A general program to compute the multivariable stability margin for systems with parametric uncertainty

NASA Technical Reports Server (NTRS)

Sanchez Pena, Ricardo S.; Sideris, Athanasios

1988-01-01

A computer program implementing an algorithm for computing the multivariable stability margin to check the robust stability of feedback systems with real parametric uncertainty is proposed. The authors present in some detail important aspects of the program. An example is presented using lateral directional control system.

Robust Decentralized Nonlinear Control for a Twin Rotor MIMO System

PubMed Central

Belmonte, Lidia María; Morales, Rafael; Fernández-Caballero, Antonio; Somolinos, José Andrés

2016-01-01

This article presents the design of a novel decentralized nonlinear multivariate control scheme for an underactuated, nonlinear and multivariate laboratory helicopter denominated the twin rotor MIMO system (TRMS). The TRMS is characterized by a coupling effect between rotor dynamics and the body of the model, which is due to the action-reaction principle originated in the acceleration and deceleration of the motor-propeller groups. The proposed controller is composed of two nested loops that are utilized to achieve stabilization and precise trajectory tracking tasks for the controlled position of the generalized coordinates of the TRMS. The nonlinear internal loop is used to control the electrical dynamics of the platform, and the nonlinear external loop allows the platform to be perfectly stabilized and positioned in space. Finally, we illustrate the theoretical control developments with a set of experiments in order to verify the effectiveness of the proposed nonlinear decentralized feedback controller, in which a comparative study with other controllers is performed, illustrating the excellent performance of the proposed robust decentralized control scheme in both stabilization and trajectory tracking tasks. PMID:27472338

Robust Decentralized Nonlinear Control for a Twin Rotor MIMO System.

PubMed

Belmonte, Lidia María; Morales, Rafael; Fernández-Caballero, Antonio; Somolinos, José Andrés

2016-07-27

This article presents the design of a novel decentralized nonlinear multivariate control scheme for an underactuated, nonlinear and multivariate laboratory helicopter denominated the twin rotor MIMO system (TRMS). The TRMS is characterized by a coupling effect between rotor dynamics and the body of the model, which is due to the action-reaction principle originated in the acceleration and deceleration of the motor-propeller groups. The proposed controller is composed of two nested loops that are utilized to achieve stabilization and precise trajectory tracking tasks for the controlled position of the generalized coordinates of the TRMS. The nonlinear internal loop is used to control the electrical dynamics of the platform, and the nonlinear external loop allows the platform to be perfectly stabilized and positioned in space. Finally, we illustrate the theoretical control developments with a set of experiments in order to verify the effectiveness of the proposed nonlinear decentralized feedback controller, in which a comparative study with other controllers is performed, illustrating the excellent performance of the proposed robust decentralized control scheme in both stabilization and trajectory tracking tasks.

A comparative study of multivariable robustness analysis methods as applied to integrated flight and propulsion control

NASA Technical Reports Server (NTRS)

Schierman, John D.; Lovell, T. A.; Schmidt, David K.

1993-01-01

Three multivariable robustness analysis methods are compared and contrasted. The focus of the analysis is on system stability and performance robustness to uncertainty in the coupling dynamics between two interacting subsystems. Of particular interest is interacting airframe and engine subsystems, and an example airframe/engine vehicle configuration is utilized in the demonstration of these approaches. The singular value (SV) and structured singular value (SSV) analysis methods are compared to a method especially well suited for analysis of robustness to uncertainties in subsystem interactions. This approach is referred to here as the interacting subsystem (IS) analysis method. This method has been used previously to analyze airframe/engine systems, emphasizing the study of stability robustness. However, performance robustness is also investigated here, and a new measure of allowable uncertainty for acceptable performance robustness is introduced. The IS methodology does not require plant uncertainty models to measure the robustness of the system, and is shown to yield valuable information regarding the effects of subsystem interactions. In contrast, the SV and SSV methods allow for the evaluation of the robustness of the system to particular models of uncertainty, and do not directly indicate how the airframe (engine) subsystem interacts with the engine (airframe) subsystem.

