synergy of interpeptide π-π and π-sulfur interactions and hydrogen bond networks. In addition, the self-assembled peptide nanomaterials were demonstrated to be promising candidate materials for applications in biocompatible electrochemical supercapacitors.

An annealed chaotic maximum neural network for bipartite subgraph problem.

PubMed

Wang, Jiahai; Tang, Zheng; Wang, Ronglong

2004-04-01

In this paper, based on maximum neural network, we propose a new parallel algorithm that can help the maximum neural network escape from local minima by including a transient chaotic neurodynamics for bipartite subgraph problem. The goal of the bipartite subgraph problem, which is an NP- complete problem, is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. Lee et al. presented a parallel algorithm using the maximum neural model (winner-take-all neuron model) for this NP- complete problem. The maximum neural model always guarantees a valid solution and greatly reduces the search space without a burden on the parameter-tuning. However, the model has a tendency to converge to a local minimum easily because it is based on the steepest descent method. By adding a negative self-feedback to the maximum neural network, we proposed a new parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm is then fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the maximum neural network and the chaotic neurodynamics. A large number of instances have been simulated to verify the proposed algorithm. The simulation results show that our algorithm finds the optimum or near-optimum solution for the bipartite subgraph problem superior to that of the best existing parallel algorithms.

Parameter optimization of electrochemical machining process using black hole algorithm

NASA Astrophysics Data System (ADS)

Singh, Dinesh; Shukla, Rajkamal

2017-12-01

Advanced machining processes are significant as higher accuracy in machined component is required in the manufacturing industries. Parameter optimization of machining processes gives optimum control to achieve the desired goals. In this paper, electrochemical machining (ECM) process is considered to evaluate the performance of the considered process using black hole algorithm (BHA). BHA considers the fundamental idea of a black hole theory and it has less operating parameters to tune. The two performance parameters, material removal rate (MRR) and overcut (OC) are considered separately to get optimum machining parameter settings using BHA. The variations of process parameters with respect to the performance parameters are reported for better and effective understanding of the considered process using single objective at a time. The results obtained using BHA are found better while compared with results of other metaheuristic algorithms, such as, genetic algorithm (GA), artificial bee colony (ABC) and bio-geography based optimization (BBO) attempted by previous researchers.

Co-evolution for Problem Simplification

NASA Technical Reports Server (NTRS)

Haith, Gary L.; Lohn, Jason D.; Cplombano, Silvano P.; Stassinopoulos, Dimitris

1999-01-01

This paper explores a co-evolutionary approach applicable to difficult problems with limited failure/success performance feedback. Like familiar "predator-prey" frameworks this algorithm evolves two populations of individuals - the solutions (predators) and the problems (prey). The approach extends previous work by rewarding only the problems that match their difficulty to the level of solut,ion competence. In complex problem domains with limited feedback, this "tractability constraint" helps provide an adaptive fitness gradient that, effectively differentiates the candidate solutions. The algorithm generates selective pressure toward the evolution of increasingly competent solutions by rewarding solution generality and uniqueness and problem tractability and difficulty. Relative (inverse-fitness) and absolute (static objective function) approaches to evaluating problem difficulty are explored and discussed. On a simple control task, this co-evolutionary algorithm was found to have significant advantages over a genetic algorithm with either a static fitness function or a fitness function that changes on a hand-tuned schedule.

A Laplacian based image filtering using switching noise detector.

PubMed

Ranjbaran, Ali; Hassan, Anwar Hasni Abu; Jafarpour, Mahboobe; Ranjbaran, Bahar

2015-01-01

This paper presents a Laplacian-based image filtering method. Using a local noise estimator function in an energy functional minimizing scheme we show that Laplacian that has been known as an edge detection function can be used for noise removal applications. The algorithm can be implemented on a 3x3 window and easily tuned by number of iterations. Image denoising is simplified to the reduction of the pixels value with their related Laplacian value weighted by local noise estimator. The only parameter which controls smoothness is the number of iterations. Noise reduction quality of the introduced method is evaluated and compared with some classic algorithms like Wiener and Total Variation based filters for Gaussian noise. And also the method compared with the state-of-the-art method BM3D for some images. The algorithm appears to be easy, fast and comparable with many classic denoising algorithms for Gaussian noise.

Unannounced Meals in the Artificial Pancreas: Detection Using Continuous Glucose Monitoring

PubMed Central

Herrero, Pau; Bondia, Jorge

2018-01-01

The artificial pancreas (AP) system is designed to regulate blood glucose in subjects with type 1 diabetes using a continuous glucose monitor informed controller that adjusts insulin infusion via an insulin pump. However, current AP developments are mainly hybrid closed-loop systems that include feed-forward actions triggered by the announcement of meals or exercise. The first step to fully closing the loop in the AP requires removing meal announcement, which is currently the most effective way to alleviate postprandial hyperglycemia due to the delay in insulin action. Here, a novel approach to meal detection in the AP is presented using a sliding window and computing the normalized cross-covariance between measured glucose and the forward difference of a disturbance term, estimated from an augmented minimal model using an Unscented Kalman Filter. Three different tunings were applied to the same meal detection algorithm: (1) a high sensitivity tuning, (2) a trade-off tuning that has a high amount of meals detected and a low amount of false positives (FP), and (3) a low FP tuning. For the three tunings sensitivities 99 ± 2%, 93 ± 5%, and 47 ± 12% were achieved, respectively. A sensitivity analysis was also performed and found that higher carbohydrate quantities and faster rates of glucose appearance result in favorable meal detection outcomes. PMID:29547553

Extracting TSK-type Neuro-Fuzzy model using the Hunting search algorithm

NASA Astrophysics Data System (ADS)

Bouzaida, Sana; Sakly, Anis; M'Sahli, Faouzi

2014-01-01

This paper proposes a Takagi-Sugeno-Kang (TSK) type Neuro-Fuzzy model tuned by a novel metaheuristic optimization algorithm called Hunting Search (HuS). The HuS algorithm is derived based on a model of group hunting of animals such as lions, wolves, and dolphins when looking for a prey. In this study, the structure and parameters of the fuzzy model are encoded into a particle. Thus, the optimal structure and parameters are achieved simultaneously. The proposed method was demonstrated through modeling and control problems, and the results have been compared with other optimization techniques. The comparisons indicate that the proposed method represents a powerful search approach and an effective optimization technique as it can extract the accurate TSK fuzzy model with an appropriate number of rules.

Self-assembly of polyhedral metal-organic framework particles into three-dimensional ordered superstructures

NASA Astrophysics Data System (ADS)

Avci, Civan; Imaz, Inhar; Carné-Sánchez, Arnau; Pariente, Jose Angel; Tasios, Nikos; Pérez-Carvajal, Javier; Alonso, Maria Isabel; Blanco, Alvaro; Dijkstra, Marjolein; López, Cefe; Maspoch, Daniel

2018-01-01

Self-assembly of particles into long-range, three-dimensional, ordered superstructures is crucial for the design of a variety of materials, including plasmonic sensing materials, energy or gas storage systems, catalysts and photonic crystals. Here, we have combined experimental and simulation data to show that truncated rhombic dodecahedral particles of the metal-organic framework (MOF) ZIF-8 can self-assemble into millimetre-sized superstructures with an underlying three-dimensional rhombohedral lattice that behave as photonic crystals. Those superstructures feature a photonic bandgap that can be tuned by controlling the size of the ZIF-8 particles and is also responsive to the adsorption of guest substances in the micropores of the ZIF-8 particles. In addition, superstructures with different lattices can also be assembled by tuning the truncation of ZIF-8 particles, or by using octahedral UiO-66 MOF particles instead. These well-ordered, sub-micrometre-sized superstructures might ultimately facilitate the design of three-dimensional photonic materials for applications in sensing.

Development of a two wheeled self balancing robot with speech recognition and navigation algorithm

NASA Astrophysics Data System (ADS)

Rahman, Md. Muhaimin; Ashik-E-Rasul, Haq, Nowab. Md. Aminul; Hassan, Mehedi; Hasib, Irfan Mohammad Al; Hassan, K. M. Rafidh

2016-07-01

This paper is aimed to discuss modeling, construction and development of navigation algorithm of a two wheeled self balancing mobile robot in an enclosure. In this paper, we have discussed the design of two of the main controller algorithms, namely PID algorithms, on the robot model. Simulation is performed in the SIMULINK environment. The controller is developed primarily for self-balancing of the robot and also it's positioning. As for the navigation in an enclosure, template matching algorithm is proposed for precise measurement of the robot position. The navigation system needs to be calibrated before navigation process starts. Almost all of the earlier template matching algorithms that can be found in the open literature can only trace the robot. But the proposed algorithm here can also locate the position of other objects in an enclosure, like furniture, tables etc. This will enable the robot to know the exact location of every stationary object in the enclosure. Moreover, some additional features, such as Speech Recognition and Object Detection, are added. For Object Detection, the single board Computer Raspberry Pi is used. The system is programmed to analyze images captured via the camera, which are then processed through background subtraction, followed by active noise reduction.

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands.

PubMed

Salem, Salem Ibrahim; Higa, Hiroto; Kim, Hyungjun; Kobayashi, Hiroshi; Oki, Kazuo; Oki, Taikan

2017-07-31

Numerous algorithms have been proposed to retrieve chlorophyll- a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m -3 , 16.25 mg·m -3 , and 19.05 mg·m -3 , respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll- a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m -3 ), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m -3 ).

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands

PubMed Central

Higa, Hiroto; Kobayashi, Hiroshi; Oki, Kazuo

2017-01-01

Numerous algorithms have been proposed to retrieve chlorophyll-a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m−3, 16.25 mg·m−3, and 19.05 mg·m−3, respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll-a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m−3), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m−3). PMID:28758984

Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

NASA Technical Reports Server (NTRS)

Yen, John; Wang, Haojin; Daugherity, Walter C.

1992-01-01

Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.

Tuning the control system of a nonlinear inverted pendulum by means of the new method of Lyapunov exponents estimation

NASA Astrophysics Data System (ADS)

Balcerzak, Marek; Dąbrowski, Artur; Pikunov, Danylo

2018-01-01

This paper presents a practical application of a new, simplified method of Lyapunov exponents estimation. The method has been applied to optimization of a real, nonlinear inverted pendulum system. Authors presented how the algorithm of the Largest Lyapunov Exponent (LLE) estimation can be applied to evaluate control systems performance. The new LLE-based control performance index has been proposed. Equations of the inverted pendulum system of the fourth order have been found. The nonlinear friction of the regulation object has been identified by means of the nonlinear least squares method. Three different friction models have been tested: linear, cubic and Coulomb model. The Differential Evolution (DE) algorithm has been used to search for the best set of parameters of the general linear regulator. This work proves that proposed method is efficient and results in faster perturbation rejection, especially when disturbances are significant.

On the fusion of tuning parameters of fuzzy rules and neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.

State of the art in adaptive control of robotic systems

NASA Technical Reports Server (NTRS)

Tosunoglu, Sabri; Tesar, Delbert

1988-01-01

An up-to-date assessment of adaptive control technology as applied to robotics is presented. Although the field is relatively new and does not yet represent a mature discipline, considerable attention for the design of sophisticated robot controllers has occured. In this presentation, adaptive control methods are divided into model reference adaptive systems and self-tuning regulators, with further definition of various approaches given in each class. The similarity and distinct features of the designed controllers are delineated and tabulated to enhance comparative review.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

PubMed

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Multiclassifier system with hybrid learning applied to the control of bioprosthetic hand.

PubMed

Kurzynski, Marek; Krysmann, Maciej; Trajdos, Pawel; Wolczowski, Andrzej

2016-02-01

In this paper the problem of recognition of the intended hand movements for the control of bio-prosthetic hand is addressed. The proposed method is based on recognition of electromiographic (EMG) and mechanomiographic (MMG) biosignals using a multiclassifier system (MCS) working in a two-level structure with a dynamic ensemble selection (DES) scheme and original concepts of competence function. Additionally, feedback information coming from bioprosthesis sensors on the correct/incorrect classification is applied to the adjustment of the combining mechanism during MCS operation through adaptive tuning competences of base classifiers depending on their decisions. Three MCS systems operating in decision tree structure and with different tuning algorithms are developed. In the MCS1 system, competence is uniformly allocated to each class belonging to the group indicated by the feedback signal. In the MCS2 system, the modification of competence depends on the node of decision tree at which a correct/incorrect classification is made. In the MCS3 system, the randomized model of classifier and the concept of cross-competence are used in the tuning procedure. Experimental investigations on the real data and computer-simulated procedure of generating feedback signals are performed. In these investigations classification accuracy of the MCS systems developed is compared and furthermore, the MCS systems are evaluated with respect to the effectiveness of the procedure of tuning competence. The results obtained indicate that modification of competence of base classifiers during the working phase essentially improves performance of the MCS system and that this improvement depends on the MCS system and tuning method used. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes

NASA Astrophysics Data System (ADS)

Bauer, Johannes; Dávila-Chacón, Jorge; Wermter, Stefan

2015-10-01

Humans and other animals have been shown to perform near-optimally in multi-sensory integration tasks. Probabilistic population codes (PPCs) have been proposed as a mechanism by which optimal integration can be accomplished. Previous approaches have focussed on how neural networks might produce PPCs from sensory input or perform calculations using them, like combining multiple PPCs. Less attention has been given to the question of how the necessary organisation of neurons can arise and how the required knowledge about the input statistics can be learned. In this paper, we propose a model of learning multi-sensory integration based on an unsupervised learning algorithm in which an artificial neural network learns the noise characteristics of each of its sources of input. Our algorithm borrows from the self-organising map the ability to learn latent-variable models of the input and extends it to learning to produce a PPC approximating a probability density function over the latent variable behind its (noisy) input. The neurons in our network are only required to perform simple calculations and we make few assumptions about input noise properties and tuning functions. We report on a neurorobotic experiment in which we apply our algorithm to multi-sensory integration in a humanoid robot to demonstrate its effectiveness and compare it to human multi-sensory integration on the behavioural level. We also show in simulations that our algorithm performs near-optimally under certain plausible conditions, and that it reproduces important aspects of natural multi-sensory integration on the neural level.

Closed Loop System Identification with Genetic Algorithms

NASA Technical Reports Server (NTRS)

Whorton, Mark S.

2004-01-01

High performance control design for a flexible space structure is challenging since high fidelity plant models are di.cult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. Closed loop system identi.cation is often required to obtain a multivariable open loop plant model based on closed-loop response data. In order to provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is employed to mitigate the non-uniqueness and over-parameterization of general state space realizations. This control-relevant system identi.cation procedure stresses the joint nature of the system identi.cation and control design problem by seeking to obtain a model that minimizes the di.erence between the predicted and actual closed-loop performance.

The application of neural network PID controller to control the light gasoline etherification

NASA Astrophysics Data System (ADS)

Cheng, Huanxin; Zhang, Yimin; Kong, Lingling; Meng, Xiangyong

2017-06-01

Light gasoline etherification technology can effectively improve the quality of gasoline, which is environmental- friendly and economical. By combining BP neural network and PID control and using BP neural network self-learning ability for online parameter tuning, this method optimizes the parameters of PID controller and applies this to the Fcc gas flow control to achieve the control of the final product- heavy oil concentration. Finally, through MATLAB simulation, it is found that the PID control based on BP neural network has better controlling effect than traditional PID control.

Trajectory Tracking of a Planer Parallel Manipulator by Using Computed Force Control Method

NASA Astrophysics Data System (ADS)

Bayram, Atilla

2017-03-01

Despite small workspace, parallel manipulators have some advantages over their serial counterparts in terms of higher speed, acceleration, rigidity, accuracy, manufacturing cost and payload. Accordingly, this type of manipulators can be used in many applications such as in high-speed machine tools, tuning machine for feeding, sensitive cutting, assembly and packaging. This paper presents a special type of planar parallel manipulator with three degrees of freedom. It is constructed as a variable geometry truss generally known planar Stewart platform. The reachable and orientation workspaces are obtained for this manipulator. The inverse kinematic analysis is solved for the trajectory tracking according to the redundancy and joint limit avoidance. Then, the dynamics model of the manipulator is established by using Virtual Work method. The simulations are performed to follow the given planar trajectories by using the dynamic equations of the variable geometry truss manipulator and computed force control method. In computed force control method, the feedback gain matrices for PD control are tuned with fixed matrices by trail end error and variable ones by means of optimization with genetic algorithm.

Controlled Self-Assembly of Cyclophane Amphiphiles: From 1D Nanofibers to Ultrathin 2D Topological Structures

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

Cai, Zhengxu; Li, Lianwei; Lo, Wai-Yip

2016-07-05

A novel series of amphiphilic TC-PEG molecules were designed and synthesized based on the orthogonal cyclophane unit. These molecules were able to self-assemble from 1D nanofibers and nanobelts to 2D ultrathin nanosheets (3 nm thick) in a controlled way by tuning the length of PEG side chains. The special structure of the cyclophane moiety allowed control in construction of nanostructures through programmed noncovalent interactions (hydrophobic hydrophilic interaction and pi-pi interaction). The self-assembled nanostructures were characterized by combining real space imaging (TEM, SEM, and AFM) and reciprocal space scattering (GIWAXS) techniques. This unique supramolecular system may provide a new strategy formore » the design of materials with tunable nanomorphology and functionality.« less

Generalized Momentum Control of the Spin-Stabilized Magnetospheric Multiscale Formation

NASA Technical Reports Server (NTRS)

Queen, Steven Z.; Shah, Neerav; Benegalrao, Suyog S.; Blackman, Kathie

2015-01-01

The Magnetospheric Multiscale (MMS) mission consists of four identically instrumented, spin-stabilized observatories elliptically orbiting the Earth in a tetrahedron formation. The on-board attitude control system adjusts the angular momentum of the system using a generalized thruster-actuated control system that simultaneously manages precession, nutation and spin. Originally developed using Lyapunov control-theory with rate-feedback, a published algorithm has been augmented to provide a balanced attitude/rate response using a single weighting parameter. This approach overcomes an orientation sign-ambiguity in the existing formulation, and also allows for a smoothly tuned-response applicable to both a compact/agile spacecraft, as well as one with large articulating appendages.

Molecular self-assembly approaches for supramolecular electronic and organic electronic devices

NASA Astrophysics Data System (ADS)

Yip, Hin-Lap

Molecular self-assembly represents an efficient bottom-up strategy to generate structurally well-defined aggregates of semiconducting pi-conjugated materials. The capability of tuning the chemical structures, intermolecular interactions and nanostructures through molecular engineering and novel materials processing renders it possible to tailor a large number of unprecedented properties such as charge transport, energy transfer and light harvesting. This approach does not only benefit traditional electronic devices based on bulk materials, but also generate a new research area so called "supramolecular electronics" in which electronic devices are built up with individual supramolecular nanostructures with size in the sub-hundred nanometers range. My work combined molecular self-assembly together with several novel materials processing techniques to control the nucleation and growth of organic semiconducting nanostructures from different type of pi-conjugated materials. By tailoring the interactions between the molecules using hydrogen bonds and pi-pi stacking, semiconducting nanoplatelets and nanowires with tunable sizes can be fabricated in solution. These supramolecular nanostructures were further patterned and aligned on solid substrates through printing and chemical templating methods. The capability to control the different hierarchies of organization on surface provides an important platform to study their structural-induced electronic properties. In addition to using molecular self-assembly to create different organic nanostructures, functional self-assembled monolayer (SAM) formed by spontaneous chemisorption on surfaces was used to tune the interfacial property in organic solar cells. Devices showed dramatically improved performance when appropriate SAMs were applied to optimize the contact property for efficiency charge collection.

On the integration of reinforcement learning and approximate reasoning for control

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1991-01-01

The author discusses the importance of strengthening the knowledge representation characteristic of reinforcement learning techniques using methods such as approximate reasoning. The ARIC (approximate reasoning-based intelligent control) architecture is an example of such a hybrid approach in which the fuzzy control rules are modified (fine-tuned) using reinforcement learning. ARIC also demonstrates that it is possible to start with an approximately correct control knowledge base and learn to refine this knowledge through further experience. On the other hand, techniques such as the TD (temporal difference) algorithm and Q-learning establish stronger theoretical foundations for their use in adaptive control and also in stability analysis of hybrid reinforcement learning and approximate reasoning-based controllers.

Efficiency Analysis of the Parallel Implementation of the SIMPLE Algorithm on Multiprocessor Computers

NASA Astrophysics Data System (ADS)

Lashkin, S. V.; Kozelkov, A. S.; Yalozo, A. V.; Gerasimov, V. Yu.; Zelensky, D. K.

2017-12-01

This paper describes the details of the parallel implementation of the SIMPLE algorithm for numerical solution of the Navier-Stokes system of equations on arbitrary unstructured grids. The iteration schemes for the serial and parallel versions of the SIMPLE algorithm are implemented. In the description of the parallel implementation, special attention is paid to computational data exchange among processors under the condition of the grid model decomposition using fictitious cells. We discuss the specific features for the storage of distributed matrices and implementation of vector-matrix operations in parallel mode. It is shown that the proposed way of matrix storage reduces the number of interprocessor exchanges. A series of numerical experiments illustrates the effect of the multigrid SLAE solver tuning on the general efficiency of the algorithm; the tuning involves the types of the cycles used (V, W, and F), the number of iterations of a smoothing operator, and the number of cells for coarsening. Two ways (direct and indirect) of efficiency evaluation for parallelization of the numerical algorithm are demonstrated. The paper presents the results of solving some internal and external flow problems with the evaluation of parallelization efficiency by two algorithms. It is shown that the proposed parallel implementation enables efficient computations for the problems on a thousand processors. Based on the results obtained, some general recommendations are made for the optimal tuning of the multigrid solver, as well as for selecting the optimal number of cells per processor.

AUTOMOTIVE DIESEL MAINTENANCE 1. UNIT VII, ENGINE TUNE-UP--DETROIT DIESEL ENGINE.

ERIC Educational Resources Information Center

Human Engineering Inst., Cleveland, OH.

THIS MODULE OF A 30-MODULE COURSE IS DESIGNED TO DEVELOP AN UNDERSTANDING OF TUNE-UP PROCEDURES FOR DIESEL ENGINES. TOPICS ARE SCHEDULING TUNE-UPS, AND TUNE-UP PROCEDURES. THE MODULE CONSISTS OF A SELF-INSTRUCTIONAL BRANCH PROGRAMED TRAINING FILM "ENGINE TUNE-UP--DETROIT DIESEL ENGINE" AND OTHER MATERIALS. SEE VT 005 655 FOR FURTHER INFORMATION.…

Crystal Growth Control

NASA Technical Reports Server (NTRS)

Duval, Walter M. B.; Batur, Celal; Bennett, Robert J.

1997-01-01

We present an innovative design of a vertical transparent multizone furnace which can operate in the temperature range of 25 C to 750 C and deliver thermal gradients of 2 C/cm to 45 C/cm for the commercial applications to crystal growth. The operation of the eight zone furnace is based on a self-tuning temperature control system with a DC power supply for optimal thermal stability. We show that the desired thermal profile over the entire length of the furnace consists of a functional combination of the fundamental thermal profiles for each individual zone obtained by setting the set-point temperature for that zone. The self-tuning system accounts for the zone to zone thermal interactions. The control system operates such that the thermal profile is maintained under thermal load, thus boundary conditions on crystal growth ampoules can be predetermined prior to crystal growth. Temperature profiles for the growth of crystals via directional solidification, vapor transport techniques, and multiple gradient applications are shown to be easily implemented. The unique feature of its transparency and ease of programming thermal profiles make the furnace useful for scientific and commercial applications for the determination of process parameters to optimize crystal growth conditions.

Implementation of an Integrated On-Board Aircraft Engine Diagnostic Architecture

NASA Technical Reports Server (NTRS)

Armstrong, Jeffrey B.; Simon, Donald L.

2012-01-01

An on-board diagnostic architecture for aircraft turbofan engine performance trending, parameter estimation, and gas-path fault detection and isolation has been developed and evaluated in a simulation environment. The architecture incorporates two independent models: a realtime self-tuning performance model providing parameter estimates and a performance baseline model for diagnostic purposes reflecting long-term engine degradation trends. This architecture was evaluated using flight profiles generated from a nonlinear model with realistic fleet engine health degradation distributions and sensor noise. The architecture was found to produce acceptable estimates of engine health and unmeasured parameters, and the integrated diagnostic algorithms were able to perform correct fault isolation in approximately 70 percent of the tested cases

Enhanced Self Tuning On-Board Real-Time Model (eSTORM) for Aircraft Engine Performance Health Tracking

NASA Technical Reports Server (NTRS)

Volponi, Al; Simon, Donald L. (Technical Monitor)

2008-01-01

A key technological concept for producing reliable engine diagnostics and prognostics exploits the benefits of fusing sensor data, information, and/or processing algorithms. This report describes the development of a hybrid engine model for a propulsion gas turbine engine, which is the result of fusing two diverse modeling methodologies: a physics-based model approach and an empirical model approach. The report describes the process and methods involved in deriving and implementing a hybrid model configuration for a commercial turbofan engine. Among the intended uses for such a model is to enable real-time, on-board tracking of engine module performance changes and engine parameter synthesis for fault detection and accommodation.