Development of a robust framework for controlling high performance turbofan engines

NASA Astrophysics Data System (ADS)

Miklosovic, Robert

This research involves the development of a robust framework for controlling complex and uncertain multivariable systems. Where mathematical modeling is often tedious or inaccurate, the new method uses an extended state observer (ESO) to estimate and cancel dynamic information in real time and dynamically decouple the system. As a result, controller design and tuning become transparent as the number of required model parameters is reduced. Much research has been devoted towards the application of modern multivariable control techniques on aircraft engines. However, few, if any, have been implemented on an operational aircraft, partially due to the difficulty in tuning the controller for satisfactory performance. The new technique is applied to a modern two-spool, high-pressure ratio, low-bypass turbofan with mixed-flow afterburning. A realistic Modular Aero-Propulsion System Simulation (MAPSS) package, developed by NASA, is used to demonstrate the new design process and compare its performance with that of a supplied nominal controller. This approach is expected to reduce gain scheduling over the full operating envelope of the engine and allow a controller to be tuned for engine-to-engine variations.

Robust Control of Multivariable and Large Scale Systems.

DTIC Science & Technology

1986-03-14

AD-A175 $5B ROBUST CONTROL OF MULTIVRRIALE AND LARG SCALE SYSTEMS V2 R75 (U) HONEYWELL SYSTEMS AND RESEARCH CENTER MINNEAPOLIS MN J C DOYLE ET AL...ONIJQ 86 R alFS ja ,.AMIECFOEPF:ORMING ORGANIZATION So OFFICE SYMBOL 7a NAME OF MONITORING ORGANIZATI ON jonevwell Systems & Research If 4000c" Air...Force Office of Scientific Research .~ C :AE S C.rv. Stare arma ZIP Code) 7C ADDRESS (Crty. Stare. am ZIP Code, *3660 Marshall Street NE Building 410

Control design for robust stability in linear regulators: Application to aerospace flight control

NASA Technical Reports Server (NTRS)

Yedavalli, R. K.

1986-01-01

Time domain stability robustness analysis and design for linear multivariable uncertain systems with bounded uncertainties is the central theme of the research. After reviewing the recently developed upper bounds on the linear elemental (structured), time varying perturbation of an asymptotically stable linear time invariant regulator, it is shown that it is possible to further improve these bounds by employing state transformations. Then introducing a quantitative measure called the stability robustness index, a state feedback conrol design algorithm is presented for a general linear regulator problem and then specialized to the case of modal systems as well as matched systems. The extension of the algorithm to stochastic systems with Kalman filter as the state estimator is presented. Finally an algorithm for robust dynamic compensator design is presented using Parameter Optimization (PO) procedure. Applications in a aircraft control and flexible structure control are presented along with a comparison with other existing methods.

A reliable algorithm for optimal control synthesis

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1992-01-01

In recent years, powerful design tools for linear time-invariant multivariable control systems have been developed based on direct parameter optimization. In this report, an algorithm for reliable optimal control synthesis using parameter optimization is presented. Specifically, a robust numerical algorithm is developed for the evaluation of the H(sup 2)-like cost functional and its gradients with respect to the controller design parameters. The method is specifically designed to handle defective degenerate systems and is based on the well-known Pade series approximation of the matrix exponential. Numerical test problems in control synthesis for simple mechanical systems and for a flexible structure with densely packed modes illustrate positively the reliability of this method when compared to a method based on diagonalization. Several types of cost functions have been considered: a cost function for robust control consisting of a linear combination of quadratic objectives for deterministic and random disturbances, and one representing an upper bound on the quadratic objective for worst case initial conditions. Finally, a framework for multivariable control synthesis has been developed combining the concept of closed-loop transfer recovery with numerical parameter optimization. The procedure enables designers to synthesize not only observer-based controllers but also controllers of arbitrary order and structure. Numerical design solutions rely heavily on the robust algorithm due to the high order of the synthesis model and the presence of near-overlapping modes. The design approach is successfully applied to the design of a high-bandwidth control system for a rotorcraft.

Overview of computational control research at UT Austin

NASA Technical Reports Server (NTRS)

Bong, Wie

1989-01-01

An overview of current research activities at UT Austin is presented to discuss certain technical issues in the following areas: (1) Computer-Aided Nonlinear Control Design: In this project, the describing function method is employed for the nonlinear control analysis and design of a flexible spacecraft equipped with pulse modulated reaction jets. INCA program has been enhanced to allow the numerical calculation of describing functions as well as the nonlinear limit cycle analysis capability in the frequency domain; (2) Robust Linear Quadratic Gaussian (LQG) Compensator Synthesis: Robust control design techniques and software tools are developed for flexible space structures with parameter uncertainty. In particular, an interactive, robust multivariable control design capability is being developed for INCA program; and (3) LQR-Based Autonomous Control System for the Space Station: In this project, real time implementation of LQR-based autonomous control system is investigated for the space station with time-varying inertias and with significant multibody dynamic interactions.