Human-based percussion and self-similarity detection in electroacoustic music

NASA Astrophysics Data System (ADS)

Mills, John Anderson, III

Electroacoustic music is music that uses electronic technology for the compositional manipulation of sound, and is a unique genre of music for many reasons. Analyzing electroacoustic music requires special measures, some of which are integrated into the design of a preliminary percussion analysis tool set for electroacoustic music. This tool set is designed to incorporate the human processing of music and sound. Models of the human auditory periphery are used as a front end to the analysis algorithms. The audio properties of percussivity and self-similarity are chosen as the focus because these properties are computable and informative. A collection of human judgments about percussion was undertaken to acquire clearly specified, sound-event dimensions that humans use as a percussive cue. A total of 29 participants was asked to make judgments about the percussivity of 360 pairs of synthesized snare-drum sounds. The grouped results indicate that of the dimensions tested rise time is the strongest cue for percussivity. String resonance also has a strong effect, but because of the complex nature of string resonance, it is not a fundamental dimension of a sound event. Gross spectral filtering also has an effect on the judgment of percussivity but the effect is weaker than for rise time and string resonance. Gross spectral filtering also has less effect when the stronger cue of rise time is modified simultaneously. A percussivity-profile algorithm (PPA) is designed to identify those instants in pieces of music that humans also would identify as percussive. The PPA is implemented using a time-domain, channel-based approach and psychoacoustic models. The input parameters are tuned to maximize performance at matching participants' choices in the percussion-judgment collection. After the PPA is tuned, the PPA then is used to analyze pieces of electroacoustic music. Real electroacoustic music introduces new challenges for the PPA, though those same challenges might affect human judgment as well. A similarity matrix is combined with the PPA in order to find self-similarity in the percussive sounds of electroacoustic music. This percussive similarity matrix is then used to identify structural characteristics in two pieces of electroacoustic music.

BBU and Corkscrew Growth Predictions for the Darht Second Axis Accelerator

NASA Astrophysics Data System (ADS)

Chen, Y. J.; Fawley, W. M.

2001-06-01

This paper discusses the means by which we plan to control BBU and corkscrew growth in DARHT-II. In section 2 we present the current design for the solenoidal field tune; since the last PAC meeting in 1999, the design beam current has been lowered from 4 to 2 kA which has lowered the necessary field strengths. In Sec. 3 we discuss the present predictions for the expected BBU growth; these predictions were made having used recent experimental measurements for the impedance of the DARHT-II accelerator cells. Finally, in Sec. 4 we present our most recent calculations for the expected corkscrew growth and also the expected performance of the tuning-V algorithm, which can reduce this growth by more than an order of magnitude.

Design of static synchronous series compensator based damping controller employing invasive weed optimization algorithm.

PubMed

Ahmed, Ashik; Al-Amin, Rasheduzzaman; Amin, Ruhul

2014-01-01

This paper proposes designing of Static Synchronous Series Compensator (SSSC) based damping controller to enhance the stability of a Single Machine Infinite Bus (SMIB) system by means of Invasive Weed Optimization (IWO) technique. Conventional PI controller is used as the SSSC damping controller which takes rotor speed deviation as the input. The damping controller parameters are tuned based on time integral of absolute error based cost function using IWO. Performance of IWO based controller is compared to that of Particle Swarm Optimization (PSO) based controller. Time domain based simulation results are presented and performance of the controllers under different loading conditions and fault scenarios is studied in order to illustrate the effectiveness of the IWO based design approach.

Control algorithms of SONET integrated self-healing networks

NASA Astrophysics Data System (ADS)

Hasegawa, Satoshi; Okaoue, Yasuyo; Egawa, Takashi; Sakauchi, Hideki

1994-01-01

As the deployment of high-speed fiber transmission systems has been accelerated, they are widely recognized as a firm infrastructure of information society. Under this circumstance, the importance of network survivability has been increasing rapidly in these days. In SONET, the self-healing networks have been highlighted as one of the most advanced mechanisms to realize SONET survivable networks. Several schemes have been proposed and studied actively due to a rapid progress on the development of highly intelligent NE's. Among them in this paper, a DCS based distributed self-healing network is discussed from a viewpoint of its control algorithms. Specifically, our self-healing algorithm called TRANS is explained in detail, which possesses such desirable features as providing fast and flexible restoration with line and path level restoration applied to an individual STS-1 channel, capability to handle multiple and even node failures, and so on. Both software simulation and hardware experiment verify that TRANS works properly in a real distributed environment, the result of which is shown in the paper. In addition, the combined use of TRANS and the ring restoration control is proposed taking into account the use in a practical SONET.

Toward control of the metal-organic interfacial electronic structure in molecular electronics: a first-principles study on self-assembled monolayers of pi-conjugated molecules on noble metals.

PubMed

Heimel, Georg; Romaner, Lorenz; Zojer, Egbert; Brédas, Jean-Luc

2007-04-01

Self-assembled monolayers (SAMs) of organic molecules provide an important tool to tune the work function of electrodes in plastic electronics and significantly improve device performance. Also, the energetic alignment of the frontier molecular orbitals in the SAM with the Fermi energy of a metal electrode dominates charge transport in single-molecule devices. On the basis of first-principles calculations on SAMs of pi-conjugated molecules on noble metals, we provide a detailed description of the mechanisms that give rise to and intrinsically link these interfacial phenomena at the atomic level. The docking chemistry on the metal side of the SAM determines the level alignment, while chemical modifications on the far side provide an additional, independent handle to modify the substrate work function; both aspects can be tuned over several eV. The comprehensive picture established in this work provides valuable guidelines for controlling charge-carrier injection in organic electronics and current-voltage characteristics in single-molecule devices.

Shutter heating system of Antarctic bright star survey telescope

NASA Astrophysics Data System (ADS)

Chen, Jie; Dong, Shucheng; Jiang, Fengxin; Zhang, Hongfei; Wang, Jian

2016-07-01

A heat preservation system for mechanical shutter in Antarctic is introduced in the paper. The system consists of the heat preservation chamber, the host controller STM32F103C8T6 with peripheral circuit and the control algorithm. The whole design is carried out on the basis of the low temperature requirement, including the cavity structure and thermal insulation. The heat preservation chamber is used to keep the shutter warm and support the weight of the camera. Using PT100 as the temperature sensor, the signal processing circuit converts the temperature to the voltage which is then digitized by the 12 bit ADC in the STM32. The host controller transforms the voltage data into temperature, and through the tuning of the Fussy PID algorithm which controls the duty cycle of the MOSFET, the temperature control of chamber is realized. The System has been tested in the cryogenic environment for a long time, with characteristic of low temperature resistance, small volume, high accuracy of temperature control as well as remote control and detection.

New self-limiting assembly model for Si quantum rings on Si(100).

PubMed

Yu, L W; Chen, K J; Song, J; Xu, J; Li, W; Li, X F; Wang, J M; Huang, X F

2007-04-20

We propose a new self-limiting assembly model for Si quantum rings on Si(100) where the ring's formation and evolution are driven by a growth-etching competition mechanism. The as-grown ring structure in a plasma enhanced chemical vapor deposition system has excellent rotational symmetry and superior morphology with a typical diameter, edge width, and height of 150-300, 10, and 5 nm, respectively. Based on this model, the size and morphology can be controlled well by simply tuning the timing procedure. We suggest that this growth model is not limited to certain material system, but provides a general scheme to control and tailor the self-assembly nanostructures into the desired size, shape, and complexity.

Enhanced robust fractional order proportional-plus-integral controller based on neural network for velocity control of permanent magnet synchronous motor.

PubMed

Zhang, Bitao; Pi, YouGuo

2013-07-01

The traditional integer order proportional-integral-differential (IO-PID) controller is sensitive to the parameter variation or/and external load disturbance of permanent magnet synchronous motor (PMSM). And the fractional order proportional-integral-differential (FO-PID) control scheme based on robustness tuning method is proposed to enhance the robustness. But the robustness focuses on the open-loop gain variation of controlled plant. In this paper, an enhanced robust fractional order proportional-plus-integral (ERFOPI) controller based on neural network is proposed. The control law of the ERFOPI controller is acted on a fractional order implement function (FOIF) of tracking error but not tracking error directly, which, according to theory analysis, can enhance the robust performance of system. Tuning rules and approaches, based on phase margin, crossover frequency specification and robustness rejecting gain variation, are introduced to obtain the parameters of ERFOPI controller. And the neural network algorithm is used to adjust the parameter of FOIF. Simulation and experimental results show that the method proposed in this paper not only achieve favorable tracking performance, but also is robust with regard to external load disturbance and parameter variation. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Nanogenerators and Piezotronics

NASA Astrophysics Data System (ADS)

Wang, Zhong Lin

2011-03-01

Developing wireless nanodevices and nanosystems is of critical importance for sensing, medical science, environmental/infrastructure monitoring, defense technology and even personal electronics. It is highly desirable for wireless devices to be self-powered without using battery. This is a new initiative in today's energy research for mico/nano-systems in searching for sustainable self-sufficient power sources. We have invented an innovative approach for converting nano-scale mechanical energy into electric energy by piezoelectric zinc oxide nanowire arrays. As today, a gentle straining can output 1-3 V from an integrated nanogenerator, using which a self-powered nanosensor has been demonstrated. A commercial LED has been lid up [3-5]. Due to the polarization of ions in a crystal that has non-central symmetry, a piezoelectric potential (piezopotential) is created in the crystal by applying a stress. The effect of piezopotential to the transport behavior of charge carriers is significant due to their multiple functionalities of piezoelectricity, semiconductor and photon excitation. Electronics fabricated by using inner-crystal piezopotential as a ``gate'' voltage to tune/control the charge transport behavior is named piezotronics [6,7].Piezo-phototronic effect is a result of three-way coupling among piezoelectricity, photonic excitation and semiconductor transport, which allows tuning and controlling of electro-optical processes by strain induced piezopotential.

Intelligent neural network and fuzzy logic control of industrial and power systems

NASA Astrophysics Data System (ADS)

Kuljaca, Ognjen

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of adaptive and neural network control systems, as well as for the analysis of the different algorithms such as elastic fuzzy systems.

Longitudinal Control for Mengshi Autonomous Vehicle via Cloud Model

NASA Astrophysics Data System (ADS)

Gao, H. B.; Zhang, X. Y.; Li, D. Y.; Liu, Y. C.

2018-03-01

Dynamic robustness and stability control is a requirement for self-driving of autonomous vehicle. Longitudinal control method of autonomous is a key technique which has drawn the attention of industry and academe. In this paper, we present a longitudinal control algorithm based on cloud model for Mengshi autonomous vehicle to ensure the dynamic stability and tracking performance of Mengshi autonomous vehicle. An experiments is applied to test the implementation of the longitudinal control algorithm. Empirical results show that if the longitudinal control algorithm based Gauss cloud model are applied to calculate the acceleration, and the vehicles drive at different speeds, a stable longitudinal control effect is achieved.

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.

Design, implementation and application of distributed order PI control.

PubMed

Zhou, Fengyu; Zhao, Yang; Li, Yan; Chen, YangQuan

2013-05-01

In this paper, a series of distributed order PI controller design methods are derived and applied to the robust control of wheeled service robots, which can tolerate more structural and parametric uncertainties than the corresponding fractional order PI control. A practical discrete incremental distributed order PI control strategy is proposed basing on the discretization method and the frequency criterions, which can be commonly used in many fields of fractional order system, control and signal processing. Besides, an auto-tuning strategy and the genetic algorithm are applied to the distributed order PI control as well. A number of experimental results are provided to show the advantages and distinguished features of the discussed methods in fairways. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Advanced Interval Type-2 Fuzzy Sliding Mode Control for Robot Manipulator.

PubMed

Hwang, Ji-Hwan; Kang, Young-Chang; Park, Jong-Wook; Kim, Dong W

2017-01-01

In this paper, advanced interval type-2 fuzzy sliding mode control (AIT2FSMC) for robot manipulator is proposed. The proposed AIT2FSMC is a combination of interval type-2 fuzzy system and sliding mode control. For resembling a feedback linearization (FL) control law, interval type-2 fuzzy system is designed. For compensating the approximation error between the FL control law and interval type-2 fuzzy system, sliding mode controller is designed, respectively. The tuning algorithms are derived in the sense of Lyapunov stability theorem. Two-link rigid robot manipulator with nonlinearity is used to test and the simulation results are presented to show the effectiveness of the proposed method that can control unknown system well.

Self-ordered, controlled structure nanoporous membranes using constant current anodization.

PubMed

Lee, Kwan; Tang, Yun; Ouyang, Min

2008-12-01

We report a constant current (CC) based anodization technique to fabricate and control structure of mechanically stable anodic aluminum oxide (AAO) membranes with a long-range ordered hexagonal nanopore pattern. For the first time we show that interpore distance (Dint) of a self-ordered nanopore feature can be continuously tuned over a broad range with CC anodization and is uniquely defined by the conductivity of sulfuric acid as electrolyte. We further demonstrate that this technique can offer new degrees of freedom for engineering planar nanopore structures by fine tailoring the CC based anodization process. Our results not only facilitate further understanding of self-ordering mechanism of alumina membranes but also provide a fast, simple (without requirement of prepatterning or preoxide layer), and flexible methodology for controlling complex nanoporous structures, thus offering promising practical applications in nanotechnology.

Handling Data Skew in MapReduce Cluster by Using Partition Tuning

PubMed

Gao, Yufei; Zhou, Yanjie; Zhou, Bing; Shi, Lei; Zhang, Jiacai

2017-01-01

The healthcare industry has generated large amounts of data, and analyzing these has emerged as an important problem in recent years. The MapReduce programming model has been successfully used for big data analytics. However, data skew invariably occurs in big data analytics and seriously affects efficiency. To overcome the data skew problem in MapReduce, we have in the past proposed a data processing algorithm called Partition Tuning-based Skew Handling (PTSH). In comparison with the one-stage partitioning strategy used in the traditional MapReduce model, PTSH uses a two-stage strategy and the partition tuning method to disperse key-value pairs in virtual partitions and recombines each partition in case of data skew. The robustness and efficiency of the proposed algorithm were tested on a wide variety of simulated datasets and real healthcare datasets. The results showed that PTSH algorithm can handle data skew in MapReduce efficiently and improve the performance of MapReduce jobs in comparison with the native Hadoop, Closer, and locality-aware and fairness-aware key partitioning (LEEN). We also found that the time needed for rule extraction can be reduced significantly by adopting the PTSH algorithm, since it is more suitable for association rule mining (ARM) on healthcare data. © 2017 Yufei Gao et al.

Handling Data Skew in MapReduce Cluster by Using Partition Tuning.

PubMed

Gao, Yufei; Zhou, Yanjie; Zhou, Bing; Shi, Lei; Zhang, Jiacai

2017-01-01

The healthcare industry has generated large amounts of data, and analyzing these has emerged as an important problem in recent years. The MapReduce programming model has been successfully used for big data analytics. However, data skew invariably occurs in big data analytics and seriously affects efficiency. To overcome the data skew problem in MapReduce, we have in the past proposed a data processing algorithm called Partition Tuning-based Skew Handling (PTSH). In comparison with the one-stage partitioning strategy used in the traditional MapReduce model, PTSH uses a two-stage strategy and the partition tuning method to disperse key-value pairs in virtual partitions and recombines each partition in case of data skew. The robustness and efficiency of the proposed algorithm were tested on a wide variety of simulated datasets and real healthcare datasets. The results showed that PTSH algorithm can handle data skew in MapReduce efficiently and improve the performance of MapReduce jobs in comparison with the native Hadoop, Closer, and locality-aware and fairness-aware key partitioning (LEEN). We also found that the time needed for rule extraction can be reduced significantly by adopting the PTSH algorithm, since it is more suitable for association rule mining (ARM) on healthcare data.

Handling Data Skew in MapReduce Cluster by Using Partition Tuning

PubMed Central

Zhou, Yanjie; Zhou, Bing; Shi, Lei

2017-01-01

The healthcare industry has generated large amounts of data, and analyzing these has emerged as an important problem in recent years. The MapReduce programming model has been successfully used for big data analytics. However, data skew invariably occurs in big data analytics and seriously affects efficiency. To overcome the data skew problem in MapReduce, we have in the past proposed a data processing algorithm called Partition Tuning-based Skew Handling (PTSH). In comparison with the one-stage partitioning strategy used in the traditional MapReduce model, PTSH uses a two-stage strategy and the partition tuning method to disperse key-value pairs in virtual partitions and recombines each partition in case of data skew. The robustness and efficiency of the proposed algorithm were tested on a wide variety of simulated datasets and real healthcare datasets. The results showed that PTSH algorithm can handle data skew in MapReduce efficiently and improve the performance of MapReduce jobs in comparison with the native Hadoop, Closer, and locality-aware and fairness-aware key partitioning (LEEN). We also found that the time needed for rule extraction can be reduced significantly by adopting the PTSH algorithm, since it is more suitable for association rule mining (ARM) on healthcare data. PMID:29065568

Output Feedback Stabilization for a Class of Multi-Variable Bilinear Stochastic Systems with Stochastic Coupling Attenuation

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

Zhang, Qichun; Zhou, Jinglin; Wang, Hong

In this paper, stochastic coupling attenuation is investigated for a class of multi-variable bilinear stochastic systems and a novel output feedback m-block backstepping controller with linear estimator is designed, where gradient descent optimization is used to tune the design parameters of the controller. It has been shown that the trajectories of the closed-loop stochastic systems are bounded in probability sense and the stochastic coupling of the system outputs can be effectively attenuated by the proposed control algorithm. Moreover, the stability of the stochastic systems is analyzed and the effectiveness of the proposed method has been demonstrated using a simulated example.

A Parameter Tuning Scheme of Sea-ice Model Based on Automatic Differentiation Technique

NASA Astrophysics Data System (ADS)

Kim, J. G.; Hovland, P. D.

2001-05-01

Automatic diferentiation (AD) technique was used to illustrate a new approach for parameter tuning scheme of an uncoupled sea-ice model. Atmospheric forcing field of 1992 obtained from NCEP data was used as enforcing variables in the study. The simulation results were compared with the observed ice movement provided by the International Arctic Buoy Programme (IABP). All of the numerical experiments were based on a widely used dynamic and thermodynamic model for simulating the seasonal sea-ice chnage of the main Arctic ocean. We selected five dynamic and thermodynamic parameters for the tuning process in which the cost function defined by the norm of the difference between observed and simulated ice drift locations was minimized. The selected parameters are the air and ocean drag coefficients, the ice strength constant, the turning angle at ice-air/ocean interface, and the bulk sensible heat transfer coefficient. The drag coefficients were the major parameters to control sea-ice movement and extent. The result of the study shows that more realistic simulations of ice thickness distribution was produced by tuning the simulated ice drift trajectories. In the tuning process, the L-BFCGS-B minimization algorithm of a quasi-Newton method was used. The derivative information required in the minimization iterations was provided by the AD processed Fortran code. Compared with a conventional approach, AD generated derivative code provided fast and robust computations of derivative information.

Efficient receiver tuning using differential evolution strategies

NASA Astrophysics Data System (ADS)

Wheeler, Caleb H.; Toland, Trevor G.

2016-08-01

Differential evolution (DE) is a powerful and computationally inexpensive optimization strategy that can be used to search an entire parameter space or to converge quickly on a solution. The Kilopixel Array Pathfinder Project (KAPPa) is a heterodyne receiver system delivering 5 GHz of instantaneous bandwidth in the tuning range of 645-695 GHz. The fully automated KAPPa receiver test system finds optimal receiver tuning using performance feedback and DE. We present an adaptation of DE for use in rapid receiver characterization. The KAPPa DE algorithm is written in Python 2.7 and is fully integrated with the KAPPa instrument control, data processing, and visualization code. KAPPa develops the technologies needed to realize heterodyne focal plane arrays containing 1000 pixels. Finding optimal receiver tuning by investigating large parameter spaces is one of many challenges facing the characterization phase of KAPPa. This is a difficult task via by-hand techniques. Characterizing or tuning in an automated fashion without need for human intervention is desirable for future large scale arrays. While many optimization strategies exist, DE is ideal for time and performance constraints because it can be set to converge to a solution rapidly with minimal computational overhead. We discuss how DE is utilized in the KAPPa system and discuss its performance and look toward the future of 1000 pixel array receivers and consider how the KAPPa DE system might be applied.

On the effect of response transformations in sequential parameter optimization.

PubMed

Wagner, Tobias; Wessing, Simon

2012-01-01

Parameter tuning of evolutionary algorithms (EAs) is attracting more and more interest. In particular, the sequential parameter optimization (SPO) framework for the model-assisted tuning of stochastic optimizers has resulted in established parameter tuning algorithms. In this paper, we enhance the SPO framework by introducing transformation steps before the response aggregation and before the actual modeling. Based on design-of-experiments techniques, we empirically analyze the effect of integrating different transformations. We show that in particular, a rank transformation of the responses provides significant improvements. A deeper analysis of the resulting models and additional experiments with adaptive procedures indicates that the rank and the Box-Cox transformation are able to improve the properties of the resultant distributions with respect to symmetry and normality of the residuals. Moreover, model-based effect plots document a higher discriminatory power obtained by the rank transformation.

Semi-Global Matching with Self-Adjusting Penalties

NASA Astrophysics Data System (ADS)

Karkalou, E.; Stentoumis, C.; Karras, G.

2017-02-01

The demand for 3D models of various scales and precisions is strong for a wide range of applications, among which cultural heritage recording is particularly important and challenging. In this context, dense image matching is a fundamental task for processes which involve image-based reconstruction of 3D models. Despite the existence of commercial software, the need for complete and accurate results under different conditions, as well as for computational efficiency under a variety of hardware, has kept image-matching algorithms as one of the most active research topics. Semi-global matching (SGM) is among the most popular optimization algorithms due to its accuracy, computational efficiency, and simplicity. A challenging aspect in SGM implementation is the determination of smoothness constraints, i.e. penalties P1, P2 for disparity changes and discontinuities. In fact, penalty adjustment is needed for every particular stereo-pair and cost computation. In this work, a novel formulation of self-adjusting penalties is proposed: SGM penalties can be estimated solely from the statistical properties of the initial disparity space image. The proposed method of self-adjusting penalties (SGM-SAP) is evaluated using typical cost functions on stereo-pairs from the recent Middlebury dataset of interior scenes, as well as from the EPFL Herz-Jesu architectural scenes. Results are competitive against the original SGM estimates. The significant aspects of self-adjusting penalties are: (i) the time-consuming tuning process is avoided; (ii) SGM can be used in image collections with limited number of stereo-pairs; and (iii) no heuristic user intervention is needed.

Implantable control, telemetry, and solar energy system in the moving actuator type total artificial heart.

PubMed

Ahn, J M; Lee, J H; Choi, S W; Kim, W E; Omn, K S; Park, S K; Kim, W G; Roh, J R; Min, B G

1998-03-01

The moving actuator type total artificial heart (TAH) developed in the Seoul National University has numerous design improvements based upon the digital signal processor (DSP). These improvements include the implantability of all electronics, an automatic control algorithm, and extension of the battery run-time in connection with an amorphous silicon solar system (SS). The implantable electronics consist of the motor drive, main processor, intelligent Li ion battery management (LIBM) based upon the DSP, telemetry system, and transcutaneous energy transmission (TET) system. Major changes in the implantable electronics include decreasing the temperature rise by over 21 degrees C on the motor drive, volume reduction (40 x 55 x 33 mm, 7 cell assembly) of the battery pack using a Li ion (3.6 V/cell, 900 mA.h), and improvement of the battery run-time (over 40 min) while providing the cardiac output (CO) of 5 L/min at 100 mm Hg afterload when the external battery for testing is connected with the SS (2.5 W, 192.192, 1 kg) for the external battery recharge or the partial TAH drive. The phase locked loop (PLL) based telemetry system was implemented to improve stability and the error correction DSP algorithm programmed to achieve high accuracy. A field focused light emitting diode (LED) was used to obtain low light scattering along the propagation path, similar to the optical property of the laser and miniature sized, mounted on the pancake type TET coils. The TET operating resonance frequency was self tuned in a range of 360 to 410 kHz to provide enough power even at high afterloads. An automatic cardiac output regulation algorithm was developed based on interventricular pressure analysis and carried out in several animal experiments successfully. All electronics have been evaluated in vitro and in vivo and prepared for implantation of the TAH. Substantial progress has been made in designing a completely implantable TAH at the preclinical stage.

Learning and Tuning of Fuzzy Rules

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1997-01-01

In this chapter, we review some of the current techniques for learning and tuning fuzzy rules. For clarity, we refer to the process of generating rules from data as the learning problem and distinguish it from tuning an already existing set of fuzzy rules. For learning, we touch on unsupervised learning techniques such as fuzzy c-means, fuzzy decision tree systems, fuzzy genetic algorithms, and linear fuzzy rules generation methods. For tuning, we discuss Jang's ANFIS architecture, Berenji-Khedkar's GARIC architecture and its extensions in GARIC-Q. We show that the hybrid techniques capable of learning and tuning fuzzy rules, such as CART-ANFIS, RNN-FLCS, and GARIC-RB, are desirable in development of a number of future intelligent systems.

Implementation of a state of the art automated system for the production of cloud/water vapor motion winds from geostationary satellites

NASA Technical Reports Server (NTRS)

Velden, Christopher

1995-01-01

The research objectives in this proposal were part of a continuing program at UW-CIMSS to develop and refine an automated geostationary satellite winds processing system which can be utilized in both research and operational environments. The majority of the originally proposed tasks were successfully accomplished, and in some cases the progress exceeded the original goals. Much of the research and development supported by this grant resulted in upgrades and modifications to the existing automated satellite winds tracking algorithm. These modifications were put to the test through case study demonstrations and numerical model impact studies. After being successfully demonstrated, the modifications and upgrades were implemented into the NESDIS algorithms in Washington DC, and have become part of the operational support. A major focus of the research supported under this grant attended to the continued development of water vapor tracked winds from geostationary observations. The fully automated UW-CIMSS tracking algorithm has been tuned to provide complete upper-tropospheric coverage from this data source, with data set quality close to that of operational cloud motion winds. Multispectral water vapor observations were collected and processed from several different geostationary satellites. The tracking and quality control algorithms were tuned and refined based on ground-truth comparisons and case studies involving impact on numerical model analyses and forecasts. The results have shown the water vapor motion winds are of good quality, complement the cloud motion wind data, and can have a positive impact in NWP on many meteorological scales.

Fuzzy self-learning control for magnetic servo system

NASA Technical Reports Server (NTRS)

Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

1994-01-01

It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

Identification and control of plasma vertical position using neural network in Damavand tokamak.

PubMed

Rasouli, H; Rasouli, C; Koohi, A

2013-02-01

In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

Note: Broadly tunable all-fiber ytterbium laser with 0.05 nm spectral width based on multimode interference filter.

PubMed

Mukhopadhyay, Pranab K; Gupta, Pradeep K; Singh, Amarjeet; Sharma, Sunil K; Bindra, Kushvinder S; Oak, Shrikant M

2014-05-01

A multimode interference filter with narrow transmission bandwidth and large self-imaging wavelength interval is constructed and implemented in an ytterbium doped fiber laser in all-fiber format for broad wavelength tunability as well as narrow spectral width of the output beam. The peak transmission wavelength of the multimode interference filter was tuned with the help of a standard in-fiber polarization controller. With this simple mechanism more than 30 nm (1038 nm-1070 nm) tuning range is demonstrated. The spectral width of the output beam from the laser was measured to be 0.05 nm.

Note: Broadly tunable all-fiber ytterbium laser with 0.05 nm spectral width based on multimode interference filter

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Pranab K.; Gupta, Pradeep K.; Singh, Amarjeet; Sharma, Sunil K.; Bindra, Kushvinder S.; Oak, Shrikant M.

2014-05-01

A multimode interference filter with narrow transmission bandwidth and large self-imaging wavelength interval is constructed and implemented in an ytterbium doped fiber laser in all-fiber format for broad wavelength tunability as well as narrow spectral width of the output beam. The peak transmission wavelength of the multimode interference filter was tuned with the help of a standard in-fiber polarization controller. With this simple mechanism more than 30 nm (1038 nm-1070 nm) tuning range is demonstrated. The spectral width of the output beam from the laser was measured to be 0.05 nm.

Note: Broadly tunable all-fiber ytterbium laser with 0.05 nm spectral width based on multimode interference filter

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

Mukhopadhyay, Pranab K., E-mail: pkm@rrcat.gov.in; Gupta, Pradeep K.; Singh, Amarjeet

2014-05-15

A multimode interference filter with narrow transmission bandwidth and large self-imaging wavelength interval is constructed and implemented in an ytterbium doped fiber laser in all-fiber format for broad wavelength tunability as well as narrow spectral width of the output beam. The peak transmission wavelength of the multimode interference filter was tuned with the help of a standard in-fiber polarization controller. With this simple mechanism more than 30 nm (1038 nm–1070 nm) tuning range is demonstrated. The spectral width of the output beam from the laser was measured to be 0.05 nm.

Optimizing human activity patterns using global sensitivity analysis.

PubMed

Fairchild, Geoffrey; Hickmann, Kyle S; Mniszewski, Susan M; Del Valle, Sara Y; Hyman, James M

2014-12-01

Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule's regularity for a population. We show how to tune an activity's regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.

Optimizing human activity patterns using global sensitivity analysis

PubMed Central

Hickmann, Kyle S.; Mniszewski, Susan M.; Del Valle, Sara Y.; Hyman, James M.

2014-01-01

Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations. PMID:25580080

Optimizing human activity patterns using global sensitivity analysis

DOE PAGES

Fairchild, Geoffrey; Hickmann, Kyle S.; Mniszewski, Susan M.; ...

2013-12-10

Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimizationmore » problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. Here we use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Finally, though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.« less

Using game theory for perceptual tuned rate control algorithm in video coding

NASA Astrophysics Data System (ADS)

Luo, Jiancong; Ahmad, Ishfaq

2005-03-01

This paper proposes a game theoretical rate control technique for video compression. Using a cooperative gaming approach, which has been utilized in several branches of natural and social sciences because of its enormous potential for solving constrained optimization problems, we propose a dual-level scheme to optimize the perceptual quality while guaranteeing "fairness" in bit allocation among macroblocks. At the frame level, the algorithm allocates target bits to frames based on their coding complexity. At the macroblock level, the algorithm distributes bits to macroblocks by defining a bargaining game. Macroblocks play cooperatively to compete for shares of resources (bits) to optimize their quantization scales while considering the Human Visual System"s perceptual property. Since the whole frame is an entity perceived by viewers, macroblocks compete cooperatively under a global objective of achieving the best quality with the given bit constraint. The major advantage of the proposed approach is that the cooperative game leads to an optimal and fair bit allocation strategy based on the Nash Bargaining Solution. Another advantage is that it allows multi-objective optimization with multiple decision makers (macroblocks). The simulation results testify the algorithm"s ability to achieve accurate bit rate with good perceptual quality, and to maintain a stable buffer level.

LS-APC v1.0: a tuning-free method for the linear inverse problem and its application to source-term determination

NASA Astrophysics Data System (ADS)

Tichý, Ondřej; Šmídl, Václav; Hofman, Radek; Stohl, Andreas

2016-11-01

Estimation of pollutant releases into the atmosphere is an important problem in the environmental sciences. It is typically formalized as an inverse problem using a linear model that can explain observable quantities (e.g., concentrations or deposition values) as a product of the source-receptor sensitivity (SRS) matrix obtained from an atmospheric transport model multiplied by the unknown source-term vector. Since this problem is typically ill-posed, current state-of-the-art methods are based on regularization of the problem and solution of a formulated optimization problem. This procedure depends on manual settings of uncertainties that are often very poorly quantified, effectively making them tuning parameters. We formulate a probabilistic model, that has the same maximum likelihood solution as the conventional method using pre-specified uncertainties. Replacement of the maximum likelihood solution by full Bayesian estimation also allows estimation of all tuning parameters from the measurements. The estimation procedure is based on the variational Bayes approximation which is evaluated by an iterative algorithm. The resulting method is thus very similar to the conventional approach, but with the possibility to also estimate all tuning parameters from the observations. The proposed algorithm is tested and compared with the standard methods on data from the European Tracer Experiment (ETEX) where advantages of the new method are demonstrated. A MATLAB implementation of the proposed algorithm is available for download.

Pattern-Recognition Algorithm for Locking Laser Frequency

NASA Technical Reports Server (NTRS)

Karayan, Vahag; Klipstein, William; Enzer, Daphna; Yates, Philip; Thompson, Robert; Wells, George

2006-01-01

A computer program serves as part of a feedback control system that locks the frequency of a laser to one of the spectral peaks of cesium atoms in an optical absorption cell. The system analyzes a saturation absorption spectrum to find a target peak and commands a laser-frequency-control circuit to minimize an error signal representing the difference between the laser frequency and the target peak. The program implements an algorithm consisting of the following steps: Acquire a saturation absorption signal while scanning the laser through the frequency range of interest. Condition the signal by use of convolution filtering. Detect peaks. Match the peaks in the signal to a pattern of known spectral peaks by use of a pattern-recognition algorithm. Add missing peaks. Tune the laser to the desired peak and thereafter lock onto this peak. Finding and locking onto the desired peak is a challenging problem, given that the saturation absorption signal includes noise and other spurious signal components; the problem is further complicated by nonlinearity and shifting of the voltage-to-frequency correspondence. The pattern-recognition algorithm, which is based on Hausdorff distance, is what enables the program to meet these challenges.

Optimizing a reconfigurable material via evolutionary computation

NASA Astrophysics Data System (ADS)

Wilken, Sam; Miskin, Marc Z.; Jaeger, Heinrich M.

2015-08-01

Rapid prototyping by combining evolutionary computation with simulations is becoming a powerful tool for solving complex design problems in materials science. This method of optimization operates in a virtual design space that simulates potential material behaviors and after completion needs to be validated by experiment. However, in principle an evolutionary optimizer can also operate on an actual physical structure or laboratory experiment directly, provided the relevant material parameters can be accessed by the optimizer and information about the material's performance can be updated by direct measurements. Here we provide a proof of concept of such direct, physical optimization by showing how a reconfigurable, highly nonlinear material can be tuned to respond to impact. We report on an entirely computer controlled laboratory experiment in which a 6 ×6 grid of electromagnets creates a magnetic field pattern that tunes the local rigidity of a concentrated suspension of ferrofluid and iron filings. A genetic algorithm is implemented and tasked to find field patterns that minimize the force transmitted through the suspension. Searching within a space of roughly 1010 possible configurations, after testing only 1500 independent trials the algorithm identifies an optimized configuration of layered rigid and compliant regions.

Autoresonant control of nonlinear mode in ultrasonic transducer for machining applications.

PubMed

Babitsky, V I; Astashev, V K; Kalashnikov, A N

2004-04-01

Experiments conducted in several countries have shown that the improvement of machining quality can be promoted through conversion of the cutting process into one involving controllable high-frequency vibration at the cutting zone. This is achieved through the generation and maintenance of ultrasonic vibration of the cutting tool to alter the fracture process of work-piece material cutting to one in which loading of the materials at the tool tip is incremental, repetitive and controlled. It was shown that excitation of the high-frequency vibro-impact mode of the tool-workpiece interaction is the most effective way of ultrasonic influence on the dynamic characteristics of machining. The exploitation of this nonlinear mode needs a new method of adaptive control for excitation and stabilisation of ultrasonic vibration known as autoresonance. An approach has been developed to design an autoresonant ultrasonic cutting unit as an oscillating system with an intelligent electronic feedback controlling self-excitation in the entire mechatronic system. The feedback produces the exciting force by means of transformation and amplification of the motion signal. This allows realisation for robust control of fine resonant tuning to bring the nonlinear high Q-factor systems into technological application. The autoresonant control provides the possibility of self-tuning and self-adaptation mechanisms for the system to keep the nonlinear resonant mode of oscillation under unpredictable variation of load, structure and parameters. This allows simple regulation of intensity of the process whilst keeping maximum efficiency at all times. An autoresonant system with supervisory computer control was developed, tested and used for the control of the piezoelectric transducer during ultrasonically assisted cutting. The system has been developed as combined analog-digital, where analog devices process the control signal, and parameters of the devices are controlled digitally by computer. The system was applied for advanced machining of aviation materials.

The signaling symphony: T cell receptor tunes cytokine-mediated T cell differentiation

PubMed Central

Huang, Weishan; August, Avery

2015-01-01

T cell development, differentiation, and maintenance are orchestrated by 2 key signaling axes: the antigen-specific TCR and cytokine-mediated signals. The TCR signals the recognition of self- and foreign antigens to control T cell homeostasis for immune tolerance and immunity, which is regulated by a variety of cytokines to determine T cell subset homeostasis and differentiation. TCR signaling can synergize with or antagonize cytokine-mediated signaling to fine tune T cell fate; however, the latter is less investigated. Murine models with attenuated TCR signaling strength have revealed that TCR signaling can function as regulatory feedback machinery for T cell homeostasis and differentiation in differential cytokine milieus, such as IL-2-mediated Treg development; IL-7-mediated, naïve CD8+ T cell homeostasis; and IL-4-induced innate memory CD8+ T cell development. In this review, we discuss the symphonic cross-talk between TCR and cytokine-mediated responses that differentially control T cell behavior, with a focus on the negative tuning by TCR activation on the cytokine effects. PMID:25525115

Sambot II: A self-assembly modular swarm robot

NASA Astrophysics Data System (ADS)

Zhang, Yuchao; Wei, Hongxing; Yang, Bo; Jiang, Cancan

2018-04-01

The new generation of self-assembly modular swarm robot Sambot II, based on the original generation of self-assembly modular swarm robot Sambot, adopting laser and camera module for information collecting, is introduced in this manuscript. The visual control algorithm of Sambot II is detailed and feasibility of the algorithm is verified by the laser and camera experiments. At the end of this manuscript, autonomous docking experiments of two Sambot II robots are presented. The results of experiments are showed and analyzed to verify the feasibility of whole scheme of Sambot II.

Data Analytics for Smart Parking Applications.

PubMed

Piovesan, Nicola; Turi, Leo; Toigo, Enrico; Martinez, Borja; Rossi, Michele

2016-09-23

We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset.

Data Analytics for Smart Parking Applications

PubMed Central

Piovesan, Nicola; Turi, Leo; Toigo, Enrico; Martinez, Borja; Rossi, Michele

2016-01-01

We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset. PMID:27669259

SPHERES Facility

NASA Technical Reports Server (NTRS)

Martinez, Andres; Benavides, Jose Victor; Ormsby, Steve L.; GuarnerosLuna, Ali

2014-01-01

Synchronized Position Hold, Engage, Reorient, Experimental Satellites (SPHERES) are bowling-ball sized satellites that provide a test bed for development and research into multi-body formation flying, multi-spacecraft control algorithms, and free-flying physical and material science investigations. Up to three self-contained free-flying satellites can fly within the cabin of the International Space Station (ISS), performing flight formations, testing of control algorithms or as a platform for investigations requiring this unique free-flying test environment. Each satellite is a self-contained unit with power, propulsion, computers, navigation equipment, and provides physical and electrical connections (via standardized expansion ports) for Principal Investigator (PI) provided hardware and sensors.

Automated Sensor Tuning for Seismic Event Detection at a Carbon Capture, Utilization, and Storage Site, Farnsworth Unit, Ochiltree County, Texas

NASA Astrophysics Data System (ADS)

Ziegler, A.; Balch, R. S.; Knox, H. A.; Van Wijk, J. W.; Draelos, T.; Peterson, M. G.

2016-12-01

We present results (e.g. seismic detections and STA/LTA detection parameters) from a continuous downhole seismic array in the Farnsworth Field, an oil field in Northern Texas that hosts an ongoing carbon capture, utilization, and storage project. Specifically, we evaluate data from a passive vertical monitoring array consisting of 16 levels of 3-component 15Hz geophones installed in the field and continuously recording since January 2014. This detection database is directly compared to ancillary data (i.e. wellbore pressure) to determine if there is any relationship between seismic observables and CO2 injection and pressure maintenance in the field. Of particular interest is detection of relatively low-amplitude signals constituting long-period long-duration (LPLD) events that may be associated with slow shear-slip analogous to low frequency tectonic tremor. While this category of seismic event provides great insight into dynamic behavior of the pressurized subsurface, it is inherently difficult to detect. To automatically detect seismic events using effective data processing parameters, an automated sensor tuning (AST) algorithm developed by Sandia National Laboratories is being utilized. AST exploits ideas from neuro-dynamic programming (reinforcement learning) to automatically self-tune and determine optimal detection parameter settings. AST adapts in near real-time to changing conditions and automatically self-tune a signal detector to identify (detect) only signals from events of interest, leading to a reduction in the number of missed legitimate event detections and the number of false event detections. Funding for this project is provided by the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (NETL) through the Southwest Regional Partnership on Carbon Sequestration (SWP) under Award No. DE-FC26-05NT42591. Additional support has been provided by site operator Chaparral Energy, L.L.C. and Schlumberger Carbon Services. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Adaptive Sensor Tuning for Seismic Event Detection in Environment with Electromagnetic Noise

NASA Astrophysics Data System (ADS)

Ziegler, Abra E.

The goal of this research is to detect possible microseismic events at a carbon sequestration site. Data recorded on a continuous downhole microseismic array in the Farnsworth Field, an oil field in Northern Texas that hosts an ongoing carbon capture, utilization, and storage project, were evaluated using machine learning and reinforcement learning techniques to determine their effectiveness at seismic event detection on a dataset with electromagnetic noise. The data were recorded from a passive vertical monitoring array consisting of 16 levels of 3-component 15 Hz geophones installed in the field and continuously recording since January 2014. Electromagnetic and other noise recorded on the array has significantly impacted the utility of the data and it was necessary to characterize and filter the noise in order to attempt event detection. Traditional detection methods using short-term average/long-term average (STA/LTA) algorithms were evaluated and determined to be ineffective because of changing noise levels. To improve the performance of event detection and automatically and dynamically detect seismic events using effective data processing parameters, an adaptive sensor tuning (AST) algorithm developed by Sandia National Laboratories was utilized. AST exploits neuro-dynamic programming (reinforcement learning) trained with historic event data to automatically self-tune and determine optimal detection parameter settings. The key metric that guides the AST algorithm is consistency of each sensor with its nearest neighbors: parameters are automatically adjusted on a per station basis to be more or less sensitive to produce consistent agreement of detections in its neighborhood. The effects that changes in neighborhood configuration have on signal detection were explored, as it was determined that neighborhood-based detections significantly reduce the number of both missed and false detections in ground-truthed data. The performance of the AST algorithm was quantitatively evaluated during a variety of noise conditions and seismic detections were identified using AST and compared to ancillary injection data. During a period of CO2 injection in a nearby well to the monitoring array, 82% of seismic events were accurately detected, 13% of events were missed, and 5% of detections were determined to be false. Additionally, seismic risk was evaluated from the stress field and faulting regime at FWU to determine the likelihood of pressure perturbations to trigger slip on previously mapped faults. Faults oriented NW-SE were identified as requiring the smallest pore pressure changes to trigger slip and faults oriented N-S will also potentially be reactivated although this is less likely.

Self-Tuning Methods for Multiple-Controller Systems.

DTIC Science & Technology

1981-08-01

the model. The plant is governed by y(t) + Aly (t-l) - B 0u(t-l) + e(t) - where -0.99101 8.80512 x 103 A1 i.. "+-0.80610 -0.77089 -0.89889 -4.59328 x 10...AC-19, No. 5, Oct. 1974, pp. 518-524. [8 Bar- Shalom , Y. and Tse, E., "Dual Effect, Certainty Equivalence and Separation in Stochastic Control," IEEE...Trans. on Automatic Control, Vol. AC-19, No. 5, Oct. 1974, pp. 494-500. [9) Bar- Shalom , Y. and Tse, E., "Concepts and Methods in Stochastic Control

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches

PubMed Central

Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils’ carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms—including the model tuning and predictor selection—were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models’ predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction. PMID:27128736

The new and computationally efficient MIL-SOM algorithm: potential benefits for visualization and analysis of a large-scale high-dimensional clinically acquired geographic data.

PubMed

Oyana, Tonny J; Achenie, Luke E K; Heo, Joon

2012-01-01

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM.

The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Data

PubMed Central

Oyana, Tonny J.; Achenie, Luke E. K.; Heo, Joon

2012-01-01

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM. PMID:22481977

Demonstration of Bias-Controlled Algorithmic Tuning of Quantum Dots in a Well (DWELL) MidIR Detectors

DTIC Science & Technology

2009-06-01

additionally be utilized to cover a wider spectral range. In recent years, the long-wave IR ( LWIR : 8–12 m) region of the electromagnetic spectrum has been... LWIR region, and they can be sensed by their apparent temper- atures and spectral signatures in the LWIR . Currently, there are three main material...technologies for photonic IR photodetectors in the LWIR region. The HgCdTe (MCT) detector is the current state of the art due to its high responsivity

A novel cost-effective parallel narrowband ANC system with local secondary-path estimation

NASA Astrophysics Data System (ADS)

Delegà, Riccardo; Bernasconi, Giancarlo; Piroddi, Luigi

2017-08-01

Many noise reduction applications are targeted at multi-tonal disturbances. Active noise control (ANC) solutions for such problems are generally based on the combination of multiple adaptive notch filters. Both the performance and the computational cost are negatively affected by an increase in the number of controlled frequencies. In this work we study a different modeling approach for the secondary path, based on the estimation of various small local models in adjacent frequency subbands, that greatly reduces the impact of reference-filtering operations in the ANC algorithm. Furthermore, in combination with a frequency-specific step size tuning method it provides a balanced attenuation performance over the whole controlled frequency range (and particularly in the high end of the range). Finally, the use of small local models is greatly beneficial for the reactivity of the online secondary path modeling algorithm when the characteristics of the acoustic channels are time-varying. Several simulations are provided to illustrate the positive features of the proposed method compared to other well-known techniques.

The Integrated Safety-Critical Advanced Avionics Communication and Control (ISAACC) System Concept: Infrastructure for ISHM

NASA Technical Reports Server (NTRS)

Gwaltney, David A.; Briscoe, Jeri M.

2005-01-01

Integrated System Health Management (ISHM) architectures for spacecraft will include hard real-time, critical subsystems and soft real-time monitoring subsystems. Interaction between these subsystems will be necessary and an architecture supporting multiple criticality levels will be required. Demonstration hardware for the Integrated Safety-Critical Advanced Avionics Communication & Control (ISAACC) system has been developed at NASA Marshall Space Flight Center. It is a modular system using a commercially available time-triggered protocol, ?Tp/C, that supports hard real-time distributed control systems independent of the data transmission medium. The protocol is implemented in hardware and provides guaranteed low-latency messaging with inherent fault-tolerance and fault-containment. Interoperability between modules and systems of modules using the TTP/C is guaranteed through definition of messages and the precise message schedule implemented by the master-less Time Division Multiple Access (TDMA) communications protocol. "Plug-and-play" capability for sensors and actuators provides automatically configurable modules supporting sensor recalibration and control algorithm re-tuning without software modification. Modular components of controlled physical system(s) critical to control algorithm tuning, such as pumps or valve components in an engine, can be replaced or upgraded as "plug and play" components without modification to the ISAACC module hardware or software. ISAACC modules can communicate with other vehicle subsystems through time-triggered protocols or other communications protocols implemented over Ethernet, MIL-STD- 1553 and RS-485/422. Other communication bus physical layers and protocols can be included as required. In this way, the ISAACC modules can be part of a system-of-systems in a vehicle with multi-tier subsystems of varying criticality. The goal of the ISAACC architecture development is control and monitoring of safety critical systems of a manned spacecraft. These systems include spacecraft navigation and attitude control, propulsion, automated docking, vehicle health management and life support. ISAACC can integrate local critical subsystem health management with subsystems performing long term health monitoring. The ISAACC system and its relationship to ISHM will be presented.

Control algorithm implementation for a redundant degree of freedom manipulator

NASA Technical Reports Server (NTRS)

Cohan, Steve

1991-01-01

This project's purpose is to develop and implement control algorithms for a kinematically redundant robotic manipulator. The manipulator is being developed concurrently by Odetics Inc., under internal research and development funding. This SBIR contract supports algorithm conception, development, and simulation, as well as software implementation and integration with the manipulator hardware. The Odetics Dexterous Manipulator is a lightweight, high strength, modular manipulator being developed for space and commercial applications. It has seven fully active degrees of freedom, is electrically powered, and is fully operational in 1 G. The manipulator consists of five self-contained modules. These modules join via simple quick-disconnect couplings and self-mating connectors which allow rapid assembly/disassembly for reconfiguration, transport, or servicing. Each joint incorporates a unique drive train design which provides zero backlash operation, is insensitive to wear, and is single fault tolerant to motor or servo amplifier failure. The sensing system is also designed to be single fault tolerant. Although the initial prototype is not space qualified, the design is well-suited to meeting space qualification requirements. The control algorithm design approach is to develop a hierarchical system with well defined access and interfaces at each level. The high level endpoint/configuration control algorithm transforms manipulator endpoint position/orientation commands to joint angle commands, providing task space motion. At the same time, the kinematic redundancy is resolved by controlling the configuration (pose) of the manipulator, using several different optimizing criteria. The center level of the hierarchy servos the joints to their commanded trajectories using both linear feedback and model-based nonlinear control techniques. The lowest control level uses sensed joint torque to close torque servo loops, with the goal of improving the manipulator dynamic behavior. The control algorithms are subjected to a dynamic simulation before implementation.

A parametric model and estimation techniques for the inharmonicity and tuning of the piano.