Multivariable Robust Control of a Simulated Hybrid Solid Oxide Fuel Cell Gas Turbine Plant

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

Tsai, Alex; Banta, Larry; Tucker, David

2010-08-01

This work presents a systematic approach to the multivariable robust control of a hybrid fuel cell gas turbine plant. The hybrid configuration under investigation built by the National Energy Technology Laboratory comprises a physical simulation of a 300kW fuel cell coupled to a 120kW auxiliary power unit single spool gas turbine. The public facility provides for the testing and simulation of different fuel cell models that in turn help identify the key difficulties encountered in the transient operation of such systems. An empirical model of the built facility comprising a simulated fuel cell cathode volume and balance of plant componentsmore » is derived via frequency response data. Through the modulation of various airflow bypass valves within the hybrid configuration, Bode plots are used to derive key input/output interactions in transfer function format. A multivariate system is then built from individual transfer functions, creating a matrix that serves as the nominal plant in an H{sub {infinity}} robust control algorithm. The controller’s main objective is to track and maintain hybrid operational constraints in the fuel cell’s cathode airflow, and the turbo machinery states of temperature and speed, under transient disturbances. This algorithm is then tested on a Simulink/MatLab platform for various perturbations of load and fuel cell heat effluence. As a complementary tool to the aforementioned empirical plant, a nonlinear analytical model faithful to the existing process and instrumentation arrangement is evaluated and designed in the Simulink environment. This parallel task intends to serve as a building block to scalable hybrid configurations that might require a more detailed nonlinear representation for a wide variety of controller schemes and hardware implementations.« less

A Robust Bayesian Approach for Structural Equation Models with Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum; Xia, Ye-Mao

2008-01-01

In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear…

Comparative Robustness of Recent Methods for Analyzing Multivariate Repeated Measures Designs

ERIC Educational Resources Information Center

Seco, Guillermo Vallejo; Gras, Jaime Arnau; Garcia, Manuel Ato

2007-01-01

This study evaluated the robustness of two recent methods for analyzing multivariate repeated measures when the assumptions of covariance homogeneity and multivariate normality are violated. Specifically, the authors' work compares the performance of the modified Brown-Forsythe (MBF) procedure and the mixed-model procedure adjusted by the…

Vibration Attenuation of the NASA Langley Evolutionary Structure Experiment Using H(infinity) and Structured Singular Value (mu) Robust Multivariable Control Techniques

NASA Technical Reports Server (NTRS)

Balas, Gary J.

1996-01-01

This final report summarizes the research results under NASA Contract NAG-1-1254 from May, 1991 - April, 1995. The main contribution of this research are in the areas of control of flexible structures, model validation, optimal control analysis and synthesis techniques, and use of shape memory alloys for structural damping.

Fixed order dynamic compensation for multivariable linear systems

NASA Technical Reports Server (NTRS)

Kramer, F. S.; Calise, A. J.

1986-01-01

This paper considers the design of fixed order dynamic compensators for multivariable time invariant linear systems, minimizing a linear quadratic performance cost functional. Attention is given to robustness issues in terms of multivariable frequency domain specifications. An output feedback formulation is adopted by suitably augmenting the system description to include the compensator states. Either a controller or observer canonical form is imposed on the compensator description to reduce the number of free parameters to its minimal number. The internal structure of the compensator is prespecified by assigning a set of ascending feedback invariant indices, thus forming a Brunovsky structure for the nominal compensator.

Sliding Mode Control of a Thermal Mixing Process

NASA Technical Reports Server (NTRS)

Richter, Hanz; Figueroa, Fernando

2004-01-01

In this paper we consider the robust control of a thermal mixer using multivariable Sliding Mode Control (SMC). The mixer consists of a mixing chamber, hot and cold fluid valves, and an exit valve. The commanded positions of the three valves are the available control inputs, while the controlled variables are total mass flow rate, chamber pressure and the density of the mixture inside the chamber. Unsteady thermodynamics and linear valve models are used in deriving a 5th order nonlinear system with three inputs and three outputs, An SMC controller is designed to achieve robust output tracking in the presence of unknown energy losses between the chamber and the environment. The usefulness of the technique is illustrated with a simulation.