PubMed

Rigaud, François; David, Bertrand; Daudet, Laurent

2013-05-01

Inharmonicity of piano tones is an essential property of their timbre that strongly influences the tuning, leading to the so-called octave stretching. It is proposed in this paper to jointly model the inharmonicity and tuning of pianos on the whole compass. While using a small number of parameters, these models are able to reflect both the specificities of instrument design and tuner's practice. An estimation algorithm is derived that can run either on a set of isolated note recordings, but also on chord recordings, assuming that the played notes are known. It is applied to extract parameters highlighting some tuner's choices on different piano types and to propose tuning curves for out-of-tune pianos or piano synthesizers.

Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID

PubMed Central

Yang, Xiaoping; Chen, Xueying; Xia, Riting; Qian, Zhihong

2018-01-01

Aiming at the problem of network congestion caused by the large number of data transmissions in wireless routing nodes of wireless sensor network (WSN), this paper puts forward an algorithm based on standard particle swarm–neural PID congestion control (PNPID). Firstly, PID control theory was applied to the queue management of wireless sensor nodes. Then, the self-learning and self-organizing ability of neurons was used to achieve online adjustment of weights to adjust the proportion, integral and differential parameters of the PID controller. Finally, the standard particle swarm optimization to neural PID (NPID) algorithm of initial values of proportion, integral and differential parameters and neuron learning rates were used for online optimization. This paper describes experiments and simulations which show that the PNPID algorithm effectively stabilized queue length near the expected value. At the same time, network performance, such as throughput and packet loss rate, was greatly improved, which alleviated network congestion and improved network QoS. PMID:29671822

Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID.

PubMed

Yang, Xiaoping; Chen, Xueying; Xia, Riting; Qian, Zhihong

2018-04-19

Aiming at the problem of network congestion caused by the large number of data transmissions in wireless routing nodes of wireless sensor network (WSN), this paper puts forward an algorithm based on standard particle swarm⁻neural PID congestion control (PNPID). Firstly, PID control theory was applied to the queue management of wireless sensor nodes. Then, the self-learning and self-organizing ability of neurons was used to achieve online adjustment of weights to adjust the proportion, integral and differential parameters of the PID controller. Finally, the standard particle swarm optimization to neural PID (NPID) algorithm of initial values of proportion, integral and differential parameters and neuron learning rates were used for online optimization. This paper describes experiments and simulations which show that the PNPID algorithm effectively stabilized queue length near the expected value. At the same time, network performance, such as throughput and packet loss rate, was greatly improved, which alleviated network congestion and improved network QoS.

Rapid Self-healing Nanocomposite Hydrogel with Tunable Dynamic Mechanics

NASA Astrophysics Data System (ADS)

Li, Qiaochu; Mishra, Sumeet; Chapman, Brian; Chen, Pangkuan; Tracy, Joseph; Holten-Andersen, Niels

The macroscopic healing rate and efficiency in self-repairing hydrogel materials are largely determined by the dissociation dynamics of their polymer network, which is hardly achieved in a controllable manner. Inspired by mussel's adhesion chemistry, we developed a novel approach to assemble inorganic nanoparticles and catechol-decorated PEG polymer into a hydrogel network. When utilized as reversible polymer-particle crosslinks, catechol-metal coordination bonds yield a unique gel network with dynamic mechanics controlled directly by interfacial crosslink structure. Taking advantage of this structure-property relationship at polymer-particle interfaces, we designed a hierarchically structured hybrid gel with two distinct relaxation timescales. By tuning the relative contribution of the two relaxation modes, we are able to finely control the gel's dynamic mechanical behavior from a viscoelastic fluid to a stiff solid, yet preserving its rapid self-healing property without the need for external stimuli.

Directing reaction pathways by catalyst active-site selection using self-assembled monolayers.

PubMed

Pang, Simon H; Schoenbaum, Carolyn A; Schwartz, Daniel K; Medlin, J Will

2013-01-01

One key route for controlling reaction selectivity in heterogeneous catalysis is to prepare catalysts that exhibit only specific types of sites required for desired product formation. Here we show that alkanethiolate self-assembled monolayers with varying surface densities can be used to tune selectivity to desired hydrogenation and hydrodeoxygenation products during the reaction of furfural on supported palladium catalysts. Vibrational spectroscopic studies demonstrate that the selectivity improvement is achieved by controlling the availability of specific sites for the hydrogenation of furfural on supported palladium catalysts through the selection of an appropriate alkanethiolate. Increasing self-assembled monolayer density by controlling the steric bulk of the organic tail ligand restricts adsorption on terrace sites and dramatically increases selectivity to desired products furfuryl alcohol and methylfuran. This technique of active-site selection simultaneously serves both to enhance selectivity and provide insight into the reaction mechanism.

Genetic reinforcement learning through symbiotic evolution for fuzzy controller design.

PubMed

Juang, C F; Lin, J Y; Lin, C T

2000-01-01

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems.

Detection of multiple chemicals based on external cavity quantum cascade laser spectroscopy

NASA Astrophysics Data System (ADS)

Sun, Juan; Ding, Junya; Liu, Ningwu; Yang, Guangxiang; Li, Jingsong

2018-02-01

A laser spectroscopy system based on a broadband tunable external cavity quantum cascade laser (ECQCL) and a mini quartz crystal tuning fork (QCTF) detector was developed for standoff detection of volatile organic compounds (VOCs). The self-established spectral analysis model based on multiple algorithms for quantitative and qualitative analysis of VOC components (i.e. ethanol and acetone) was detailedly investigated in both closed cell and open path configurations. A good agreement was obtained between the experimentally observed spectra and the standard reference spectra. For open path detection of VOCs, the sensor system was demonstrated at a distance of 30 m. The preliminary laboratory results show that standoff detection of VOCs at a distance of over 100 m is very promising.

Self-starting, self-regulating Fourier domain mode locked fiber laser for OCT imaging

PubMed Central

Murari, Kartikeya; Mavadia, Jessica; Xi, Jiefeng; Li, Xingde

2011-01-01

We present a Fourier domain mode locking (FDML) fiber laser with a feedback loop allowing automatic startup without a priori knowledge of the fundamental drive frequency. The feedback can also regulate the drive frequency making the source robust against environmental variations. A control system samples the energy of the light traversing the FDML cavity and uses a voltage controlled oscillator (VCO) to drive the tunable fiber Fabry-Perot filter in order to maximize that energy. We demonstrate a prototype self-starting, self-regulating FDML operating at 40 kHz with a full width tuning range of 140 nm around 1305 nm and a power output of ~40 mW. The laser starts up with no operator intervention in less than 5 seconds and exhibits improved spectral stability over a conventional FDML source. In OCT applications the source achieved over 120 dB detection sensitivity and an ~8.9-µm axial resolution. PMID:21750775

Controlling Photoconductivity in PBI Films by Supramolecular Assembly.

PubMed

Draper, Emily R; Archibald, Lewis J; Nolan, Michael C; Schweins, Ralf; Zwijnenburg, Martijn A; Sproules, Stephen; Adams, Dave J

2018-03-15

Perylene bisimides (PBIs) self-assemble in solution. The solubility of the PBIs is commonly changed through the choice of substituents at the imide positions. It is generally assumed this substitution does not affect the electronic properties of the PBI, and that the properties of the self-assembled aggregate are essentially that of the isolated molecule. However, substituents do affect the self-assembly, resulting in potentially different packing in the formed aggregates. Here, we show that the photoconductivity of films formed from a library of substituted PBIs varies strongly with the substituent and demonstrate that this is due to the different ways in which they pack. Our results open the possibility for tuning the optoelectronic properties of self-assembled PBIs by controlling the aggregate structure through careful choice of substituent, as demonstrated by us here optimising the photoconductivity of PBI films in this way. © 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Functional Based Adaptive and Fuzzy Sliding Controller for Non-Autonomous Active Suspension System

NASA Astrophysics Data System (ADS)

Huang, Shiuh-Jer; Chen, Hung-Yi

In this paper, an adaptive sliding controller is developed for controlling a vehicle active suspension system. The functional approximation technique is employed to substitute the unknown non-autonomous functions of the suspension system and release the model-based requirement of sliding mode control algorithm. In order to improve the control performance and reduce the implementation problem, a fuzzy strategy with online learning ability is added to compensate the functional approximation error. The update laws of the functional approximation coefficients and the fuzzy tuning parameters are derived from the Lyapunov theorem to guarantee the system stability. The proposed controller is implemented on a quarter-car hydraulic actuating active suspension system test-rig. The experimental results show that the proposed controller suppresses the oscillation amplitude of the suspension system effectively.

Model-on-Demand Predictive Control for Nonlinear Hybrid Systems With Application to Adaptive Behavioral Interventions

PubMed Central

Nandola, Naresh N.; Rivera, Daniel E.

2011-01-01

This paper presents a data-centric modeling and predictive control approach for nonlinear hybrid systems. System identification of hybrid systems represents a challenging problem because model parameters depend on the mode or operating point of the system. The proposed algorithm applies Model-on-Demand (MoD) estimation to generate a local linear approximation of the nonlinear hybrid system at each time step, using a small subset of data selected by an adaptive bandwidth selector. The appeal of the MoD approach lies in the fact that model parameters are estimated based on a current operating point; hence estimation of locations or modes governed by autonomous discrete events is achieved automatically. The local MoD model is then converted into a mixed logical dynamical (MLD) system representation which can be used directly in a model predictive control (MPC) law for hybrid systems using multiple-degree-of-freedom tuning. The effectiveness of the proposed MoD predictive control algorithm for nonlinear hybrid systems is demonstrated on a hypothetical adaptive behavioral intervention problem inspired by Fast Track, a real-life preventive intervention for improving parental function and reducing conduct disorder in at-risk children. Simulation results demonstrate that the proposed algorithm can be useful for adaptive intervention problems exhibiting both nonlinear and hybrid character. PMID:21874087

Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors.

PubMed

Wei, Qinglai; Li, Benkai; Song, Ruizhuo

2018-04-01

In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.

Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes

PubMed Central

Daskalaki, Elena; Diem, Peter; Mougiakakou, Stavroula G.

2016-01-01

Although reinforcement learning (RL) is suitable for highly uncertain systems, the applicability of this class of algorithms to medical treatment may be limited by the patient variability which dictates individualised tuning for their usually multiple algorithmic parameters. This study explores the feasibility of RL in the framework of artificial pancreas development for type 1 diabetes (T1D). In this approach, an Actor-Critic (AC) learning algorithm is designed and developed for the optimisation of insulin infusion for personalised glucose regulation. AC optimises the daily basal insulin rate and insulin:carbohydrate ratio for each patient, on the basis of his/her measured glucose profile. Automatic, personalised tuning of AC is based on the estimation of information transfer (IT) from insulin to glucose signals. Insulin-to-glucose IT is linked to patient-specific characteristics related to total daily insulin needs and insulin sensitivity (SI). The AC algorithm is evaluated using an FDA-accepted T1D simulator on a large patient database under a complex meal protocol, meal uncertainty and diurnal SI variation. The results showed that 95.66% of time was spent in normoglycaemia in the presence of meal uncertainty and 93.02% when meal uncertainty and SI variation were simultaneously considered. The time spent in hypoglycaemia was 0.27% in both cases. The novel tuning method reduced the risk of severe hypoglycaemia, especially in patients with low SI. PMID:27441367

Self-tuning wireless power transmission scheme based on on-line scattering parameters measurement and two-side power matching.

PubMed

Luo, Yanting; Yang, Yongmin; Chen, Zhongsheng

2014-04-10

Sub-resonances often happen in wireless power transmission (WPT) systems using coupled magnetic resonances (CMR) due to environmental changes, coil movements or component degradations, which is a serious challenge for high efficiency power transmission. Thus self-tuning is very significant to keep WPT systems following strongly magnetic resonant conditions in practice. Traditional coupled-mode ways is difficult to solve this problem. In this paper a two-port power wave model is presented, where power matching and the overall systemic power transmission efficiency are firstly defined by scattering (S) parameters. Then we propose a novel self-tuning scheme based on on-line S parameters measurements and two-side power matching. Experimental results testify the feasibility of the proposed method. These findings suggest that the proposed method is much potential to develop strongly self-adaptive WPT systems with CMR.

Development of an above-knee prosthesis equipped with a microcomputer-controlled knee joint: first test results.

PubMed

Aeyels, B; Peeraer, L; Vander Sloten, J; Van der Perre, G

1992-05-01

The shortcomings of conventional above-knee prostheses are due to their lack of adaptive control. Implementation of a microcomputer controlling the knee joint in a passive way has been suggested to enhance the patient's gait comfort, safety and cosmesis. This approach was used in the design of a new prosthetic system for the above-knee amputee, and tested on one patient. The knee joint of a conventional, modular prosthesis was replaced by a knee joint mechanism, equipped with a controllable brake on the knee joint axis. Sensors and a microcomputer were added, keeping the system self-contained. The modularity of the design permits the use of an alternative, external, PC-based control unit, emulating the self-contained one, and offering extended data monitoring and storage facilities. For both units an operating environment was written, including sensor/actuator interfacing and the implementation of a real-time interrupt, executing the control algorithm. A double finite state approach was used in the design of the control algorithm. On a higher level, the mode identification algorithm reveals the patient's intent. Within a specific mode (lower level), the relevant mode control algorithm looks for the current phase within the gait cycle. Within a particular phase, a specific simple control action with the brake replaces normal knee muscle activity. Tests were carried out with one prosthetic patient using a basic control algorithm for level walking, allowing controlled knee flexion during stance phase. The technical feasibility of such a concept is illustrated by the test results, even though only flexion during early stance phase was controlled during the trials.(ABSTRACT TRUNCATED AT 250 WORDS)

Automatic Thread-Level Parallelization in the Chombo AMR Library

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

Christen, Matthias; Keen, Noel; Ligocki, Terry

2011-05-26

The increasing on-chip parallelism has some substantial implications for HPC applications. Currently, hybrid programming models (typically MPI+OpenMP) are employed for mapping software to the hardware in order to leverage the hardware?s architectural features. In this paper, we present an approach that automatically introduces thread level parallelism into Chombo, a parallel adaptive mesh refinement framework for finite difference type PDE solvers. In Chombo, core algorithms are specified in the ChomboFortran, a macro language extension to F77 that is part of the Chombo framework. This domain-specific language forms an already used target language for an automatic migration of the large number ofmore » existing algorithms into a hybrid MPI+OpenMP implementation. It also provides access to the auto-tuning methodology that enables tuning certain aspects of an algorithm to hardware characteristics. Performance measurements are presented for a few of the most relevant kernels with respect to a specific application benchmark using this technique as well as benchmark results for the entire application. The kernel benchmarks show that, using auto-tuning, up to a factor of 11 in performance was gained with 4 threads with respect to the serial reference implementation.« less

Development and application of the modal space self-tuning regulator

NASA Astrophysics Data System (ADS)

Schultze, John Francis

The control and reduction of vibration of flexible structures is currently an area of much research and concern in the aerospace and automotive industries. Often these systems are idealized as discrete systems with a finite number of degrees of freedom. Traditional active control approaches have attempted either to identify the complete system and design an appropriate controller or; use an ad-hoc set of single degree of freedom controllers. Both methods have limitations. The former requires great computational and control design effort. This approach also attempts to reduce the vibration across the complete spectrum as opposed to applying control effort only to the problematic mode(s). The latter method is often limited by its inability to address the structural coupling inherent in these systems. The Modal Space Self Tuning Regulator (MSSTR) method proposed in this research addresses both of these problems as well as changes in the structural properties of a system. The control problem is approached in a two stage effort, decoupling and adaptive control. The structure's motion is decoupled through the Modified Reciprocal Modal Vector method. The control is then implemented in modal space as a new acceleration feedback based, single degree of freedom, form of the Self Tuning Regulator. The range of application of this controller in terms of maximum additive damping, actuator location sensitivity, and discrete and continuous system mass changes are investigated. Also, the behavior of the internal controller parameters are studied for the extension of this method to system monitoring and damage detection. Proof of the numeric stability of the controller in the ideal case is presented as well as its practical implementation issues. This control approach was shown to be effective for the cases of specified damping increases up to 10 dB, several actuator locations, three discrete mass perturbations and several continuous mass change cases. There appears to be little dependence on the actuator position until the additive damping limit is reached. The discrete mass change tests investigate both increases and reductions in the effective moving mass of the system. The controller performed well in all cases investigated achieving a minimum of 7 dB and up to 15 dB of attenuation. The continuous mass change cases, modeling tool-wear, fuel consumption, or other time varying phenomena, show good convergence behavior of the system model and the accompanying regulator law parameters. This validates the controller for its implementation in a rapidly changing system. The MSSTR performed well in several varied test cases, showing both insensitivity to actuator location and resilience to changing system parameters. Extensions to multi-input, multi-mode control appears within ready grasp.

Nonlinear Performance Seeking Control using Fuzzy Model Reference Learning Control and the Method of Steepest Descent

NASA Technical Reports Server (NTRS)

Kopasakis, George

1997-01-01

Performance Seeking Control (PSC) attempts to find and control the process at the operating condition that will generate maximum performance. In this paper a nonlinear multivariable PSC methodology will be developed, utilizing the Fuzzy Model Reference Learning Control (FMRLC) and the method of Steepest Descent or Gradient (SDG). This PSC control methodology employs the SDG method to find the operating condition that will generate maximum performance. This operating condition is in turn passed to the FMRLC controller as a set point for the control of the process. The conventional SDG algorithm is modified in this paper in order for convergence to occur monotonically. For the FMRLC control, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for effective tuning of the FMRLC controller.

The Shock and Vibration Bulletin. Part 4. Vibration Testing, Instrumentation, Loads and Environments, Tracked Vehicles

DTIC Science & Technology

1978-09-01

self with a nonlinear (thermocouple) function, the limits X os nu 2O being: x Enables Temperature Controller to command heat 0 Code Temnperature ___6...34 Shock and Vi- bration Bulletin 42, 1970. (2) R.T. Fandrich, " Self -Tuning Re- • sonant Fixtures", 47th Shock andVibration Symposium, October 1976. . " (3...is given in Fig. 4. timTit for "ysem From the shape of some of these it is apparent linear respose that in some tests dampino ratios of the order 0 of

Fast parallel MR image reconstruction via B1-based, adaptive restart, iterative soft thresholding algorithms (BARISTA).

PubMed

Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A

2015-02-01

Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms.

Fast Parallel MR Image Reconstruction via B1-based, Adaptive Restart, Iterative Soft Thresholding Algorithms (BARISTA)

PubMed Central

Noll, Douglas C.; Fessler, Jeffrey A.

2014-01-01

Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms. PMID:25330484

Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation

DOE PAGES

Huang, Hao; Yoo, Shinjae; Yu, Dantong; ...

2015-06-01

Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\\ models the heat diffusion traces along the entire time scale, somore » it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.« less

Automated red blood cells extraction from holographic images using fully convolutional neural networks.

PubMed

Yi, Faliu; Moon, Inkyu; Javidi, Bahram

2017-10-01

In this paper, we present two models for automatically extracting red blood cells (RBCs) from RBCs holographic images based on a deep learning fully convolutional neural network (FCN) algorithm. The first model, called FCN-1, only uses the FCN algorithm to carry out RBCs prediction, whereas the second model, called FCN-2, combines the FCN approach with the marker-controlled watershed transform segmentation scheme to achieve RBCs extraction. Both models achieve good segmentation accuracy. In addition, the second model has much better performance in terms of cell separation than traditional segmentation methods. In the proposed methods, the RBCs phase images are first numerically reconstructed from RBCs holograms recorded with off-axis digital holographic microscopy. Then, some RBCs phase images are manually segmented and used as training data to fine-tune the FCN. Finally, each pixel in new input RBCs phase images is predicted into either foreground or background using the trained FCN models. The RBCs prediction result from the first model is the final segmentation result, whereas the result from the second model is used as the internal markers of the marker-controlled transform algorithm for further segmentation. Experimental results show that the given schemes can automatically extract RBCs from RBCs phase images and much better RBCs separation results are obtained when the FCN technique is combined with the marker-controlled watershed segmentation algorithm.

Automated red blood cells extraction from holographic images using fully convolutional neural networks

PubMed Central

Yi, Faliu; Moon, Inkyu; Javidi, Bahram

2017-01-01

In this paper, we present two models for automatically extracting red blood cells (RBCs) from RBCs holographic images based on a deep learning fully convolutional neural network (FCN) algorithm. The first model, called FCN-1, only uses the FCN algorithm to carry out RBCs prediction, whereas the second model, called FCN-2, combines the FCN approach with the marker-controlled watershed transform segmentation scheme to achieve RBCs extraction. Both models achieve good segmentation accuracy. In addition, the second model has much better performance in terms of cell separation than traditional segmentation methods. In the proposed methods, the RBCs phase images are first numerically reconstructed from RBCs holograms recorded with off-axis digital holographic microscopy. Then, some RBCs phase images are manually segmented and used as training data to fine-tune the FCN. Finally, each pixel in new input RBCs phase images is predicted into either foreground or background using the trained FCN models. The RBCs prediction result from the first model is the final segmentation result, whereas the result from the second model is used as the internal markers of the marker-controlled transform algorithm for further segmentation. Experimental results show that the given schemes can automatically extract RBCs from RBCs phase images and much better RBCs separation results are obtained when the FCN technique is combined with the marker-controlled watershed segmentation algorithm. PMID:29082078

A Self-Tuning Kalman Filter for Autonomous Navigation Using the Global Positioning System (GPS)

NASA Technical Reports Server (NTRS)

Truong, Son H.

1999-01-01

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS (Global Positioning Systems) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.

A Self-Tuning Kalman Filter for Autonomous Navigation using the Global Positioning System (GPS)

NASA Technical Reports Server (NTRS)

Truong, S. H.

1999-01-01

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.

Active control of flexible structures using a fuzzy logic algorithm

NASA Astrophysics Data System (ADS)

Cohen, Kelly; Weller, Tanchum; Ben-Asher, Joseph Z.

2002-08-01

This study deals with the development and application of an active control law for the vibration suppression of beam-like flexible structures experiencing transient disturbances. Collocated pairs of sensors/actuators provide active control of the structure. A design methodology for the closed-loop control algorithm based on fuzzy logic is proposed. First, the behavior of the open-loop system is observed. Then, the number and locations of collocated actuator/sensor pairs are selected. The proposed control law, which is based on the principles of passivity, commands the actuator to emulate the behavior of a dynamic vibration absorber. The absorber is tuned to a targeted frequency, whereas the damping coefficient of the dashpot is varied in a closed loop using a fuzzy logic based algorithm. This approach not only ensures inherent stability associated with passive absorbers, but also circumvents the phenomenon of modal spillover. The developed controller is applied to the AFWAL/FIB 10 bar truss. Simulated results using MATLAB© show that the closed-loop system exhibits fairly quick settling times and desirable performance, as well as robustness characteristics. To demonstrate the robustness of the control system to changes in the temporal dynamics of the flexible structure, the transient response to a considerably perturbed plant is simulated. The modal frequencies of the 10 bar truss were raised as well as lowered substantially, thereby significantly perturbing the natural frequencies of vibration. For these cases, too, the developed control law provides adequate settling times and rates of vibrational energy dissipation.

Intelligent Systems Approach for Automated Identification of Individual Control Behavior of a Human Operator

NASA Technical Reports Server (NTRS)

Zaychik, Kirill B.; Cardullo, Frank M.

2012-01-01

Results have been obtained using conventional techniques to model the generic human operator?s control behavior, however little research has been done to identify an individual based on control behavior. The hypothesis investigated is that different operators exhibit different control behavior when performing a given control task. Two enhancements to existing human operator models, which allow personalization of the modeled control behavior, are presented. One enhancement accounts for the testing control signals, which are introduced by an operator for more accurate control of the system and/or to adjust the control strategy. This uses the Artificial Neural Network which can be fine-tuned to model the testing control. Another enhancement takes the form of an equiripple filter which conditions the control system power spectrum. A novel automated parameter identification technique was developed to facilitate the identification process of the parameters of the selected models. This utilizes a Genetic Algorithm based optimization engine called the Bit-Climbing Algorithm. Enhancements were validated using experimental data obtained from three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. This manuscript also addresses applying human operator models to evaluate the effectiveness of motion feedback when simulating actual pilot control behavior in a flight simulator.

Hands-on parameter search for neural simulations by a MIDI-controller.

PubMed

Eichner, Hubert; Borst, Alexander

2011-01-01

Computational neuroscientists frequently encounter the challenge of parameter fitting--exploring a usually high dimensional variable space to find a parameter set that reproduces an experimental data set. One common approach is using automated search algorithms such as gradient descent or genetic algorithms. However, these approaches suffer several shortcomings related to their lack of understanding the underlying question, such as defining a suitable error function or getting stuck in local minima. Another widespread approach is manual parameter fitting using a keyboard or a mouse, evaluating different parameter sets following the users intuition. However, this process is often cumbersome and time-intensive. Here, we present a new method for manual parameter fitting. A MIDI controller provides input to the simulation software, where model parameters are then tuned according to the knob and slider positions on the device. The model is immediately updated on every parameter change, continuously plotting the latest results. Given reasonably short simulation times of less than one second, we find this method to be highly efficient in quickly determining good parameter sets. Our approach bears a close resemblance to tuning the sound of an analog synthesizer, giving the user a very good intuition of the problem at hand, such as immediate feedback if and how results are affected by specific parameter changes. In addition to be used in research, our approach should be an ideal teaching tool, allowing students to interactively explore complex models such as Hodgkin-Huxley or dynamical systems.

Hands-On Parameter Search for Neural Simulations by a MIDI-Controller

PubMed Central

Eichner, Hubert; Borst, Alexander

2011-01-01

Computational neuroscientists frequently encounter the challenge of parameter fitting – exploring a usually high dimensional variable space to find a parameter set that reproduces an experimental data set. One common approach is using automated search algorithms such as gradient descent or genetic algorithms. However, these approaches suffer several shortcomings related to their lack of understanding the underlying question, such as defining a suitable error function or getting stuck in local minima. Another widespread approach is manual parameter fitting using a keyboard or a mouse, evaluating different parameter sets following the users intuition. However, this process is often cumbersome and time-intensive. Here, we present a new method for manual parameter fitting. A MIDI controller provides input to the simulation software, where model parameters are then tuned according to the knob and slider positions on the device. The model is immediately updated on every parameter change, continuously plotting the latest results. Given reasonably short simulation times of less than one second, we find this method to be highly efficient in quickly determining good parameter sets. Our approach bears a close resemblance to tuning the sound of an analog synthesizer, giving the user a very good intuition of the problem at hand, such as immediate feedback if and how results are affected by specific parameter changes. In addition to be used in research, our approach should be an ideal teaching tool, allowing students to interactively explore complex models such as Hodgkin-Huxley or dynamical systems. PMID:22066027

Novel aspects of plasma control in ITER

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

Humphreys, D.; Jackson, G.; Walker, M.

2015-02-15

ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily formore » ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g., current profile regulation, tearing mode (TM) suppression), control mathematics (e.g., algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g., methods for management of highly subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.« less

Novel aspects of plasma control in ITER

DOE PAGES

Humphreys, David; Ambrosino, G.; de Vries, Peter; ...

2015-02-12

ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily formore » ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g. current profile regulation, tearing mode suppression (TM)), control mathematics (e.g. algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g. methods for management of highly-subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Finally, issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.« less

Recent Developments on Microencapsulation for Autonomous Corrosion Protection

NASA Technical Reports Server (NTRS)

Calle, Luz M.; Li, Wenyan; Buhrow, Jerry W.; Fitzpatrick, Lilliana; Jolley, Scott T.; Surma, Jan M.; Pearman, Benjamin P.; Zhang, Xuejun

2014-01-01

This work concerns recent progress in the development of a multifunctional smart coating based on microencapsulation for the autonomous control of corrosion. Microencapsulation allows the incorporation of desired corrosion control functionalities, such as early corrosion detection and inhibition through corrosion controlled release of corrosion indicators and inhibitors, as well as self-healing agent release when mechanical damage occurs.While proof-of-concept results have been reported previously, more recent efforts have been concentrated in technical developments to improve coating compatibility, synthesis procedure scalability, as well as fine tuning the release property of encapsulated active agents.

Development of Hardware-in-the-Loop Simulation Based on Gazebo and Pixhawk for Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Nguyen, Khoa Dang; Ha, Cheolkeun

2018-04-01

Hardware-in-the-loop simulation (HILS) is well known as an effective approach in the design of unmanned aerial vehicles (UAV) systems, enabling engineers to test the control algorithm on a hardware board with a UAV model on the software. Performance of HILS is determined by performances of the control algorithm, the developed model, and the signal transfer between the hardware and software. The result of HILS is degraded if any signal could not be transferred to the correct destination. Therefore, this paper aims to develop a middleware software to secure communications in HILS system for testing the operation of a quad-rotor UAV. In our HILS, the Gazebo software is used to generate a nonlinear six-degrees-of-freedom (6DOF) model, sensor model, and 3D visualization for the quad-rotor UAV. Meanwhile, the flight control algorithm is designed and implemented on the Pixhawk hardware. New middleware software, referred to as the control application software (CAS), is proposed to ensure the connection and data transfer between Gazebo and Pixhawk using the multithread structure in Qt Creator. The CAS provides a graphical user interface (GUI), allowing the user to monitor the status of packet transfer, and perform the flight control commands and the real-time tuning parameters for the quad-rotor UAV. Numerical implementations have been performed to prove the effectiveness of the middleware software CAS suggested in this paper.

JPRS Report, Science & Technology, USSR: Electronics & Electrical Engineering.

DTIC Science & Technology

1988-02-05

Sirena -1 Self-propelled Flaw Detector [PRIBORYI SISTEMY UPRAVLENIYA, Jan 87] 14 Crane Strain-measurement Scales With Data Processing by a Microprocessor...was 3-5 m. 06415/06662 UDC 620.179.1:620.165.29 Algorithimization of Control of Electric Motor Drive of Sirena -1 Self-propelled Flaw Detector...The article describes one of the most optimum algorithms of control of the electric motor drive of the Sirena -1 self-propelled flaw detector

The PI3K Pathway Balances Self-Renewal and Differentiation of Nephron Progenitor Cells through β-Catenin Signaling

PubMed Central

Lindström, Nils Olof; Carragher, Neil Oliver; Hohenstein, Peter

2015-01-01

Summary Nephron progenitor cells differentiate to form nephrons during embryonic kidney development. In contrast, self-renewal maintains progenitor numbers and premature depletion leads to impaired kidney function. Here we analyze the PI3K pathway as a point of convergence for the multiple pathways that are known to control self-renewal in the kidney. We demonstrate that a reduction in PI3K signaling triggers premature differentiation of the progenitors and activates a differentiation program that precedes the mesenchymal-to-epithelial transition through ectopic activation of the β-catenin pathway. Therefore, the combined output of PI3K and other pathways fine-tunes the balance between self-renewal and differentiation in nephron progenitors. PMID:25754203

3D Printing Polymers with Supramolecular Functionality for Biological Applications.

PubMed

Pekkanen, Allison M; Mondschein, Ryan J; Williams, Christopher B; Long, Timothy E

2017-09-11

Supramolecular chemistry continues to experience widespread growth, as fine-tuned chemical structures lead to well-defined bulk materials. Previous literature described the roles of hydrogen bonding, ionic aggregation, guest/host interactions, and π-π stacking to tune mechanical, viscoelastic, and processing performance. The versatility of reversible interactions enables the more facile manufacturing of molded parts with tailored hierarchical structures such as tissue engineered scaffolds for biological applications. Recently, supramolecular polymers and additive manufacturing processes merged to provide parts with control of the molecular, macromolecular, and feature length scales. Additive manufacturing, or 3D printing, generates customizable constructs desirable for many applications, and the introduction of supramolecular interactions will potentially increase production speed, offer a tunable surface structure for controlling cell/scaffold interactions, and impart desired mechanical properties through reinforcing interlayer adhesion and introducing gradients or self-assembled structures. This review details the synthesis and characterization of supramolecular polymers suitable for additive manufacture and biomedical applications as well as the use of supramolecular polymers in additive manufacturing for drug delivery and complex tissue scaffold formation. The effect of supramolecular assembly and its dynamic behavior offers potential for controlling the anisotropy of the printed objects with exquisite geometrical control. The potential for supramolecular polymers to generate well-defined parts, hierarchical structures, and scaffolds with gradient properties/tuned surfaces provides an avenue for developing next-generation biomedical devices and tissue scaffolds.

An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks.

PubMed

Lei, Chunyang; Bie, Hongxia; Fang, Gengfa; Zhang, Xuekun

2015-12-03

Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT), Machine-to-Machine (M2M) communications, Vehicular-to-Vehicular (V2V) communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95 % of the theoretical optimal throughput and 0 . 97 of fairness index especially in dynamic and dense networks.

Model identification and vision-based H∞ position control of 6-DoF cable-driven parallel robots

NASA Astrophysics Data System (ADS)

Chellal, R.; Cuvillon, L.; Laroche, E.

2017-04-01

This paper presents methodologies for the identification and control of 6-degrees of freedom (6-DoF) cable-driven parallel robots (CDPRs). First a two-step identification methodology is proposed to accurately estimate the kinematic parameters independently and prior to the dynamic parameters of a physics-based model of CDPRs. Second, an original control scheme is developed, including a vision-based position controller tuned with the H∞ methodology and a cable tension distribution algorithm. The position is controlled in the operational space, making use of the end-effector pose measured by a motion-tracking system. A four-block H∞ design scheme with adjusted weighting filters ensures good trajectory tracking and disturbance rejection properties for the CDPR system, which is a nonlinear-coupled MIMO system with constrained states. The tension management algorithm generates control signals that maintain the cables under feasible tensions. The paper makes an extensive review of the available methods and presents an extension of one of them. The presented methodologies are evaluated by simulations and experimentally on a redundant 6-DoF INCA 6D CDPR with eight cables, equipped with a motion-tracking system.

Photo-control of nanointeractions.

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

Thomes, William Joseph, Jr.; Potter, Barrett George, Jr.; Jiang, Liu

2005-02-01

The manipulation of physical interactions between structural moieties on the molecular scale is a fundamental hurdle in the realization and operation of nanostructured materials and high surface area microsystem architectures. These include such nano-interaction-based phenomena as self-assembly, fluid flow, and interfacial tribology. The proposed research utilizes photosensitive molecular structures to tune such interactions reversibly. This new material strategy provides optical actuation of nano-interactions impacting behavior on both the nano- and macroscales and with potential to impact directed nanostructure formation, microfluidic rheology, and tribological control.

Adaptive Gas Turbine Engine Control for Deterioration Compensation Due to Aging

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.; Parker, Khary I.; Chatterjee, Santanu

2003-01-01

This paper presents an ad hoc adaptive, multivariable controller tuning rule that compensates for a thrust response variation in an engine whose performance has been degraded though use and wear. The upset appears when a large throttle transient is performed such that the engine controller switches from low-speed to high-speed mode. A relationship was observed between the level of engine degradation and the overshoot in engine temperature ratio, which was determined to cause the thrust response variation. This relationship was used to adapt the controller. The method is shown to work very well up to the operability limits of the engine. Additionally, since the level of degradation can be estimated from sensor data, it would be feasible to implement the adaptive control algorithm on-line.

Hysteresis compensation of the Prandtl-Ishlinskii model for piezoelectric actuators using modified particle swarm optimization with chaotic map.

PubMed

Long, Zhili; Wang, Rui; Fang, Jiwen; Dai, Xufei; Li, Zuohua

2017-07-01

Piezoelectric actuators invariably exhibit hysteresis nonlinearities that tend to become significant under the open-loop condition and could cause oscillations and errors in nanometer-positioning tasks. Chaotic map modified particle swarm optimization (MPSO) is proposed and implemented to identify the Prandtl-Ishlinskii model for piezoelectric actuators. Hysteresis compensation is attained through application of an inverse Prandtl-Ishlinskii model, in which the parameters are formulated based on the original model with chaotic map MPSO. To strengthen the diversity and improve the searching ergodicity of the swarm, an initial method of adaptive inertia weight based on a chaotic map is proposed. To compare and prove that the swarm's convergence occurs before stochastic initialization and to attain an optimal particle swarm optimization algorithm, the parameters of a proportional-integral-derivative controller are searched using self-tuning, and the simulated results are used to verify the search effectiveness of chaotic map MPSO. The results show that chaotic map MPSO is superior to its competitors for identifying the Prandtl-Ishlinskii model and that the inverse Prandtl-Ishlinskii model can provide hysteresis compensation under different conditions in a simple and effective manner.

Tailoring local density of optical states to control emission intensity and anisotropy of quantum dots in hybrid photonic-plasmonic templates

NASA Astrophysics Data System (ADS)

Indukuri, Chaitanya; Mukherjee, Arnab; Basu, J. K.

2015-03-01

We report results of controlled tuning of the local density of states (LDOS) in versatile, flexible, and hierarchical self assembled plasmonic templates. Using 5 nm diameter gold (Au) spherical nanoantenna within a polymer template randomly dispersed with quantum dots, we show how the photoluminescence intensity and lifetime anisotropy of these dots can be significantly enhanced through LDOS tuning. Finite difference time domain simulations corroborate the experimental observations and extend the regime of enhancement to a wider range of geometric and spectral parameters bringing out the versatility of these functional plasmonic templates. It is also demonstrated how the templates act as plasmonic resonators for effectively engineer giant enhancement of the scattering efficiency of these nano antenna embedded in the templates. Our work provides an alternative method to achieve spontaneous emission intensity and anisotropy enhancement with true nanoscale plasmon resonators.

Tunable actuation of dielectric elastomer by electromechanical loading rates

NASA Astrophysics Data System (ADS)

Li, Guorui; Zhang, Mingqi; Chen, Xiangping; Yang, Xuxu; Wong, Tuck-Whye; Li, Tiefeng; Huang, Zhilong

2017-10-01

Dielectric elastomer (DE) membranes are able to self-deform with the application of an electric field through the thickness direction. In comparison to conventional rigid counterparts, soft actuators using DE provide a variety of advantages such as high compliance, low noise, and light weight. As one of the challenges in the development of DE actuating devices, tuning the electromechanical actuating behavior is crucial in order to achieve demanded loading paths and to avoid electromechanical failures. In this paper, our experimental results show that the electromechanical loading conditions affect the actuating behaviors of the DE. The electrical actuating force can be tuned by 29.4% with the control of the electrical charging rate. In addition, controllable actuations have been investigated by the mechanical model in manipulating the electromechanical loading rate. The calculated results agree well with the experimental data. Lastly, it is believed that the mechanisms of controlling the electromechanical loading rate may serve as a guide for the design of DE devices and high performance soft robots in the near future.

Intelligent self-organization methods for wireless ad hoc sensor networks based on limited resources

NASA Astrophysics Data System (ADS)

Hortos, William S.

2006-05-01

A wireless ad hoc sensor network (WSN) is a configuration for area surveillance that affords rapid, flexible deployment in arbitrary threat environments. There is no infrastructure support and sensor nodes communicate with each other only when they are in transmission range. To a greater degree than the terminals found in mobile ad hoc networks (MANETs) for communications, sensor nodes are resource-constrained, with limited computational processing, bandwidth, memory, and power, and are typically unattended once in operation. Consequently, the level of information exchange among nodes, to support any complex adaptive algorithms to establish network connectivity and optimize throughput, not only deplete those limited resources and creates high overhead in narrowband communications, but also increase network vulnerability to eavesdropping by malicious nodes. Cooperation among nodes, critical to the mission of sensor networks, can thus be disrupted by the inappropriate choice of the method for self-organization. Recent published contributions to the self-configuration of ad hoc sensor networks, e.g., self-organizing mapping and swarm intelligence techniques, have been based on the adaptive control of the cross-layer interactions found in MANET protocols to achieve one or more performance objectives: connectivity, intrusion resistance, power control, throughput, and delay. However, few studies have examined the performance of these algorithms when implemented with the limited resources of WSNs. In this paper, self-organization algorithms for the initiation, operation and maintenance of a network topology from a collection of wireless sensor nodes are proposed that improve the performance metrics significant to WSNs. The intelligent algorithm approach emphasizes low computational complexity, energy efficiency and robust adaptation to change, allowing distributed implementation with the actual limited resources of the cooperative nodes of the network. Extensions of the algorithms from flat topologies to two-tier hierarchies of sensor nodes are presented. Results from a few simulations of the proposed algorithms are compared to the published results of other approaches to sensor network self-organization in common scenarios. The estimated network lifetime and extent under static resource allocations are computed.

Advanced Targeting Cost Function Design for Evolutionary Optimization of Control of Logistic Equation

NASA Astrophysics Data System (ADS)

Senkerik, Roman; Zelinka, Ivan; Davendra, Donald; Oplatkova, Zuzana

2010-06-01

This research deals with the optimization of the control of chaos by means of evolutionary algorithms. This work is aimed on an explanation of how to use evolutionary algorithms (EAs) and how to properly define the advanced targeting cost function (CF) securing very fast and precise stabilization of desired state for any initial conditions. As a model of deterministic chaotic system, the one dimensional Logistic equation was used. The evolutionary algorithm Self-Organizing Migrating Algorithm (SOMA) was used in four versions. For each version, repeated simulations were conducted to outline the effectiveness and robustness of used method and targeting CF.

Smith predictor based-sliding mode controller for integrating processes with elevated deadtime.

PubMed

Camacho, Oscar; De la Cruz, Francisco

2004-04-01

An approach to control integrating processes with elevated deadtime using a Smith predictor sliding mode controller is presented. A PID sliding surface and an integrating first-order plus deadtime model have been used to synthesize the controller. Since the performance of existing controllers with a Smith predictor decrease in the presence of modeling errors, this paper presents a simple approach to combining the Smith predictor with the sliding mode concept, which is a proven, simple, and robust procedure. The proposed scheme has a set of tuning equations as a function of the characteristic parameters of the model. For implementation of our proposed approach, computer based industrial controllers that execute PID algorithms can be used. The performance and robustness of the proposed controller are compared with the Matausek-Micić scheme for linear systems using simulations.

Investigation of earthquake factor for optimum tuned mass dampers

NASA Astrophysics Data System (ADS)

Nigdeli, Sinan Melih; Bekdaş, Gebrail

2012-09-01

In this study the optimum parameters of tuned mass dampers (TMD) are investigated under earthquake excitations. An optimization strategy was carried out by using the Harmony Search (HS) algorithm. HS is a metaheuristic method which is inspired from the nature of musical performances. In addition to the HS algorithm, the results of the optimization objective are compared with the results of the other documented method and the corresponding results are eliminated. In that case, the best optimum results are obtained. During the optimization, the optimum TMD parameters were searched for single degree of freedom (SDOF) structure models with different periods. The optimization was done for different earthquakes separately and the results were compared.

Subcellular distribution of Lck during CD4 T-cell maturation in the thymic medulla regulates the T-cell activation threshold.

PubMed

Stephen, Tom Li; Wilson, Bridget S; Laufer, Terri M

2012-05-08

Mature peripheral T cells respond to foreign but not to self-antigens. During development in the thymus, deletion of high-affinity self-reactive immature thymocytes contributes to tolerance of mature T cells. However, double-positive thymocytes are positively selected to survive if they respond to self-peptide-MHC complexes; thus, there must be mechanisms to prevent overt reactivity to those same complexes in the periphery. "Developmental tuning" is the active process through which T-cell receptor (TCR)-associated signaling pathways of single-positive (SP) thymocytes are attenuated to respond appropriately to self-peptide-MHC complexes in the periphery. We previously showed that MHC class II expression in the thymic medulla was necessary to tune CD4(+) SP (CD4 SP) thymocytes. CD4 SP thymocytes from mice lacking medullary MHC class II expression had inappropriately enhanced proximal TCR signaling to low-affinity self-ligands that was associated with altered cellular distribution of the tyrosine kinase Lck. Now, we report that activation of both tuned and untuned CD4 SP thymocytes is Lck-dependent. Untuned CD4 SP cells contain a pool of Lck with increased basal phosphorylation that is not associated with the CD4 coreceptor. Phosphorylation of this pool of Lck decreases with tuning. Immunogold transmission electron microscopy of membrane sheets permitted direct visualization of Lck. In the absence of tuning, a significant proportion of Lck and the TCR subunit CD3ζ are expressed on the same protein island; this close association of Lck and the TCR probably explains the enhanced activation of untuned CD4 SP cells. Thus, changes in membrane topography during thymic maturation determine the set point for TCR responsiveness.

A study on the application of Fourier series in IMRT treatment planning.

PubMed

Almeida-Trinidad, R; Garnica-Garza, H M

2007-12-01

In intensity-modulated radiotherapy, a set of x-ray fluence profiles is iteratively adjusted until a desired absorbed dose distribution is obtained. The purpose of this article is to present a method that allows the optimization of fluence profiles based on the Fourier series decomposition of an initial approximation to the profile. The method has the advantage that a new fluence profile can be obtained in a precise and controlled way with the tuning of only two parameters, namely the phase of the sine and cosine terms of one of the Fourier components, in contrast to the point-by-point tuning of the profile. Also, because the method uses analytical functions, the resultant profiles do not exhibit numerical artifacts. A test case consisting of a mathematical phantom with a target wrapped around a critical structure is discussed to illustrate the algorithm. It is shown that the degree of conformality of the absorbed dose distribution can be tailored by varying the number of Fourier terms made available to the optimization algorithm. For the test case discussed here, it is shown that the number of Fourier terms to be modified depends on the number of radiation beams incident on the target but it is in general in the order of 10 terms.

Surface step terrace tuned microstructures and dielectric properties of highly epitaxial CaCu3Ti4O12 thin films on vicinal LaAlO3 substrates

PubMed Central

Yao, Guang; Gao, Min; Ji, Yanda; Liang, Weizheng; Gao, Lei; Zheng, Shengliang; Wang, You; Pang, Bin; Chen, Y. B.; Zeng, Huizhong; Li, Handong; Wang, Zhiming; Liu, Jingsong; Chen, Chonglin; Lin, Yuan

2016-01-01

Controllable interfacial strain can manipulate the physical properties of epitaxial films and help understand the physical nature of the correlation between the properties and the atomic microstructures. By using a proper design of vicinal single-crystal substrate, the interface strain in epitaxial thin films can be well controlled by adjusting the miscut angle via a surface-step-terrace matching growth mode. Here, we demonstrate that LaAlO3 (LAO) substrates with various miscut angles of 1.0°, 2.75°, and 5.0° were used to tune the dielectric properties of epitaxial CaCu3Ti4O12 (CCTO) thin films. A model of coexistent compressive and tensile strained domains is proposed to understand the epitaxial nature. Our findings on the self-tuning of the compressive and tensile strained domain ratio along the interface depending on the miscut angle and the stress relaxation mechanism under this growth mode will open a new avenue to achieve CCTO films with high dielectric constant and low dielectric loss, which is critical for the design and integration of advanced heterostructures for high performance capacitance device applications. PMID:27703253

Surface step terrace tuned microstructures and dielectric properties of highly epitaxial CaCu3Ti4O12 thin films on vicinal LaAlO3 substrates

NASA Astrophysics Data System (ADS)

Yao, Guang; Gao, Min; Ji, Yanda; Liang, Weizheng; Gao, Lei; Zheng, Shengliang; Wang, You; Pang, Bin; Chen, Y. B.; Zeng, Huizhong; Li, Handong; Wang, Zhiming; Liu, Jingsong; Chen, Chonglin; Lin, Yuan

2016-10-01

Controllable interfacial strain can manipulate the physical properties of epitaxial films and help understand the physical nature of the correlation between the properties and the atomic microstructures. By using a proper design of vicinal single-crystal substrate, the interface strain in epitaxial thin films can be well controlled by adjusting the miscut angle via a surface-step-terrace matching growth mode. Here, we demonstrate that LaAlO3 (LAO) substrates with various miscut angles of 1.0°, 2.75°, and 5.0° were used to tune the dielectric properties of epitaxial CaCu3Ti4O12 (CCTO) thin films. A model of coexistent compressive and tensile strained domains is proposed to understand the epitaxial nature. Our findings on the self-tuning of the compressive and tensile strained domain ratio along the interface depending on the miscut angle and the stress relaxation mechanism under this growth mode will open a new avenue to achieve CCTO films with high dielectric constant and low dielectric loss, which is critical for the design and integration of advanced heterostructures for high performance capacitance device applications.

Surface step terrace tuned microstructures and dielectric properties of highly epitaxial CaCu3Ti4O12 thin films on vicinal LaAlO3 substrates.

PubMed

Yao, Guang; Gao, Min; Ji, Yanda; Liang, Weizheng; Gao, Lei; Zheng, Shengliang; Wang, You; Pang, Bin; Chen, Y B; Zeng, Huizhong; Li, Handong; Wang, Zhiming; Liu, Jingsong; Chen, Chonglin; Lin, Yuan

2016-10-05

Controllable interfacial strain can manipulate the physical properties of epitaxial films and help understand the physical nature of the correlation between the properties and the atomic microstructures. By using a proper design of vicinal single-crystal substrate, the interface strain in epitaxial thin films can be well controlled by adjusting the miscut angle via a surface-step-terrace matching growth mode. Here, we demonstrate that LaAlO 3 (LAO) substrates with various miscut angles of 1.0°, 2.75°, and 5.0° were used to tune the dielectric properties of epitaxial CaCu 3 Ti 4 O 12 (CCTO) thin films. A model of coexistent compressive and tensile strained domains is proposed to understand the epitaxial nature. Our findings on the self-tuning of the compressive and tensile strained domain ratio along the interface depending on the miscut angle and the stress relaxation mechanism under this growth mode will open a new avenue to achieve CCTO films with high dielectric constant and low dielectric loss, which is critical for the design and integration of advanced heterostructures for high performance capacitance device applications.