On the Numerical Formulation of Parametric Linear Fractional Transformation (LFT) Uncertainty Models for Multivariate Matrix Polynomial Problems

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.

1998-01-01

Robust control system analysis and design is based on an uncertainty description, called a linear fractional transformation (LFT), which separates the uncertain (or varying) part of the system from the nominal system. These models are also useful in the design of gain-scheduled control systems based on Linear Parameter Varying (LPV) methods. Low-order LFT models are difficult to form for problems involving nonlinear parameter variations. This paper presents a numerical computational method for constructing and LFT model for a given LPV model. The method is developed for multivariate polynomial problems, and uses simple matrix computations to obtain an exact low-order LFT representation of the given LPV system without the use of model reduction. Although the method is developed for multivariate polynomial problems, multivariate rational problems can also be solved using this method by reformulating the rational problem into a polynomial form.

Bellman Continuum (3rd) International Workshop (13-14 June 1988)

DTIC Science & Technology

1988-06-01

Modelling Uncertain Problem ................. 53 David Bensoussan ,---,>Asymptotic Linearization of Uncertain Multivariable Systems by Sliding Modes...K. Ghosh .-. Robust Model Tracking for a Class of Singularly Perturbed Nonlinear Systems via Composite Control ....... 93 F. Garofalo and L. Glielmo...MODELISATION ET COMMANDE EN ECONOMIE MODELS AND CONTROL POLICIES IN ECONOMICS Qualitative Differential Games : A Viability Approach ............. 117

Homotopy Algorithm for Fixed Order Mixed H2/H(infinity) Design

NASA Technical Reports Server (NTRS)

Whorton, Mark; Buschek, Harald; Calise, Anthony J.

1996-01-01

Recent developments in the field of robust multivariable control have merged the theories of H-infinity and H-2 control. This mixed H-2/H-infinity compensator formulation allows design for nominal performance by H-2 norm minimization while guaranteeing robust stability to unstructured uncertainties by constraining the H-infinity norm. A key difficulty associated with mixed H-2/H-infinity compensation is compensator synthesis. A homotopy algorithm is presented for synthesis of fixed order mixed H-2/H-infinity compensators. Numerical results are presented for a four disk flexible structure to evaluate the efficiency of the algorithm.

Return Difference Feedback Design for Robust Uncertainty Tolerance in Stochastic Multivariable Control Systems.

DTIC Science & Technology

1984-07-01

34robustness" analysis for multiloop feedback systems. Reference [55] describes a simple method based on the Perron - Frobenius Theory of non-negative...Viewpoint, " Operator Theory : Advances and Applications, 12, pp. 277-302, 1984. - E. A. Jonckheere, "New Bound on the Sensitivity -- of the Solution of...Reidel, Dordrecht, Holland, 1984. M. G. Safonov, "Comments on Singular Value Theory in Uncertain Feedback Systems, " to appear IEEE Trans. on Automatic

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

PubMed

Wang, Minlin; Ren, Xuemei; Chen, Qiang

2018-01-01

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

Robust Economic Control Decision Method of Uncertain System on Urban Domestic Water Supply.

PubMed

Li, Kebai; Ma, Tianyi; Wei, Guo

2018-03-31

As China quickly urbanizes, urban domestic water generally presents the circumstances of both rising tendency and seasonal cycle fluctuation. A robust economic control decision method for dynamic uncertain systems is proposed in this paper. It is developed based on the internal model principle and pole allocation method, and it is applied to an urban domestic water supply system with rising tendency and seasonal cycle fluctuation. To achieve this goal, first a multiplicative model is used to describe the urban domestic water demand. Then, a capital stock and a labor stock are selected as the state vector, and the investment and labor are designed as the control vector. Next, the compensator subsystem is devised in light of the internal model principle. Finally, by using the state feedback control strategy and pole allocation method, the multivariable robust economic control decision method is implemented. The implementation with this model can accomplish the urban domestic water supply control goal, with the robustness for the variation of parameters. The methodology presented in this study may be applied to the water management system in other parts of the world, provided all data used in this study are available. The robust control decision method in this paper is also applicable to deal with tracking control problems as well as stabilization control problems of other general dynamic uncertain systems.