Self-consistent field theory and numerical scheme for calculating the phase diagram of wormlike diblock copolymers

NASA Astrophysics Data System (ADS)

Jiang, Ying; Chen, Jeff Z. Y.

2013-10-01

This paper concerns establishing a theoretical basis and numerical scheme for studying the phase behavior of AB diblock copolymers made of wormlike chains. The general idea of a self-consistent field theory is the combination of the mean-field approach together with a statistical weight that describes the configurational properties of a polymer chain. In recent years, this approach has been extensively used for structural prediction of block copolymers, based on the Gaussian-model description of a polymer chain. The wormlike-chain model has played an important role in the description of polymer systems, covering the semiflexible-to-rod crossover of the polymer properties and the highly stretching regime, which the Gaussian-chain model has difficulties to describe. Although the idea of developing a self-consistent field theory for wormlike chains could be traced back to early development in polymer physics, the solution of such a theory has been limited due to technical difficulties. In particular, a challenge has been to develop a numerical algorithm enabling the calculation of the phase diagram containing three-dimensional structures for wormlike AB diblock copolymers. This paper describes a computational algorithm that combines a number of numerical tricks, which can be used for such a calculation. A phase diagram covering major parameter areas was constructed for the wormlike-chain system and reported by us, where the ratio between the total length and the persistence length of a constituent polymer is suggested as another tuning parameter for the microphase-separated structures; all detailed technical issues are carefully addressed in the current paper.

Self-assembly of cinnamic acid-capped gold nanoparticles

NASA Astrophysics Data System (ADS)

Wang, Li; Wei, Gang; Sun, Lanlan; Liu, Zhiguo; Song, Yonghai; Yang, Tao; Sun, Yujing; Guo, Cunlan; Li, Zhuang

2006-06-01

In this work, a new capping agent, cinnamic acid (CA) was used to synthesize Au nanoparticles (NPs) under ambient conditions. The size of the NPs can be controlled by adjusting the concentration of reductant (in our experiment sodium borohydride was used) or CA. The CA-stabilized Au NPs can self-assemble into 'nanowire-like' or 'pearl-necklace-like' nanostructures by adjusting the molar ratio of CA to HAuCl4 or by tuning the pH value of the Au colloidal solution. The process of Au NPs self-assembly was investigated by UV-vis spectroscopy and transmission electron microscopy. The results reveal that the induced dipole-dipole interaction is the driving force of Au NP linear assemblies.

A surprising way to control the charge transport in molecular electronics: the subtle impact of the coverage of self-assembled monolayers of floppy molecules adsorbed on metallic electrodes.

PubMed

Bâldea, Ioan

2017-10-26

Inspired by earlier attempts in organic electronics aiming at controlling charge injection from metals into organic materials by manipulating the Schottky energy barrier using self-assembled monolayers (SAMs), recent experimental and theoretical work in molecular electronics showed that metal-organic interfaces can be controlled via changes in the metal work function that are induced by SAMs. In this paper we indicate a different route to achieve interface-driven control over the charge transfer/transport at the molecular scale. It is based on the fact that, in floppy molecule based SAMs, the molecular conformation can be tuned by varying the coverage of the adsorbate. We demonstrate this effect with the aid of benchmark molecules that are often used to fabricate nanojunctions and consist of two rings that can easily rotate relative to each other. We show that, by varying the coverage of the SAM, the twisting angle φ of the considered molecular species can be modified by a factor of two. Given the fact that the low bias conductance G scales as cos 2  φ, this results in a change in G of over one order of magnitude for the considered molecular species. Tuning the twisting angle by controlling the SAM coverage may be significant, e.g., for current efforts to fabricate molecular switches. Conversely, the lack of control over the local SAM coverage may be problematic for the reproducibility and interpretation of the STM (scanning tunneling microscope) measurements on repeatedly forming single molecule break junctions.

Analytical investigation of the dynamics of tethered constellations in Earth orbit, phase 2

NASA Technical Reports Server (NTRS)

Lorenzini, E. C.; Arnold, D. A.; Grossi, M. D.; Gullahorn, G. E.

1986-01-01

The g-tuning maneuvers of a 3-mass, vertical tethered system are considered. In particular, the case of reaching a zero-g acceleration level on board the middle mass from a non-zero initial condition is analyzed. A control law that provides a satisfactory transient response is derived. The constellation dynamics in the case of the middle mass travelling from one tether tip to the other is also investigated. Instabilities that take place at the end of the maneuver are analyzed and accommodated by devising suitable damping algorithms.

Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method

NASA Astrophysics Data System (ADS)

Barducci, Alessandro; Bussi, Giovanni; Parrinello, Michele

2008-01-01

We present a method for determining the free-energy dependence on a selected number of collective variables using an adaptive bias. The formalism provides a unified description which has metadynamics and canonical sampling as limiting cases. Convergence and errors can be rigorously and easily controlled. The parameters of the simulation can be tuned so as to focus the computational effort only on the physically relevant regions of the order parameter space. The algorithm is tested on the reconstruction of an alanine dipeptide free-energy landscape.

Well-tempered metadynamics: a smoothly converging and tunable free-energy method.

PubMed

Barducci, Alessandro; Bussi, Giovanni; Parrinello, Michele

2008-01-18

We present a method for determining the free-energy dependence on a selected number of collective variables using an adaptive bias. The formalism provides a unified description which has metadynamics and canonical sampling as limiting cases. Convergence and errors can be rigorously and easily controlled. The parameters of the simulation can be tuned so as to focus the computational effort only on the physically relevant regions of the order parameter space. The algorithm is tested on the reconstruction of an alanine dipeptide free-energy landscape.

Firefly algorithm with chaos

NASA Astrophysics Data System (ADS)

Gandomi, A. H.; Yang, X.-S.; Talatahari, S.; Alavi, A. H.

2013-01-01

A recently developed metaheuristic optimization algorithm, firefly algorithm (FA), mimics the social behavior of fireflies based on the flashing and attraction characteristics of fireflies. In the present study, we will introduce chaos into FA so as to increase its global search mobility for robust global optimization. Detailed studies are carried out on benchmark problems with different chaotic maps. Here, 12 different chaotic maps are utilized to tune the attractive movement of the fireflies in the algorithm. The results show that some chaotic FAs can clearly outperform the standard FA.

Smart Phase Tuning in Microwave Photonic Integrated Circuits Toward Automated Frequency Multiplication by Design

NASA Astrophysics Data System (ADS)

Nabavi, N.

2018-07-01

The author investigates the monitoring methods for fine adjustment of the previously proposed on-chip architecture for frequency multiplication and translation of harmonics by design. Digital signal processing (DSP) algorithms are utilized to create an optimized microwave photonic integrated circuit functionality toward automated frequency multiplication. The implemented DSP algorithms are formed on discrete Fourier transform and optimization-based algorithms (Greedy and gradient-based algorithms), which are analytically derived and numerically compared based on the accuracy and speed of convergence criteria.

Compact Encoding of Robot-Generated 3D Maps for Efficient Wireless Transmission

DTIC Science & Technology

2003-01-01

Lempel - Ziv -Welch (LZW) and Ziv - Lempel (LZ77) respectively. Image based compression can also be based on dic- tionaries... compression of the data , without actually displaying a 3D model, printing statistical results for comparison of the different algorithms . 1http... compression algorithms , and wavelet algorithms tuned to the specific nature of the raw laser data . For most such applications, the usage of lossless

Optimizing a multi-product closed-loop supply chain using NSGA-II, MOSA, and MOPSO meta-heuristic algorithms

NASA Astrophysics Data System (ADS)

Babaveisi, Vahid; Paydar, Mohammad Mahdi; Safaei, Abdul Sattar

2018-07-01

This study aims to discuss the solution methodology for a closed-loop supply chain (CLSC) network that includes the collection of used products as well as distribution of the new products. This supply chain is presented on behalf of the problems that can be solved by the proposed meta-heuristic algorithms. A mathematical model is designed for a CLSC that involves three objective functions of maximizing the profit, minimizing the total risk and shortages of products. Since three objective functions are considered, a multi-objective solution methodology can be advantageous. Therefore, several approaches have been studied and an NSGA-II algorithm is first utilized, and then the results are validated using an MOSA and MOPSO algorithms. Priority-based encoding, which is used in all the algorithms, is the core of the solution computations. To compare the performance of the meta-heuristics, random numerical instances are evaluated by four criteria involving mean ideal distance, spread of non-dominance solution, the number of Pareto solutions, and CPU time. In order to enhance the performance of the algorithms, Taguchi method is used for parameter tuning. Finally, sensitivity analyses are performed and the computational results are presented based on the sensitivity analyses in parameter tuning.

Optimizing a multi-product closed-loop supply chain using NSGA-II, MOSA, and MOPSO meta-heuristic algorithms

NASA Astrophysics Data System (ADS)

Babaveisi, Vahid; Paydar, Mohammad Mahdi; Safaei, Abdul Sattar

2017-07-01

This study aims to discuss the solution methodology for a closed-loop supply chain (CLSC) network that includes the collection of used products as well as distribution of the new products. This supply chain is presented on behalf of the problems that can be solved by the proposed meta-heuristic algorithms. A mathematical model is designed for a CLSC that involves three objective functions of maximizing the profit, minimizing the total risk and shortages of products. Since three objective functions are considered, a multi-objective solution methodology can be advantageous. Therefore, several approaches have been studied and an NSGA-II algorithm is first utilized, and then the results are validated using an MOSA and MOPSO algorithms. Priority-based encoding, which is used in all the algorithms, is the core of the solution computations. To compare the performance of the meta-heuristics, random numerical instances are evaluated by four criteria involving mean ideal distance, spread of non-dominance solution, the number of Pareto solutions, and CPU time. In order to enhance the performance of the algorithms, Taguchi method is used for parameter tuning. Finally, sensitivity analyses are performed and the computational results are presented based on the sensitivity analyses in parameter tuning.

What a Difference a Parameter Makes: a Psychophysical Comparison of Random Dot Motion Algorithms

PubMed Central

Pilly, Praveen K.; Seitz, Aaron R.

2009-01-01

Random dot motion (RDM) displays have emerged as one of the standard stimulus types employed in psychophysical and physiological studies of motion processing. RDMs are convenient because it is straightforward to manipulate the relative motion energy for a given motion direction in addition to stimulus parameters such as the speed, contrast, duration, density, aperture, etc. However, as widely as RDMs are employed so do they vary in their details of implementation. As a result, it is often difficult to make direct comparisons across studies employing different RDM algorithms and parameters. Here, we systematically measure the ability of human subjects to estimate motion direction for four commonly used RDM algorithms under a range of parameters in order to understand how these different algorithms compare in their perceptibility. We find that parametric and algorithmic differences can produce dramatically different performances. These effects, while surprising, can be understood in relationship to pertinent neurophysiological data regarding spatiotemporal displacement tuning properties of cells in area MT and how the tuning function changes with stimulus contrast and retinal eccentricity. These data help give a baseline by which different RDM algorithms can be compared, demonstrate a need for clearly reporting RDM details in the methods of papers, and also pose new constraints and challenges to models of motion direction processing. PMID:19336240

Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells.

PubMed

Masoli, Stefano; Rizza, Martina F; Sgritta, Martina; Van Geit, Werner; Schürmann, Felix; D'Angelo, Egidio

2017-01-01

In realistic neuronal modeling, once the ionic channel complement has been defined, the maximum ionic conductance (G i-max ) values need to be tuned in order to match the firing pattern revealed by electrophysiological recordings. Recently, selection/mutation genetic algorithms have been proposed to efficiently and automatically tune these parameters. Nonetheless, since similar firing patterns can be achieved through different combinations of G i-max values, it is not clear how well these algorithms approximate the corresponding properties of real cells. Here we have evaluated the issue by exploiting a unique opportunity offered by the cerebellar granule cell (GrC), which is electrotonically compact and has therefore allowed the direct experimental measurement of ionic currents. Previous models were constructed using empirical tuning of G i-max values to match the original data set. Here, by using repetitive discharge patterns as a template, the optimization procedure yielded models that closely approximated the experimental G i-max values. These models, in addition to repetitive firing, captured additional features, including inward rectification, near-threshold oscillations, and resonance, which were not used as features. Thus, parameter optimization using genetic algorithms provided an efficient modeling strategy for reconstructing the biophysical properties of neurons and for the subsequent reconstruction of large-scale neuronal network models.

Dispatch Strategy Development for Grid-tied Household Energy Systems

NASA Astrophysics Data System (ADS)

Cardwell, Joseph

The prevalence of renewable generation will increase in the next several decades and offset conventional generation more and more. Yet this increase is not coming without challenges. Solar, wind, and even some water resources are intermittent and unpredictable, and thereby create scheduling challenges due to their inherent "uncontrolled" nature. To effectively manage these distributed renewable assets, new control algorithms must be developed for applications including energy management, bridge power, and system stability. This can be completed through a centralized control center though efforts are being made to parallel the control architecture with the organization of the renewable assets themselves--namely, distributed controls. Building energy management systems are being employed to control localized energy generation, storage, and use to reduce disruption on the net utility load. One such example is VOLTTRONTM, an agent-based platform for building energy control in real time. In this thesis, algorithms developed in VOLTTRON simulate a home energy management system that consists of a solar PV array, a lithium-ion battery bank, and the grid. Dispatch strategies are implemented to reduce energy charges from overall consumption (/kWh) and demand charges (/kW). Dispatch strategies for implementing storage devices are tuned on a month-to-month basis to provide a meaningful economic advantage under simulated scenarios to explore algorithm sensitivity to changing external factors. VOLTTRON agents provide automated real-time optimization of dispatch strategies to efficiently manage energy supply and demand, lower consumer costs associated with energy usage, and reduce load on the utility grid.

Field-programmable analogue arrays for the sensorless control of DC motors

NASA Astrophysics Data System (ADS)

Rivera, J.; Dueñas, I.; Ortega, S.; Del Valle, J. L.

2018-02-01

This work presents the analogue implementation of a sensorless controller for direct current motors based on the super-twisting (ST) sliding mode technique, by means of field programmable analogue arrays (FPAA). The novelty of this work is twofold, first is the use of the ST algorithm in a sensorless scheme for DC motors, and the implementation method of this type of sliding mode controllers in FPAAs. The ST algorithm reduces the chattering problem produced with the deliberate use of the sign function in classical sliding mode approaches. On the other hand, the advantages of the implementation method over a digital one are that the controller is not digitally approximated, the controller gains are not fine tuned and the implementation does not require the use of analogue-to-digital and digital-to-analogue converter circuits. In addition to this, the FPAA is a reconfigurable, lower cost and power consumption technology. Simulation and experimentation results were registered, where a more accurate transient response and lower power consumption were obtained by the proposed implementation method when compared to a digital implementation. Also, a more accurate performance by the DC motor is obtained with proposed sensorless ST technique when compared with a classical sliding mode approach.

Shape-Controllable Gold Nanoparticle-MoS2 Hybrids Prepared by Tuning Edge-Active Sites and Surface Structures of MoS2 via Temporally Shaped Femtosecond Pulses.

PubMed

Zuo, Pei; Jiang, Lan; Li, Xin; Li, Bo; Xu, Yongda; Shi, Xuesong; Ran, Peng; Ma, Tianbao; Li, Dawei; Qu, Liangti; Lu, Yongfeng; Grigoropoulos, Costas P

2017-03-01

Edge-active site control of MoS 2 is crucial for applications such as chemical catalysis, synthesis of functional composites, and biochemical sensing. This work presents a novel nonthermal method to simultaneously tune surface chemical (edge-active sites) and physical (surface periodic micro/nano structures) properties of MoS 2 using temporally shaped femtosecond pulses, through which shape-controlled gold nanoparticles are in situ and self-assembly grown on MoS 2 surfaces to form Au-MoS 2 hybrids. The edge-active sites with unbound sulfurs of laser-treated MoS 2 drive the reduction of gold nanoparticles, while the surface periodic structures of laser-treated MoS 2 assist the shape-controllable growth of gold nanoparticles. The proposed novel method highlights the broad application potential of MoS 2 ; for example, these Au-MoS 2 hybrids exhibit tunable and highly sensitive SERS activity with an enhancement factor up to 1.2 × 10 7 , indicating the marked potential of MoS 2 in future chemical and biological sensing applications.

Patterned FePt nanostructures using ultrathin self-organized templates

NASA Astrophysics Data System (ADS)

Deng, Chen Hua; Zhang, Min; Wang, Fang; Xu, Xiao Hong

2018-02-01

Patterned magnetic thin films are both scientifically interesting and technologically useful. Ultrathin self-organized anodic aluminum oxide (AAO) template can be used to fabricate large area nanodot and antidot arrays. The magnetic properties of these nanostructures may be tuned by the morphology of the AAO template, which in turn can be controlled by synthetic parameters. In this work, ultrathin AAO templates were used as etching masks for the fabrication of both FePt nanodot and antidot arrays with high areal density. The perpendicular magnetic anisotropy of L10 FePt thin films are preserved in the nanostructures.

An Overview of a Trajectory-Based Solution for En Route and Terminal Area Self-Spacing: Fifth Edition

NASA Technical Reports Server (NTRS)

Abbott, Terence S.

2015-01-01

This paper presents an overview of the fifth revision to an algorithm specifically designed to support NASA's Airborne Precision Spacing concept. This algorithm is referred to as the Airborne Spacing for Terminal Arrival Routes version 12 (ASTAR12). This airborne self-spacing concept is trajectory-based, allowing for spacing operations prior to the aircraft being on a common path. Because this algorithm is trajectory-based, it also has the inherent ability to support required-time-of- arrival (RTA) operations. This algorithm was also designed specifically to support a standalone, non-integrated implementation in the spacing aircraft. This current revision to the algorithm includes a ground speed feedback term to compensate for slower than expected traffic aircraft speeds based on the accepted air traffic control tendency to slow aircraft below the nominal arrival speeds when they are farther from the airport.

A New Powered Lower Limb Prosthesis Control Framework Based on Adaptive Dynamic Programming.

PubMed

Wen, Yue; Si, Jennie; Gao, Xiang; Huang, Stephanie; Huang, He Helen

2017-09-01

This brief presents a novel application of adaptive dynamic programming (ADP) for optimal adaptive control of powered lower limb prostheses, a type of wearable robots to assist the motor function of the limb amputees. Current control of these robotic devices typically relies on finite state impedance control (FS-IC), which lacks adaptability to the user's physical condition. As a result, joint impedance settings are often customized manually and heuristically in clinics, which greatly hinder the wide use of these advanced medical devices. This simulation study aimed at demonstrating the feasibility of ADP for automatic tuning of the twelve knee joint impedance parameters during a complete gait cycle to achieve balanced walking. Given that the accurate models of human walking dynamics are difficult to obtain, the model-free ADP control algorithms were considered. First, direct heuristic dynamic programming (dHDP) was applied to the control problem, and its performance was evaluated on OpenSim, an often-used dynamic walking simulator. For the comparison purposes, we selected another established ADP algorithm, the neural fitted Q with continuous action (NFQCA). In both cases, the ADP controllers learned to control the right knee joint and achieved balanced walking, but dHDP outperformed NFQCA in this application during a 200 gait cycle-based testing.

TALARIS project update: Overview of flight testing and development of a prototype planetary surface exploration hopper

NASA Astrophysics Data System (ADS)

Rossi, Christopher; Cunio, Phillip M.; Alibay, Farah; Morrow, Joe; Nothnagel, Sarah L.; Steiner, Ted; Han, Christopher J.; Lanford, Ephraim; Hoffman, Jeffrey A.

2012-12-01

The TALARIS (Terrestrial Artificial Lunar And Reduced GravIty Simulator) project is intended to test GNC (Guidance, Navigation, and Control) algorithms on a prototype planetary surface exploration hopper in a dynamic environment with simulated reduced gravity. The vehicle is being developed by the Charles Stark Draper Laboratory and Massachusetts Institute of Technology in support of efforts in the Google Lunar X-Prize contest. This paper presents progress achieved since September 2010 in vehicle development and flight testing. Upgrades to the vehicle are described, including a redesign of the power train for the gravity-offset propulsion system and a redesign of key elements of the spacecraft emulator propulsion system. The integration of flight algorithms into modular flight software is also discussed. Results are reported for restricted degree of freedom (DOF) tests used to tune GNC algorithms on the path to a full 6-DOF hover-hop flight profile. These tests include 3-DOF tests on flat surfaces restricted to horizontal motion, and 2-DOF vertical tests restricted to vertical motion and 1-DOF attitude control. The results of tests leading up to full flight operations are described, as are lessons learned and future test plans.

Self-assembled InN quantum dots on side facets of GaN nanowires

NASA Astrophysics Data System (ADS)

Bi, Zhaoxia; Ek, Martin; Stankevic, Tomas; Colvin, Jovana; Hjort, Martin; Lindgren, David; Lenrick, Filip; Johansson, Jonas; Wallenberg, L. Reine; Timm, Rainer; Feidenhans'l, Robert; Mikkelsen, Anders; Borgström, Magnus T.; Gustafsson, Anders; Ohlsson, B. Jonas; Monemar, Bo; Samuelson, Lars

2018-04-01

Self-assembled, atomic diffusion controlled growth of InN quantum dots was realized on the side facets of dislocation-free and c-oriented GaN nanowires having a hexagonal cross-section. The nanowires were synthesized by selective area metal organic vapor phase epitaxy. A 3 Å thick InN wetting layer was observed after growth, on top of which the InN quantum dots formed, indicating self-assembly in the Stranski-Krastanow growth mode. We found that the InN quantum dots can be tuned to nucleate either preferentially at the edges between GaN nanowire side facets, or directly on the side facets by tuning the adatom migration by controlling the precursor supersaturation and growth temperature. Structural characterization by transmission electron microscopy and reciprocal space mapping show that the InN quantum dots are close to be fully relaxed (residual strain below 1%) and that the c-planes of the InN quantum dots are tilted with respect to the GaN core. The strain relaxes mainly by the formation of misfit dislocations, observed with a periodicity of 3.2 nm at the InN and GaN hetero-interface. The misfit dislocations introduce I1 type stacking faults (…ABABCBC…) in the InN quantum dots. Photoluminescence investigations of the InN quantum dots show that the emissions shift to higher energy with reduced quantum dot size, which we attribute to increased quantum confinement.

Reducing the Volume of NASA Earth-Science Data

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Braverman, Amy J.; Guillaume, Alexandre

2010-01-01

A computer program reduces data generated by NASA Earth-science missions into representative clusters characterized by centroids and membership information, thereby reducing the large volume of data to a level more amenable to analysis. The program effects an autonomous data-reduction/clustering process to produce a representative distribution and joint relationships of the data, without assuming a specific type of distribution and relationship and without resorting to domain-specific knowledge about the data. The program implements a combination of a data-reduction algorithm known as the entropy-constrained vector quantization (ECVQ) and an optimization algorithm known as the differential evolution (DE). The combination of algorithms generates the Pareto front of clustering solutions that presents the compromise between the quality of the reduced data and the degree of reduction. Similar prior data-reduction computer programs utilize only a clustering algorithm, the parameters of which are tuned manually by users. In the present program, autonomous optimization of the parameters by means of the DE supplants the manual tuning of the parameters. Thus, the program determines the best set of clustering solutions without human intervention.

Automatic Blocking Of QR and LU Factorizations for Locality

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

Yi, Q; Kennedy, K; You, H

2004-03-26

QR and LU factorizations for dense matrices are important linear algebra computations that are widely used in scientific applications. To efficiently perform these computations on modern computers, the factorization algorithms need to be blocked when operating on large matrices to effectively exploit the deep cache hierarchy prevalent in today's computer memory systems. Because both QR (based on Householder transformations) and LU factorization algorithms contain complex loop structures, few compilers can fully automate the blocking of these algorithms. Though linear algebra libraries such as LAPACK provides manually blocked implementations of these algorithms, by automatically generating blocked versions of the computations, moremore » benefit can be gained such as automatic adaptation of different blocking strategies. This paper demonstrates how to apply an aggressive loop transformation technique, dependence hoisting, to produce efficient blockings for both QR and LU with partial pivoting. We present different blocking strategies that can be generated by our optimizer and compare the performance of auto-blocked versions with manually tuned versions in LAPACK, both using reference BLAS, ATLAS BLAS and native BLAS specially tuned for the underlying machine architectures.« less

Fuzzy Control of Robotic Arm

NASA Astrophysics Data System (ADS)

Lin, Kyaw Kyaw; Soe, Aung Kyaw; Thu, Theint Theint

2008-10-01

This research work investigates a Self-Tuning Proportional Derivative (PD) type Fuzzy Logic Controller (STPDFLC) for a two link robot system. The proposed scheme adjusts on-line the output Scaling Factor (SF) by fuzzy rules according to the current trend of the robot. The rule base for tuning the output scaling factor is defined on the error (e) and change in error (de). The scheme is also based on the fact that the controller always tries to manipulate the process input. The rules are in the familiar if-then format. All membership functions for controller inputs (e and de) and controller output (UN) are defined on the common interval [-1,1]; whereas the membership functions for the gain updating factor (α) is defined on [0,1]. There are various methods to calculate the crisp output of the system. Center of Gravity (COG) method is used in this application due to better results it gives. Performances of the proposed STPDFLC are compared with those of their corresponding PD-type conventional Fuzzy Logic Controller (PDFLC). The proposed scheme shows a remarkably improved performance over its conventional counterpart especially under parameters variation (payload). The two-link results of analysis are simulated. These simulation results are illustrated by using MATLAB® programming.

Nonlinear rescaling of control values simplifies fuzzy control

NASA Technical Reports Server (NTRS)

Vanlangingham, H.; Tsoukkas, A.; Kreinovich, V.; Quintana, C.

1993-01-01

Traditional control theory is well-developed mainly for linear control situations. In non-linear cases there is no general method of generating a good control, so we have to rely on the ability of the experts (operators) to control them. If we want to automate their control, we must acquire their knowledge and translate it into a precise control strategy. The experts' knowledge is usually represented in non-numeric terms, namely, in terms of uncertain statements of the type 'if the obstacle is straight ahead, the distance to it is small, and the velocity of the car is medium, press the brakes hard'. Fuzzy control is a methodology that translates such statements into precise formulas for control. The necessary first step of this strategy consists of assigning membership functions to all the terms that the expert uses in his rules (in our sample phrase these words are 'small', 'medium', and 'hard'). The appropriate choice of a membership function can drastically improve the quality of a fuzzy control. In the simplest cases, we can take the functions whose domains have equally spaced endpoints. Because of that, many software packages for fuzzy control are based on this choice of membership functions. This choice is not very efficient in more complicated cases. Therefore, methods have been developed that use neural networks or generic algorithms to 'tune' membership functions. But this tuning takes lots of time (for example, several thousands iterations are typical for neural networks). In some cases there are evident physical reasons why equally space domains do not work: e.g., if the control variable u is always positive (i.e., if we control temperature in a reactor), then negative values (that are generated by equal spacing) simply make no sense. In this case it sounds reasonable to choose another scale u' = f(u) to represent u, so that equal spacing will work fine for u'. In the present paper we formulate the problem of finding the best rescaling function, solve this problem, and show (on a real-life example) that after an optimal rescaling, the un-tuned fuzzy control can be as good as the best state-of-art traditional non-linear controls.

Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

PubMed

Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

2017-09-01

This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

Engineering Globular Protein Vesicles through Tunable Self-Assembly of Recombinant Fusion Proteins

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

Jang, Yeongseon; Choi, Won Tae; Heller, William T.

Vesicles assembled from folded, globular proteins have potential for functions different from traditional lipid or polymeric vesicles. However, they also present challenges in understanding the assembly process and controlling vesicle properties. From detailed investigation of the assembly behavior of recombinant fusion proteins, this work reports a simple strategy to engineer protein vesicles containing functional, globular domains. This is achieved through tunable self-assembly of recombinant globular fusion proteins containing leucine zippers and elastin-like polypeptides. The fusion proteins form complexes in solution via high affinity binding of the zippers, and transition through dynamic coacervates to stable hollow vesicles upon warming. The thermalmore » driving force, which can be tuned by protein concentration or temperature, controls both vesicle size and whether vesicles are single or bi-layered. Lastly, these results provide critical information to engineer globular protein vesicles via self-assembly with desired size and membrane structure.« less

Engineering Globular Protein Vesicles through Tunable Self-Assembly of Recombinant Fusion Proteins

DOE PAGES

Jang, Yeongseon; Choi, Won Tae; Heller, William T.; ...

2017-07-27

Vesicles assembled from folded, globular proteins have potential for functions different from traditional lipid or polymeric vesicles. However, they also present challenges in understanding the assembly process and controlling vesicle properties. From detailed investigation of the assembly behavior of recombinant fusion proteins, this work reports a simple strategy to engineer protein vesicles containing functional, globular domains. This is achieved through tunable self-assembly of recombinant globular fusion proteins containing leucine zippers and elastin-like polypeptides. The fusion proteins form complexes in solution via high affinity binding of the zippers, and transition through dynamic coacervates to stable hollow vesicles upon warming. The thermalmore » driving force, which can be tuned by protein concentration or temperature, controls both vesicle size and whether vesicles are single or bi-layered. Lastly, these results provide critical information to engineer globular protein vesicles via self-assembly with desired size and membrane structure.« less

Self-peeling of impacting droplets

NASA Astrophysics Data System (ADS)

de Ruiter, Jolet; Soto, Dan; Varanasi, Kripa K.

2018-01-01

Whether an impacting droplet sticks or not to a solid surface has been conventionally controlled by functionalizing the target surface or by using additives in the drop. Here we report on an unexpected self-peeling phenomenon that can happen even on smooth untreated surfaces by taking advantage of the solidification of the impacting drop and the thermal properties of the substrate. We control this phenomenon by tuning the coupling of the short-timescale fluid dynamics--leading to interfacial defects upon local freezing--and the longer-timescale thermo-mechanical stresses--leading to global deformation. We establish a regime map that predicts whether a molten metal drop impacting onto a colder substrate will bounce, stick or self-peel. In many applications, avoiding adhesion of impacting droplets around designated target surfaces can be as crucial as bonding onto them to minimize waste or cleaning. These insights have broad applicability in processes ranging from thermal spraying and additive manufacturing to extreme ultraviolet lithography.

Tunable volatile release from organogel-emulsions based on the self-assembly of β-sitosterol and γ-oryzanol.

PubMed

Chen, Xiao-Wei; Chen, Ya-Jun; Wang, Jin-Mei; Guo, Jian; Yin, Shou-Wei; Yang, Xiao-Quan

2017-04-15

A current challenge in the area of food emulsion is the design of microstructure that provides controlled release of volatile compounds during storage and consumption. Here, a new strategy addressed this problem at the fundamental level by describing the design of organogel-based emulsion from the self-assembly of β-sitosterol and γ-oryzanol that are capable of tuning volatile release. The results showed that the release rate (v 0 ), maximum headspace concentrations (C max ) and partition coefficients (k a / e ) above structured emulsions were significantly lower than unstructured emulsions and controlled release doing undergo tunable though the self-assembled interface and core fine microstructure from internal phase under dynamic and static condition. This result provides an understanding of how emulsions can behave as delivery system to better design novel food products with enhanced sensorial and nutritional attributes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Immunological self-tolerance: Lessons from mathematical modeling

NASA Astrophysics Data System (ADS)

Carneiro, Jorge; Paixao, Tiago; Milutinovic, Dejan; Sousa, Joao; Leon, Kalet; Gardner, Rui; Faro, Jose

2005-12-01

One of the fundamental properties of the immune system is its capacity to avoid autoimmune diseases. The mechanism underlying this process, known as self-tolerance, is hitherto unresolved but seems to involve the control of clonal expansion of autoreactive lymphocytes. This article reviews mathematical modeling of self-tolerance, addressing two specific hypotheses. The first hypothesis posits that self-tolerance is mediated by tuning of activation thresholds, which makes autoreactive T lymphocytes reversibly "anergic" and unable to proliferate. The second hypothesis posits that the proliferation of autoreactive T lymphocytes is instead controlled by specific regulatory T lymphocytes. Models representing the population dynamics of autoreactive T lymphocytes according to these two hypotheses were derived. For each model we identified how cell density affects tolerance, and predicted the corresponding phase spaces and bifurcations. We show that the simple induction of proliferative anergy, as modeled here, has a density dependence that is only partially compatible with adoptive transfers of tolerance, and that the models of tolerance mediated by specific regulatory T cells are closer to the observations.

Simultaneous gains tuning in boiler/turbine PID-based controller clusters using iterative feedback tuning methodology.

PubMed

Zhang, Shu; Taft, Cyrus W; Bentsman, Joseph; Hussey, Aaron; Petrus, Bryan

2012-09-01

Tuning a complex multi-loop PID based control system requires considerable experience. In today's power industry the number of available qualified tuners is dwindling and there is a great need for better tuning tools to maintain and improve the performance of complex multivariable processes. Multi-loop PID tuning is the procedure for the online tuning of a cluster of PID controllers operating in a closed loop with a multivariable process. This paper presents the first application of the simultaneous tuning technique to the multi-input-multi-output (MIMO) PID based nonlinear controller in the power plant control context, with the closed-loop system consisting of a MIMO nonlinear boiler/turbine model and a nonlinear cluster of six PID-type controllers. Although simplified, the dynamics and cross-coupling of the process and the PID cluster are similar to those used in a real power plant. The particular technique selected, iterative feedback tuning (IFT), utilizes the linearized version of the PID cluster for signal conditioning, but the data collection and tuning is carried out on the full nonlinear closed-loop system. Based on the figure of merit for the control system performance, the IFT is shown to deliver performance favorably comparable to that attained through the empirical tuning carried out by an experienced control engineer. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

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.

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload.

PubMed

Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A P

2016-05-01

Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Cherenkov Radiation Control via Self-accelerating Wave-packets.

PubMed

Hu, Yi; Li, Zhili; Wetzel, Benjamin; Morandotti, Roberto; Chen, Zhigang; Xu, Jingjun

2017-08-18

Cherenkov radiation is a ubiquitous phenomenon in nature. It describes electromagnetic radiation from a charged particle moving in a medium with a uniform velocity larger than the phase velocity of light in the same medium. Such a picture is typically adopted in the investigation of traditional Cherenkov radiation as well as its counterparts in different branches of physics, including nonlinear optics, spintronics and plasmonics. In these cases, the radiation emitted spreads along a "cone", making it impractical for most applications. Here, we employ a self-accelerating optical pump wave-packet to demonstrate controlled shaping of one type of generalized Cherenkov radiation - dispersive waves in optical fibers. We show that, by tuning the parameters of the wave-packet, the emitted waves can be judiciously compressed and focused at desired locations, paving the way to such control in any physical system.

Achieving increased bandwidth for 4 degree of freedom self-tuning energy harvester

NASA Astrophysics Data System (ADS)

Staaf, L. G. H.; Smith, A. D.; Köhler, E.; Lundgren, P.; Folkow, P. D.; Enoksson, P.

2018-04-01

The frequency response of a self-tuning energy harvester composed of two piezoelectric cantilevers connected by a middle beam with a sliding mass is investigated. Measurements show that incorporation of a free-sliding mass increases the bandwidth. Using an analytical model, the system is explained through close investigation of the resonance modes. Resonance mode behavior further suggests that, by breaking the symmetry of the system, even broader bandwidths are achievable.

Enhanced processing in arrays of optimally tuned nonlinear biomimetic sensors: A coupling-mediated Ringelmann effect and its dynamical mitigation

NASA Astrophysics Data System (ADS)

Nikitin, Alexander P.; Bulsara, Adi R.; Stocks, Nigel G.

2017-03-01

Inspired by recent results on self-tunability in the outer hair cells of the mammalian cochlea, we describe an array of magnetic sensors where each individual sensor can self-tune to an optimal operating regime. The self-tuning gives the array its "biomimetic" features. We show that the overall performance of the array can, as expected, be improved by increasing the number of sensors but, however, coupling between sensors reduces the overall performance even though the individual sensors in the system could see an improvement. We quantify the similarity of this phenomenon to the Ringelmann effect that was formulated 103 years ago to account for productivity losses in human and animal groups. We propose a global feedback scheme that can be used to greatly mitigate the performance degradation that would, normally, stem from the Ringelmann effect.

General Second-Order Scalar-Tensor Theory and Self-Tuning

NASA Astrophysics Data System (ADS)

Charmousis, Christos; Copeland, Edmund J.; Padilla, Antonio; Saffin, Paul M.

2012-02-01

Starting from the most general scalar-tensor theory with second-order field equations in four dimensions, we establish the unique action that will allow for the existence of a consistent self-tuning mechanism on Friedmann-Lemaître-Robertson-Walker backgrounds, and show how it can be understood as a combination of just four base Lagrangians with an intriguing geometric structure dependent on the Ricci scalar, the Einstein tensor, the double dual of the Riemann tensor, and the Gauss-Bonnet combination. Spacetime curvature can be screened from the net cosmological constant at any given moment because we allow the scalar field to break Poincaré invariance on the self-tuning vacua, thereby evading the Weinberg no-go theorem. We show how the four arbitrary functions of the scalar field combine in an elegant way opening up the possibility of obtaining nontrivial cosmological solutions.

Counterbalance of cutting force for advanced milling operations

NASA Astrophysics Data System (ADS)

Tsai, Nan-Chyuan; Shih, Li-Wen; Lee, Rong-Mao

2010-05-01

The goal of this work is to concurrently counterbalance the dynamic cutting force and regulate the spindle position deviation under various milling conditions by integrating active magnetic bearing (AMB) technique, fuzzy logic algorithm and an adaptive self-tuning feedback loop. Since the dynamics of milling system is highly determined by a few operation conditions, such as speed of spindle, cut depth and feedrate, therefore the dynamic model for cutting process is more appropriate to be constructed by experiments, instead of using theoretical approach. The experimental data, either for idle or cutting, are utilized to establish the database of milling dynamics so that the system parameters can be on-line estimated by employing the proposed fuzzy logic algorithm as the cutting mission is engaged. Based on the estimated milling system model and preset operation conditions, i.e., spindle speed, cut depth and feedrate, the current cutting force can be numerically estimated. Once the current cutting force can be real-time estimated, the corresponding compensation force can be exerted by the equipped AMB to counterbalance the cutting force, in addition to the spindle position regulation by feedback of spindle position. On the other hand, for the magnetic force is nonlinear with respect to the applied electric current and air gap, the characteristics of the employed AMB is investigated also by experiments and a nonlinear mathematic model, in terms of air gap between spindle and electromagnetic pole and coil current, is developed. At the end, the experimental simulations on realistic milling are presented to verify the efficacy of the fuzzy controller for spindle position regulation and the capability of the dynamic cutting force counterbalance.

An Adaptive Kalman Filter using a Simple Residual Tuning Method

NASA Technical Reports Server (NTRS)

Harman, Richard R.

1999-01-01

One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.

Telerobotic control of a mobile coordinated robotic server

NASA Technical Reports Server (NTRS)

Lee, Gordon

1991-01-01

Results from the Master's Degree Thesis of Mr. Robert Stanley, a graduate student supervised by the principal investigator on this project is reported. The goal of this effort is to develop advanced control methods for flexible space manipulator systems. As such, a fuzzy logic controller has been developed in which model structure as well as parameter constraints are not required for compensation. A general rule base is formulated using quantized linguistic terms; it is then augmented to a traditional integral control. The resulting hybrid fuzzy controller stabilizes the structure over a broad range of uncertainties, including unknown initial conditions. An off-line tuning approach using phase portraits gives further insight into the algorithm. The approach was applied to a three-degree-of-freedom manipulator system - the prototype of the coordinated flexible manipulator system currently being designed and built at North Carolina State University.

Multilevel adaptive control of nonlinear interconnected systems.

PubMed

Motallebzadeh, Farzaneh; Ozgoli, Sadjaad; Momeni, Hamid Reza

2015-01-01

This paper presents an adaptive backstepping-based multilevel approach for the first time to control nonlinear interconnected systems with unknown parameters. The system consists of a nonlinear controller at the first level to neutralize the interaction terms, and some adaptive controllers at the second level, in which the gains are optimally tuned using genetic algorithm. The presented scheme can be used in systems with strong couplings where completely ignoring the interactions leads to problems in performance or stability. In order to test the suitability of the method, two case studies are provided: the uncertain double and triple coupled inverted pendulums connected by springs with unknown parameters. The simulation results show that the method is capable of controlling the system effectively, in both regulation and tracking tasks. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Tracking Control of Shape-Memory-Alloy Actuators Based on Self-Sensing Feedback and Inverse Hysteresis Compensation

PubMed Central

Liu, Shu-Hung; Huang, Tse-Shih; Yen, Jia-Yush

2010-01-01

Shape memory alloys (SMAs) offer a high power-to-weight ratio, large recovery strain, and low driving voltages, and have thus attracted considerable research attention. The difficulty of controlling SMA actuators arises from their highly nonlinear hysteresis and temperature dependence. This paper describes a combination of self-sensing and model-based control, where the model includes both the major and minor hysteresis loops as well as the thermodynamics effects. The self-sensing algorithm uses only the power width modulation (PWM) signal and requires no heavy equipment. The method can achieve high-accuracy servo control and is especially suitable for miniaturized applications. PMID:22315530

Next generation of decision making software for nanopatterns characterization: application to semiconductor industry

NASA Astrophysics Data System (ADS)

Dervilllé, A.; Labrosse, A.; Zimmermann, Y.; Foucher, J.; Gronheid, R.; Boeckx, C.; Singh, A.; Leray, P.; Halder, S.

2016-03-01

The dimensional scaling in IC manufacturing strongly drives the demands on CD and defect metrology techniques and their measurement uncertainties. Defect review has become as important as CD metrology and both of them create a new metrology paradigm because it creates a completely new need for flexible, robust and scalable metrology software. Current, software architectures and metrology algorithms are performant but it must be pushed to another higher level in order to follow roadmap speed and requirements. For example: manage defect and CD in one step algorithm, customize algorithms and outputs features for each R&D team environment, provide software update every day or every week for R&D teams in order to explore easily various development strategies. The final goal is to avoid spending hours and days to manually tune algorithm to analyze metrology data and to allow R&D teams to stay focus on their expertise. The benefits are drastic costs reduction, more efficient R&D team and better process quality. In this paper, we propose a new generation of software platform and development infrastructure which can integrate specific metrology business modules. For example, we will show the integration of a chemistry module dedicated to electronics materials like Direct Self Assembly features. We will show a new generation of image analysis algorithms which are able to manage at the same time defect rates, images classifications, CD and roughness measurements with high throughput performances in order to be compatible with HVM. In a second part, we will assess the reliability, the customization of algorithm and the software platform capabilities to follow new specific semiconductor metrology software requirements: flexibility, robustness, high throughput and scalability. Finally, we will demonstrate how such environment has allowed a drastic reduction of data analysis cycle time.

Intelligent Tracking Control for a Class of Uncertain High-Order Nonlinear Systems.

PubMed

Zhao, Xudong; Shi, Peng; Zheng, Xiaolong; Zhang, Jianhua

2016-09-01

This brief is concerned with the problem of intelligent tracking control for a class of high-order nonlinear systems with completely unknown nonlinearities. An intelligent adaptive control algorithm is presented by combining the adaptive backstepping technique with the neural networks' approximation ability. It is shown that the practical output tracking performance of the system is achieved using the proposed state-feedback controller under two mild assumptions. In particular, by introducing a parameter in the derivations, the tracking error between the time-varying target signal and the output can be reduced via tuning the controller design parameters. Moreover, in order to solve the problem of overparameterization, which is a common issue in adaptive control design, a controller with one adaptive law is also designed. Finally, simulation results are given to show the effectiveness of the theoretical approaches and the potential of the proposed new design techniques.

Metal-coordination: Using one of nature’s tricks to control soft material mechanics

PubMed Central

Holten-Andersen, Niels; Jaishankar, Aditya; Harrington, Matthew; Fullenkamp, Dominic E.; DiMarco, Genevieve; He, Lihong; McKinley, Gareth H.; Messersmith, Phillip B.; Lee, Ka Yee C.

2015-01-01

Growing evidence supports a critical role of dynamic metal-coordination crosslinking in soft biological material properties such as self-healing and underwater adhesion1. Using bio-inspired metal-coordinating polymers, initial efforts to mimic these properties have shown promise2. Here we demonstrate how bio-inspired aqueous polymer network mechanics can be easily controlled via metal-coordination crosslink dynamics; metal ion-based crosslink stability control allows aqueous polymer network relaxation times to be finely tuned over several orders of magnitude. In addition to further biological material insights, our demonstration of this compositional scaling mechanism should provide inspiration for new polymer material property-control designs. PMID:26413297

Training self-assessment and task-selection skills to foster self-regulated learning: Do trained skills transfer across domains?

PubMed

Raaijmakers, Steven F; Baars, Martine; Paas, Fred; van Merriënboer, Jeroen J G; van Gog, Tamara

2018-01-01

Students' ability to accurately self-assess their performance and select a suitable subsequent learning task in response is imperative for effective self-regulated learning. Video modeling examples have proven effective for training self-assessment and task-selection skills, and-importantly-such training fostered self-regulated learning outcomes. It is unclear, however, whether trained skills would transfer across domains. We investigated whether skills acquired from training with either a specific, algorithmic task-selection rule or a more general heuristic task-selection rule in biology would transfer to self-regulated learning in math. A manipulation check performed after the training confirmed that both algorithmic and heuristic training improved task-selection skills on the biology problems compared with the control condition. However, we found no evidence that students subsequently applied the acquired skills during self-regulated learning in math. Future research should investigate how to support transfer of task-selection skills across domains.

Investigating the ability of shunt hybrid power filter based on SRF method under non-ideal supply voltage

NASA Astrophysics Data System (ADS)

Omran, Mohamed A.; Mohd, Izzeldin I.; Almelian, Mohamad M.; Ullah Sheikh, Usman; Bofares, Mustafa E. A. A.

2018-04-01

This study presents the capacity of a self-tuning filter based on the synchronous reference frame method with a fuzzy logic controller for the improvement of the efficiency of harmonic suppression of a shunt hybrid active power filter in an unbalanced distorted and un-distorted voltage supply conditions. The simulation results indicated that the filter with a fuzzy logic controller had a good filtering performance in steady and transient states, irrespective of whether the voltage supply is distorted or unbalanced.

Data-driven advice for applying machine learning to bioinformatics problems

PubMed Central

Olson, Randal S.; La Cava, William; Mustahsan, Zairah; Varik, Akshay; Moore, Jason H.

2017-01-01

As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems. PMID:29218881

Demonstration of a wireless, self-powered, electroacoustic liner system.

PubMed

Phipps, Alex; Liu, Fei; Cattafesta, Louis; Sheplak, Mark; Nishida, Toshikazu

2009-02-01

This paper demonstrates the system operation of a self-powered active liner for the suppression of aircraft engine noise. The fundamental element of the active liner system is an electromechanical Helmholtz resonator (EMHR), which consists of a Helmholtz resonator with one of its rigid walls replaced with a circular piezoceramic composite plate. For this system demonstration, two EMHR elements are used, one for acoustic impedance tuning and one for energy harvesting. The EMHR used for acoustic impedance tuning is shunted with a variable resistive load, while the EMHR used for energy harvesting is shunted to a flyback power converter and storage element. The desired acoustic impedance conditions are determined externally, and wirelessly transmitted to the liner system. The power for the receiver and the impedance tuning circuitry in the liner are supplied by the harvested energy. Tuning of the active liner is demonstrated at three different sound pressure levels (148, 151, and 153 dB) in order to show the robustness of the energy harvesting and storage system. An acoustic tuning range of approximately 200 Hz is demonstrated for each of the three available power levels.

ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform.

PubMed

El B'charri, Oussama; Latif, Rachid; Elmansouri, Khalifa; Abenaou, Abdenbi; Jenkal, Wissam

2017-02-07

Since the electrocardiogram (ECG) signal has a low frequency and a weak amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic accuracy and hinder the physician's correct decision on patients. The dual tree wavelet transform (DT-WT) is one of the most recent enhanced versions of discrete wavelet transform. However, threshold tuning on this method for noise removal from ECG signal has not been investigated yet. In this work, we shall provide a comprehensive study on the impact of the choice of threshold algorithm, threshold value, and the appropriate wavelet decomposition level to evaluate the ECG signal de-noising performance. A set of simulations is performed on both synthetic and real ECG signals to achieve the promised results. First, the synthetic ECG signal is used to observe the algorithm response. The evaluation results of synthetic ECG signal corrupted by various types of noise has showed that the modified unified threshold and wavelet hyperbolic threshold de-noising method is better in realistic and colored noises. The tuned threshold is then used on real ECG signals from the MIT-BIH database. The results has shown that the proposed method achieves higher performance than the ordinary dual tree wavelet transform into all kinds of noise removal from ECG signal. The simulation results indicate that the algorithm is robust for all kinds of noises with varying degrees of input noise, providing a high quality clean signal. Moreover, the algorithm is quite simple and can be used in real time ECG monitoring.

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.

Simultaneous vibration control and energy harvesting using actor-critic based reinforcement learning

NASA Astrophysics Data System (ADS)

Loong, Cheng Ning; Chang, C. C.; Dimitrakopoulos, Elias G.

2018-03-01

Mitigating excessive vibration of civil engineering structures using various types of devices has been a conspicuous research topic in the past few decades. Some devices, such as electromagnetic transducers, which have a capability of exerting control forces while simultaneously harvesting energy, have been proposed recently. These devices make possible a self-regenerative system that can semi-actively mitigate structural vibration without the need of external energy. Integrating mechanical, electrical components, and control algorithms, these devices open up a new research domain that needs to be addressed. In this study, the feasibility of using an actor-critic based reinforcement learning control algorithm for simultaneous vibration control and energy harvesting for a civil engineering structure is investigated. The actor-critic based reinforcement learning control algorithm is a real-time, model-free adaptive technique that can adjust the controller parameters based on observations and reward signals without knowing the system characteristics. It is suitable for the control of a partially known nonlinear system with uncertain parameters. The feasibility of implementing this algorithm on a building structure equipped with an electromagnetic damper will be investigated in this study. Issues related to the modelling of learning algorithm, initialization and convergence will be presented and discussed.

Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot

PubMed Central

Amador-Angulo, Leticia; Mendoza, Olivia; Castro, Juan R.; Rodríguez-Díaz, Antonio; Melin, Patricia; Castillo, Oscar

2016-01-01

A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm. PMID:27618062

Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

PubMed

Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

2013-10-01

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

Changes to the COS Extraction Algorithm for Lifetime Position 3

NASA Astrophysics Data System (ADS)

Proffitt, Charles R.; Bostroem, K. Azalee; Ely, Justin; Foster, Deatrick; Hernandez, Svea; Hodge, Philip; Jedrzejewski, Robert I.; Lockwood, Sean A.; Massa, Derck; Peeples, Molly S.; Oliveira, Cristina M.; Penton, Steven V.; Plesha, Rachel; Roman-Duval, Julia; Sana, Hugues; Sahnow, David J.; Sonnentrucker, Paule; Taylor, Joanna M.

2015-09-01

The COS FUV Detector Lifetime Position 3 (LP3) has been placed only 2.5" below the original lifetime position (LP1). This is sufficiently close to gain-sagged regions at LP1 that a revised extraction algorithm is needed to ensure good spectral quality. We provide an overview of this new "TWOZONE" extraction algorithm, discuss its strengths and limitations, describe new output columns in the X1D files that show the boundaries of the new extraction regions, and provide some advice on how to manually tune the algorithm for specialized applications.

Structural damage identification using an enhanced thermal exchange optimization algorithm

NASA Astrophysics Data System (ADS)

Kaveh, A.; Dadras, A.

2018-03-01

The recently developed optimization algorithm-the so-called thermal exchange optimization (TEO) algorithm-is enhanced and applied to a damage detection problem. An offline parameter tuning approach is utilized to set the internal parameters of the TEO, resulting in the enhanced heat transfer optimization (ETEO) algorithm. The damage detection problem is defined as an inverse problem, and ETEO is applied to a wide range of structures. Several scenarios with noise and noise-free modal data are tested and the locations and extents of damages are identified with good accuracy.

How to model supernovae in simulations of star and galaxy formation

NASA Astrophysics Data System (ADS)

Hopkins, Philip F.; Wetzel, Andrew; Kereš, Dušan; Faucher-Giguère, Claude-André; Quataert, Eliot; Boylan-Kolchin, Michael; Murray, Norman; Hayward, Christopher C.; El-Badry, Kareem

2018-06-01

We study the implementation of mechanical feedback from supernovae (SNe) and stellar mass loss in galaxy simulations, within the Feedback In Realistic Environments (FIRE) project. We present the FIRE-2 algorithm for coupling mechanical feedback, which can be applied to any hydrodynamics method (e.g. fixed-grid, moving-mesh, and mesh-less methods), and black hole as well as stellar feedback. This algorithm ensures manifest conservation of mass, energy, and momentum, and avoids imprinting `preferred directions' on the ejecta. We show that it is critical to incorporate both momentum and thermal energy of mechanical ejecta in a self-consistent manner, accounting for SNe cooling radii when they are not resolved. Using idealized simulations of single SN explosions, we show that the FIRE-2 algorithm, independent of resolution, reproduces converged solutions in both energy and momentum. In contrast, common `fully thermal' (energy-dump) or `fully kinetic' (particle-kicking) schemes in the literature depend strongly on resolution: when applied at mass resolution ≳100 M⊙, they diverge by orders of magnitude from the converged solution. In galaxy-formation simulations, this divergence leads to orders-of-magnitude differences in galaxy properties, unless those models are adjusted in a resolution-dependent way. We show that all models that individually time-resolve SNe converge to the FIRE-2 solution at sufficiently high resolution (<100 M⊙). However, in both idealized single-SN simulations and cosmological galaxy-formation simulations, the FIRE-2 algorithm converges much faster than other sub-grid models without re-tuning parameters.

A Numerical Optimization Approach for Tuning Fuzzy Logic Controllers

NASA Technical Reports Server (NTRS)

Woodard, Stanley E.; Garg, Devendra P.

1998-01-01

This paper develops a method to tune fuzzy controllers using numerical optimization. The main attribute of this approach is that it allows fuzzy logic controllers to be tuned to achieve global performance requirements. Furthermore, this approach allows design constraints to be implemented during the tuning process. The method tunes the controller by parameterizing the membership functions for error, change-in-error and control output. The resulting parameters form a design vector which is iteratively changed to minimize an objective function. The minimal objective function results in an optimal performance of the system. A spacecraft mounted science instrument line-of-sight pointing control is used to demonstrate results.

Constraining ecosystem model with adaptive Metropolis algorithm using boreal forest site eddy covariance measurements

NASA Astrophysics Data System (ADS)

Mäkelä, Jarmo; Susiluoto, Jouni; Markkanen, Tiina; Aurela, Mika; Järvinen, Heikki; Mammarella, Ivan; Hagemann, Stefan; Aalto, Tuula

2016-12-01

We examined parameter optimisation in the JSBACH (Kaminski et al., 2013; Knorr and Kattge, 2005; Reick et al., 2013) ecosystem model, applied to two boreal forest sites (Hyytiälä and Sodankylä) in Finland. We identified and tested key parameters in soil hydrology and forest water and carbon-exchange-related formulations, and optimised them using the adaptive Metropolis (AM) algorithm for Hyytiälä with a 5-year calibration period (2000-2004) followed by a 4-year validation period (2005-2008). Sodankylä acted as an independent validation site, where optimisations were not made. The tuning provided estimates for full distribution of possible parameters, along with information about correlation, sensitivity and identifiability. Some parameters were correlated with each other due to a phenomenological connection between carbon uptake and water stress or other connections due to the set-up of the model formulations. The latter holds especially for vegetation phenology parameters. The least identifiable parameters include phenology parameters, parameters connecting relative humidity and soil dryness, and the field capacity of the skin reservoir. These soil parameters were masked by the large contribution from vegetation transpiration. In addition to leaf area index and the maximum carboxylation rate, the most effective parameters adjusting the gross primary production (GPP) and evapotranspiration (ET) fluxes in seasonal tuning were related to soil wilting point, drainage and moisture stress imposed on vegetation. For daily and half-hourly tunings the most important parameters were the ratio of leaf internal CO2 concentration to external CO2 and the parameter connecting relative humidity and soil dryness. Effectively the seasonal tuning transferred water from soil moisture into ET, and daily and half-hourly tunings reversed this process. The seasonal tuning improved the month-to-month development of GPP and ET, and produced the most stable estimates of water use efficiency. When compared to the seasonal tuning, the daily tuning is worse on the seasonal scale. However, daily parametrisation reproduced the observations for average diurnal cycle best, except for the GPP for Sodankylä validation period, where half-hourly tuned parameters were better. In general, the daily tuning provided the largest reduction in model-data mismatch. The models response to drought was unaffected by our parametrisations and further studies are needed into enhancing the dry response in JSBACH.

Experimental investigation of dynamic performance of a prototype hybrid tuned mass damper under human excitation

NASA Astrophysics Data System (ADS)

Noormohammadi, Nima; Reynolds, Paul

2013-04-01

Current sport stadia designs focus mainly on maximizing audience capacity and providing a clear view for all spectators. Hence, incorporation of one or more cantilevered tiers is typical in these designs. However, employing such cantilevered tiers, usually with relatively low damping and natural frequencies, can make grandstands more susceptible to excitation by human activities. This is caused by the coincidence between the activity frequencies (and their lowest three harmonics) and the structural natural frequencies hence raising the possibility of resonant vibration. This can be both a vibration serviceability and a safety issue. Past solutions to deal with observed or anticipated vibration serviceability problems have been mainly passive methods, such as tuned mass dampers (TMDs). These techniques have exhibited problems such as lack of performance and offtuning caused by human-structure interaction. To address this issue, research is currently underway to investigate the possible application of hybrid TMDs (HTMDs), which are a combination of active and passive control, to improve the vibration serviceability of such structures under human excitation. The work presented here shows a comparative experimental investigation of a passive TMD and a prototype HTMD applied on a slab strip structure. The most effective control algorithm to enhance the performance of the HTMD and also deal with the off-tuning problem is investigated. The laboratory structure used here is an in-situ cast simply-supported post-tensioned slab strip excited by forces from a range of human activities.

Adaptive synchronized switch damping on an inductor: a self-tuning switching law

NASA Astrophysics Data System (ADS)

Kelley, Christopher R.; Kauffman, Jeffrey L.

2017-03-01

Synchronized switch damping (SSD) techniques exploit low-power switching between passive circuits connected to piezoelectric material to reduce structural vibration. In the classical implementation of SSD, the piezoelectric material remains in an open circuit for the majority of the vibration cycle and switches briefly to a shunt circuit at every displacement extremum. Recent research indicates that this switch timing is only optimal for excitation exactly at resonance and points to more general optimal switch criteria based on the phase of the displacement and the system parameters. This work proposes a self-tuning approach that implements the more general optimal switch timing for synchronized switch damping on an inductor (SSDI) without needing any knowledge of the system parameters. The law involves a gradient-based search optimization that is robust to noise and uncertainties in the system. Testing of a physical implementation confirms this law successfully adapts to the frequency and parameters of the system. Overall, the adaptive SSDI controller provides better off-resonance steady-state vibration reduction than classical SSDI while matching performance at resonance.

Development of an MR seat suspension with self-powered generation capability

NASA Astrophysics Data System (ADS)

Sun, S. S.; Ning, D. H.; Yang, J.; Du, H.; Zhang, S. W.; Li, W. H.; Nakano, M.

2017-08-01

This paper proposes a self-powered magnetorheological (MR) seat suspension on the basis of a rotary MR damper and an electromagnetic induction device. By applying the self-powering component to the MR seat suspension, the operation cost of the semi-active seat is much cheaper because no external energy is required to control the MR damper. In this paper, the structure, design and analysis of the seat suspension were presented following the introduction section. The property tests of the self-powered seat suspension were conducted using an MTS machine. A robust control algorithm was developed to control the self-powered MR seat suspension and the vibration attenuation performance of the seat suspension was tested under two different vibration excitations, i.e. harmonic excitation and random excitation. The testing result verifies that the self-powered MR seat suspension under proper control can improve the ride comfort for passengers and drivers.

rf measurements and tuning of the 750 MHz radio frequency quadrupole

NASA Astrophysics Data System (ADS)

Koubek, Benjamin; Grudiev, Alexej; Timmins, Marc

2017-08-01

In the framework of the program on medical applications a compact 750 MHz RFQ has been designed and built to be used as an injector for a hadron therapy linac. This RFQ was designed to accelerate protons to an energy of 5 MeV within only 2 m length. It is divided into four segments and equipped with 32 tuners in total. The length of the RFQ corresponds to 5 λ which is considered to be close to the limit for field adjustment using only piston tuners. Moreover the high frequency, which is about double the frequency of existing RFQs, results in a sensitive structure and requires careful tuning. In this paper we present the tuning algorithm, the tuning procedure and rf measurements of the RFQ.

Supramolecular guests in solvent driven block copolymer assembly: From internally structured nanoparticles to micelles

PubMed Central

Klinger, Daniel; Robb, Maxwell J.; Spruell, Jason M.; Lynd, Nathaniel A.; Hawker, Craig J.

2014-01-01

Supramolecular interactions between different hydrogen-bonding guests and poly(2-vinyl pyridine)-block-poly (styrene) can be exploited to prepare remarkably diverse self-assembled nanostructures in dispersion from a single block copolymer (BCP). The characteristics of the BCP can be efficiently controlled by tailoring the properties of a guest which preferentially binds to the P2VP block. For example, the incorporation of a hydrophobic guest creates a hydrophobic BCP complex that forms phase separated nanoparticles upon self-assembly. Conversely, the incorporation of a hydrophilic guest results in an amphiphilic BCP complex that forms spherical micelles in water. The ability to tune the self-assembly behavior and access dramatically different nanostructures from a single BCP substrate demonstrates the exceptional versatility of the self-assembly of BCPs driven by supramolecular interactions. This approach represents a new methodology that will enable the further design of complex, responsive self-assembled nanostructures. PMID:25525473

Interior search algorithm (ISA): a novel approach for global optimization.

PubMed

Gandomi, Amir H

2014-07-01

This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Traveling Salesman Problem for Surveillance Mission Using Particle Swarm Optimization

DTIC Science & Technology

2001-03-20

design of experiments, results of the experiments, and qualitative and quantitative analysis . Conclusions and recommendations based on the qualitative and...characterize the algorithm. Such analysis and comparison between LK and a non-deterministic algorithm produces claims such as "Lin-Kernighan algorithm takes... based on experiments 5 and 6. All other parameters are the same as the baseline (see 4.2.1.2). 4.2.2.6 Experiment 10 - Fine Tuning PSO AS: 85,95% Global

A distributed model predictive control scheme for leader-follower multi-agent systems

NASA Astrophysics Data System (ADS)

Franzè, Giuseppe; Lucia, Walter; Tedesco, Francesco

2018-02-01

In this paper, we present a novel receding horizon control scheme for solving the formation problem of leader-follower configurations. The algorithm is based on set-theoretic ideas and is tuned for agents described by linear time-invariant (LTI) systems subject to input and state constraints. The novelty of the proposed framework relies on the capability to jointly use sequences of one-step controllable sets and polyhedral piecewise state-space partitions in order to online apply the 'better' control action in a distributed receding horizon fashion. Moreover, we prove that the design of both robust positively invariant sets and one-step-ahead controllable regions is achieved in a distributed sense. Simulations and numerical comparisons with respect to centralised and local-based strategies are finally performed on a group of mobile robots to demonstrate the effectiveness of the proposed control strategy.

Real-time simulation of the nonlinear visco-elastic deformations of soft tissues.

PubMed

Basafa, Ehsan; Farahmand, Farzam

2011-05-01

Mass-spring-damper (MSD) models are often used for real-time surgery simulation due to their fast response and fairly realistic deformation replication. An improved real time simulation model of soft tissue deformation due to a laparoscopic surgical indenter was developed and tested. The mechanical realization of conventional MSD models was improved using nonlinear springs and nodal dampers, while their high computational efficiency was maintained using an adapted implicit integration algorithm. New practical algorithms for model parameter tuning, collision detection, and simulation were incorporated. The model was able to replicate complex biological soft tissue mechanical properties under large deformations, i.e., the nonlinear and viscoelastic behaviors. The simulated response of the model after tuning of its parameters to the experimental data of a deer liver sample, closely tracked the reference data with high correlation and maximum relative differences of less than 5 and 10%, for the tuning and testing data sets respectively. Finally, implementation of the proposed model and algorithms in a graphical environment resulted in a real-time simulation with update rates of 150 Hz for interactive deformation and haptic manipulation, and 30 Hz for visual rendering. The proposed real time simulation model of soft tissue deformation due to a laparoscopic surgical indenter was efficient, realistic, and accurate in ex vivo testing. This model is a suitable candidate for testing in vivo during laparoscopic surgery.

Does a patient-managed insulin intensification strategy with insulin glargine and insulin glulisine provide similar glycemic control as a physician-managed strategy? Results of the START (Self-Titration With Apidra to Reach Target) Study: a randomized noninferiority trial.

PubMed

Harris, Stewart B; Yale, Jean-François; Berard, Lori; Stewart, John; Abbaszadeh, Babak; Webster-Bogaert, Susan; Gerstein, Hertzel C

2014-01-01

OBJECTIVE Diabetes self-management is universally regarded as a foundation of diabetes care. We determined whether comparable glycemic control could be achieved by self-titration versus physician titration of a once-daily bolus insulin dose in patients with type 2 diabetes who are unable to achieve optimal glycemia control with a basal insulin. RESEARCH DESIGN AND METHODS Patients with type 2 diabetes, an HbA1c level >7% (53 mmol/mol), and either nocturnal hypoglycemia episodes or an insufficient basal insulin glargine level (with or without oral agents) to achieve a fasting plasma glucose level ≤6 mmol/L (108 mg/dL) were studied. Participants all had bolus insulin glulisine added at breakfast and were allocated to either algorithm-guided patient self-titration or physician titration. The primary outcome was an HbA1c level ≤7% (53 mmol/mol) without severe hypoglycemia. RESULTS After a mean (SD) follow-up of 159.4 days (36.2 days), 28.4% of participants in the self-titration arm vs. 21.2% in the physician titration arm achieved an HbA1c level of ≤7% (53 mmol/mol) without severe hypoglycemia (between-group absolute difference 7.2%; 95% CI -3.2 to 17.7). The lower end of this 95% confidence interval was within the predetermined noninferiority boundary of -5% (P noninferiority = 0.011). CONCLUSIONS In stable patients with type 2 diabetes who are receiving doses of basal insulin glargine who require bolus insulin, a simple bolus insulin patient-managed titration algorithm is as effective as a physician-managed algorithm.

Thermal tuning the reversible optical band gap of self-assembled polystyrene photonic crystals

NASA Astrophysics Data System (ADS)

Vakili Tahami, S. H.; Pourmahdian, S.; Shirkavand Hadavand, B.; Azizi, Z. S.; Tehranchi, M. M.

2016-11-01

Nano-sized polymeric colloidal particles could undergo self-organization into three-dimensional structures to produce desired optical properties. In this research, a facile emulsifier-free emulsion polymerization method was employed to synthesize highly mono-disperse sub-micron polystyrene colloids. A high quality photonic crystal (PhC) structure was prepared by colloidal polystyrene. The reversible thermal tuning effect on photonic band gap position as well as the attenuation of the band gap was investigated in detail. The position of PBG can be tuned from 420 nm to 400 nm by varying the temperature of the PhC structure, reversibly. This reversible effect provides a reconfigurable PhC structure which could be used as thermo-responsive shape memory polymers.

Development of a Self Powered Vehicle Detector

DTIC Science & Technology

1978-10-01

Low Power RFTelemetry Link, Audio Tone kncoder/Decoder, 9mn’dlrectional Microstrip Antenna, RF Oscillator , RF Transmitter, Battery/ Solar Cell Tests...tuned Colpitts oscillator using a fundamental mode crystal, a reactance modulator (varactor diode), and a collector tank circuit tuned to the second...papers discussing this type of VCXO. The basic Colpitts oscillator equivalent circuit is shown in Figure 29 having a collector tank tuned to the 2nd

Hierarchical self-assembly of actin in micro-confinements using microfluidics

PubMed Central

Deshpande, Siddharth; Pfohl, Thomas

2012-01-01

We present a straightforward microfluidics system to achieve step-by-step reaction sequences in a diffusion-controlled manner in quasi two-dimensional micro-confinements. We demonstrate the hierarchical self-organization of actin (actin monomers—entangled networks of filaments—networks of bundles) in a reversible fashion by tuning the Mg2+ ion concentration in the system. We show that actin can form networks of bundles in the presence of Mg2+ without any cross-linking proteins. The properties of these networks are influenced by the confinement geometry. In square microchambers we predominantly find rectangular networks, whereas triangular meshes are predominantly found in circular chambers. PMID:24032070

Well-tempered metadynamics: a smoothly-converging and tunable free-energy method

NASA Astrophysics Data System (ADS)

Barducci, Alessandro; Bussi, Giovanni; Parrinello, Michele

2008-03-01

We present [1] a method for determining the free energy dependence on a selected number of order parameters using an adaptive bias. The formalism provides a unified description which has metadynamics and canonical sampling as limiting cases. Convergence and errors can be rigorously and easily controlled. The parameters of the simulation can be tuned so as to focus the computational effort only on the physically relevantregions of the order parameter space. The algorithm is tested on the reconstruction of alanine dipeptide free energy landscape. [1] A. Barducci, G. Bussi and M. Parrinello, Phys. Rev. Lett., accepted (2007).

Speed Control Law for Precision Terminal Area In-Trail Self Spacing

NASA Technical Reports Server (NTRS)

Abbott, Terence S.

2002-01-01

This document describes a speed control law for precision in-trail airborne self-spacing during final approach. This control law was designed to provide an operationally viable means to obtain a desired runway threshold crossing time or minimum distance, one aircraft relative to another. The control law compensates for dissimilar final approach speeds between aircraft pairs and provides guidance for a stable final approach. This algorithm has been extensively tested in Monte Carlo simulation and has been evaluated in piloted simulation, with preliminary results indicating acceptability from operational and workload standpoints.

Self-localization for an autonomous mobile robot based on an omni-directional vision system

NASA Astrophysics Data System (ADS)

Chiang, Shu-Yin; Lin, Kuang-Yu; Chia, Tsorng-Lin

2013-12-01

In this study, we designed an autonomous mobile robot based on the rules of the Federation of International Robotsoccer Association (FIRA) RoboSot category, integrating the techniques of computer vision, real-time image processing, dynamic target tracking, wireless communication, self-localization, motion control, path planning, and control strategy to achieve the contest goal. The self-localization scheme of the mobile robot is based on the algorithms featured in the images from its omni-directional vision system. In previous works, we used the image colors of the field goals as reference points, combining either dual-circle or trilateration positioning of the reference points to achieve selflocalization of the autonomous mobile robot. However, because the image of the game field is easily affected by ambient light, positioning systems exclusively based on color model algorithms cause errors. To reduce environmental effects and achieve the self-localization of the robot, the proposed algorithm is applied in assessing the corners of field lines by using an omni-directional vision system. Particularly in the mid-size league of the RobotCup soccer competition, selflocalization algorithms based on extracting white lines from the soccer field have become increasingly popular. Moreover, white lines are less influenced by light than are the color model of the goals. Therefore, we propose an algorithm that transforms the omni-directional image into an unwrapped transformed image, enhancing the extraction features. The process is described as follows: First, radical scan-lines were used to process omni-directional images, reducing the computational load and improving system efficiency. The lines were radically arranged around the center of the omni-directional camera image, resulting in a shorter computational time compared with the traditional Cartesian coordinate system. However, the omni-directional image is a distorted image, which makes it difficult to recognize the position of the robot. Therefore, image transformation was required to implement self-localization. Second, we used an approach to transform the omni-directional images into panoramic images. Hence, the distortion of the white line can be fixed through the transformation. The interest points that form the corners of the landmark were then located using the features from accelerated segment test (FAST) algorithm. In this algorithm, a circle of sixteen pixels surrounding the corner candidate is considered and is a high-speed feature detector in real-time frame rate applications. Finally, the dual-circle, trilateration, and cross-ratio projection algorithms were implemented in choosing the corners obtained from the FAST algorithm and localizing the position of the robot. The results demonstrate that the proposed algorithm is accurate, exhibiting a 2-cm position error in the soccer field measuring 600 cm2 x 400 cm2.

Self-assembly of silica nanoparticles by tuning substrate-adsorbate interaction

NASA Astrophysics Data System (ADS)

Utsav, Khanna, Sakshum; Mukhopadhayay, Indrajit; Banerjee, Rupak

2018-05-01

We report on self-assembled nanodisc formations of silica nanoparticles on a surface modified silicon substrate using modified Langmuir-Schafer deposition technique (stamping). The size, inter-particle separation as well as the packing of the silica nanoparticles within the nanodiscs formed spontaneously can be tuned by the surface pressure applied on the water surface. We obtain self-assembled nanodiscs of silica nanoparticle arranged in a hexagonal symmetry. We also observe that by varying the surface pressure of deposition at the water-molecule-air interface we obtain such 2D disc-shaped structure with varying sizes and a packing ratio of the silica nanoparticle.

Improving substructure identification accuracy of shear structures using virtual control system

NASA Astrophysics Data System (ADS)

Zhang, Dongyu; Yang, Yang; Wang, Tingqiang; Li, Hui

2018-02-01

Substructure identification is a powerful tool to identify the parameters of a complex structure. Previously, the authors developed an inductive substructure identification method for shear structures. The identification error analysis showed that the identification accuracy of this method is significantly influenced by the magnitudes of two key structural responses near a certain frequency; if these responses are unfavorable, the method cannot provide accurate estimation results. In this paper, a novel method is proposed to improve the substructure identification accuracy by introducing a virtual control system (VCS) into the structure. A virtual control system is a self-balanced system, which consists of some control devices and a set of self-balanced forces. The self-balanced forces counterbalance the forces that the control devices apply on the structure. The control devices are combined with the structure to form a controlled structure used to replace the original structure in the substructure identification; and the self-balance forces are treated as known external excitations to the controlled structure. By optimally tuning the VCS’s parameters, the dynamic characteristics of the controlled structure can be changed such that the original structural responses become more favorable for the substructure identification and, thus, the identification accuracy is improved. A numerical example of 6-story shear structure is utilized to verify the effectiveness of the VCS based controlled substructure identification method. Finally, shake table tests are conducted on a 3-story structural model to verify the efficacy of the VCS to enhance the identification accuracy of the structural parameters.