Robust Economic Control Decision Method of Uncertain System on Urban Domestic Water Supply

PubMed Central

Li, Kebai; Ma, Tianyi; Wei, Guo

2018-01-01

As China quickly urbanizes, urban domestic water generally presents the circumstances of both rising tendency and seasonal cycle fluctuation. A robust economic control decision method for dynamic uncertain systems is proposed in this paper. It is developed based on the internal model principle and pole allocation method, and it is applied to an urban domestic water supply system with rising tendency and seasonal cycle fluctuation. To achieve this goal, first a multiplicative model is used to describe the urban domestic water demand. Then, a capital stock and a labor stock are selected as the state vector, and the investment and labor are designed as the control vector. Next, the compensator subsystem is devised in light of the internal model principle. Finally, by using the state feedback control strategy and pole allocation method, the multivariable robust economic control decision method is implemented. The implementation with this model can accomplish the urban domestic water supply control goal, with the robustness for the variation of parameters. The methodology presented in this study may be applied to the water management system in other parts of the world, provided all data used in this study are available. The robust control decision method in this paper is also applicable to deal with tracking control problems as well as stabilization control problems of other general dynamic uncertain systems. PMID:29614749

A Comparison of Multivariable Control Design Techniques for a Turbofan Engine Control

NASA Technical Reports Server (NTRS)

Garg, Sanjay; Watts, Stephen R.

1995-01-01

This paper compares two previously published design procedures for two different multivariable control design techniques for application to a linear engine model of a jet engine. The two multivariable control design techniques compared were the Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) and the H-Infinity synthesis. The two control design techniques were used with specific previously published design procedures to synthesize controls which would provide equivalent closed loop frequency response for the primary control loops while assuring adequate loop decoupling. The resulting controllers were then reduced in order to minimize the programming and data storage requirements for a typical implementation. The reduced order linear controllers designed by each method were combined with the linear model of an advanced turbofan engine and the system performance was evaluated for the continuous linear system. Included in the performance analysis are the resulting frequency and transient responses as well as actuator usage and rate capability for each design method. The controls were also analyzed for robustness with respect to structured uncertainties in the unmodeled system dynamics. The two controls were then compared for performance capability and hardware implementation issues.

Decoupling control of vehicle chassis system based on neural network inverse system

NASA Astrophysics Data System (ADS)

Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke

2018-06-01

Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.

An approach to multivariable control of manipulators

NASA Technical Reports Server (NTRS)

Seraji, H.

1987-01-01

The paper presents simple schemes for multivariable control of multiple-joint robot manipulators in joint and Cartesian coordinates. The joint control scheme consists of two independent multivariable feedforward and feedback controllers. The feedforward controller is the minimal inverse of the linearized model of robot dynamics and contains only proportional-double-derivative (PD2) terms - implying feedforward from the desired position, velocity and acceleration. This controller ensures that the manipulator joint angles track any reference trajectories. The feedback controller is of proportional-integral-derivative (PID) type and is designed to achieve pole placement. This controller reduces any initial tracking error to zero as desired and also ensures that robust steady-state tracking of step-plus-exponential trajectories is achieved by the joint angles. Simple and explicit expressions of computation of the feedforward and feedback gains are obtained based on the linearized model of robot dynamics. This leads to computationally efficient schemes for either on-line gain computation or off-line gain scheduling to account for variations in the linearized robot model due to changes in the operating point. The joint control scheme is extended to direct control of the end-effector motion in Cartesian space. Simulation results are given for illustration.

Introduction to Robust Multivariable Control

DTIC Science & Technology

1986-02-01

AX DAVIS, Assistant for Research and Technology Flight Control Division Flight Dynamics Laboratory "If your address has changed, if you wish to be...particular chapter is given at the end of each chapter. It is needless to say that there are numerour other - publications in this area of controls research ...tco in this Area of research comprehensively in a single report. Finally, even though most of the material in this report comes from publications of W

Return Difference Feedback Design for Robust Uncertainty Tolerance in Stochastic Multivariable Control Systems.

DTIC Science & Technology

1982-11-01

D- R136 495 RETURN DIFFERENCE FEEDBACK DESIGN FOR ROBUSTj/ UNCERTAINTY TOLERANCE IN STO..(U) UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES DEPT OF...State and ZIP Code) 7. b6 ADORESS (City. Staft and ZIP Code) Department of Electrical Engineering -’M Directorate of Mathematical & Information Systems ...13. SUBJECT TERMS Continur on rverse ineeesaty and identify by block nmber) FIELD GROUP SUE. GR. Systems theory; control; feedback; automatic control

Progress in multirate digital control system design

NASA Technical Reports Server (NTRS)

Berg, Martin C.; Mason, Gregory S.

1991-01-01

A new methodology for multirate sampled-data control design based on a new generalized control law structure, two new parameter-optimization-based control law synthesis methods, and a new singular-value-based robustness analysis method are described. The control law structure can represent multirate sampled-data control laws of arbitrary structure and dynamic order, with arbitrarily prescribed sampling rates for all sensors and update rates for all processor states and actuators. The two control law synthesis methods employ numerical optimization to determine values for the control law parameters. The robustness analysis method is based on the multivariable Nyquist criterion applied to the loop transfer function for the sampling period equal to the period of repetition of the system's complete sampling/update schedule. The complete methodology is demonstrated by application to the design of a combination yaw damper and modal suppression system for a commercial aircraft.

Application of the LQG/LTR technique to robust controller synthesis for a large flexible space antenna

NASA Technical Reports Server (NTRS)

Joshi, S. M.; Armstrong, E. S.; Sundararajan, N.

1986-01-01

The problem of synthesizing a robust controller is considered for a large, flexible space-based antenna by using the linear-quadratic-Gaussian (LQG)/loop transfer recovery (LTR) method. The study is based on a finite-element model of the 122-m hoop/column antenna, which consists of three rigid-body rotational modes and the first 10 elastic modes. A robust compensator design for achieving the required performance bandwidth in the presence of modeling uncertainties is obtained using the LQG/LTR method for loop-shaping in the frequency domain. Different sensor actuator locations are analyzed in terms of the pole/zero locations of the multivariable systems and possible best locations are indicated. The computations are performed by using the LQG design package ORACLS augmented with frequency domain singular value analysis software.

Robust detection, isolation and accommodation for sensor failures

NASA Technical Reports Server (NTRS)

Emami-Naeini, A.; Akhter, M. M.; Rock, S. M.

1986-01-01

The objective is to extend the recent advances in robust control system design of multivariable systems to sensor failure detection, isolation, and accommodation (DIA), and estimator design. This effort provides analysis tools to quantify the trade-off between performance robustness and DIA sensitivity, which are to be used to achieve higher levels of performance robustness for given levels of DIA sensitivity. An innovations-based DIA scheme is used. Estimators, which depend upon a model of the process and process inputs and outputs, are used to generate these innovations. Thresholds used to determine failure detection are computed based on bounds on modeling errors, noise properties, and the class of failures. The applicability of the newly developed tools are demonstrated on a multivariable aircraft turbojet engine example. A new concept call the threshold selector was developed. It represents a significant and innovative tool for the analysis and synthesis of DiA algorithms. The estimators were made robust by introduction of an internal model and by frequency shaping. The internal mode provides asymptotically unbiased filter estimates.The incorporation of frequency shaping of the Linear Quadratic Gaussian cost functional modifies the estimator design to make it suitable for sensor failure DIA. The results are compared with previous studies which used thresholds that were selcted empirically. Comparison of these two techniques on a nonlinear dynamic engine simulation shows improved performance of the new method compared to previous techniques

Robustness analysis of an air heating plant and control law by using polynomial chaos

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

Colón, Diego; Ferreira, Murillo A. S.; Bueno, Átila M.

2014-12-10

This paper presents a robustness analysis of an air heating plant with a multivariable closed-loop control law by using the polynomial chaos methodology (MPC). The plant consists of a PVC tube with a fan in the air input (that forces the air through the tube) and a mass flux sensor in the output. A heating resistance warms the air as it flows inside the tube, and a thermo-couple sensor measures the air temperature. The plant has thus two inputs (the fan's rotation intensity and heat generated by the resistance, both measured in percent of the maximum value) and two outputsmore » (air temperature and air mass flux, also in percent of the maximal value). The mathematical model is obtained by System Identification techniques. The mass flux sensor, which is nonlinear, is linearized and the delays in the transfer functions are properly approximated by non-minimum phase transfer functions. The resulting model is transformed to a state-space model, which is used for control design purposes. The multivariable robust control design techniques used is the LQG/LTR, and the controllers are validated in simulation software and in the real plant. Finally, the MPC is applied by considering some of the system's parameters as random variables (one at a time, and the system's stochastic differential equations are solved by expanding the solution (a stochastic process) in an orthogonal basis of polynomial functions of the basic random variables. This method transforms the stochastic equations in a set of deterministic differential equations, which can be solved by traditional numerical methods (That is the MPC). Statistical data for the system (like expected values and variances) are then calculated. The effects of randomness in the parameters are evaluated in the open-loop and closed-loop pole's positions.« less

Linear quadratic servo control of a reusable rocket engine

NASA Technical Reports Server (NTRS)

Musgrave, Jeffrey L.

1991-01-01

The paper deals with the development of a design method for a servo component in the frequency domain using singular values and its application to a reusable rocket engine. A general methodology used to design a class of linear multivariable controllers for intelligent control systems is presented. Focus is placed on performance and robustness characteristics, and an estimator design performed in the framework of the Kalman-filter formalism with emphasis on using a sensor set different from the commanded values is discussed. It is noted that loop transfer recovery modifies the nominal plant noise intensities in order to obtain the desired degree of robustness to uncertainty reflected at the plant input. Simulation results demonstrating the performance of the linear design on a nonlinear engine model over all power levels during mainstage operation are discussed.

The significance and robustness of a plasma free amino acid (PFAA) profile-based multiplex function for detecting lung cancer

PubMed Central

2013-01-01

Background We have recently reported on the changes in plasma free amino acid (PFAA) profiles in lung cancer patients and the efficacy of a PFAA-based, multivariate discrimination index for the early detection of lung cancer. In this study, we aimed to verify the usefulness and robustness of PFAA profiling for detecting lung cancer using new test samples. Methods Plasma samples were collected from 171 lung cancer patients and 3849 controls without apparent cancer. PFAA levels were measured by high-performance liquid chromatography (HPLC)–electrospray ionization (ESI)–mass spectrometry (MS). Results High reproducibility was observed for both the change in the PFAA profiles in the lung cancer patients and the discriminating performance for lung cancer patients compared to previously reported results. Furthermore, multivariate discriminating functions obtained in previous studies clearly distinguished the lung cancer patients from the controls based on the area under the receiver-operator characteristics curve (AUC of ROC = 0.731 ~ 0.806), strongly suggesting the robustness of the methodology for clinical use. Moreover, the results suggested that the combinatorial use of this classifier and tumor markers improves the clinical performance of tumor markers. Conclusions These findings suggest that PFAA profiling, which involves a relatively simple plasma assay and imposes a low physical burden on subjects, has great potential for improving early detection of lung cancer. PMID:23409863

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

NASA Technical Reports Server (NTRS)

Suh, Peter M.; Chin, Alexander W.; Marvis, Dimitri N.

2014-01-01

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

Robust Modal Filtering and Control of the X-56A Model with Simulated Fiber Optic Sensor Failures

NASA Technical Reports Server (NTRS)

Suh, Peter M.; Chin, Alexander W.; Mavris, Dimitri N.

2016-01-01

The X-56A aircraft is a remotely-piloted aircraft with flutter modes intentionally designed into the flight envelope. The X-56A program must demonstrate flight control while suppressing all unstable modes. A previous X-56A model study demonstrated a distributed-sensing-based active shape and active flutter suppression controller. The controller relies on an estimator which is sensitive to bias. This estimator is improved herein, and a real-time robust estimator is derived and demonstrated on 1530 fiber optic sensors. It is shown in simulation that the estimator can simultaneously reject 230 worst-case fiber optic sensor failures automatically. These sensor failures include locations with high leverage (or importance). To reduce the impact of leverage outliers, concentration based on a Mahalanobis trim criterion is introduced. A redescending M-estimator with Tukey bisquare weights is used to improve location and dispersion estimates within each concentration step in the presence of asymmetry (or leverage). A dynamic simulation is used to compare the concentrated robust estimator to a state-of-the-art real-time robust multivariate estimator. The estimators support a previously-derived mu-optimal shape controller. It is found that during the failure scenario, the concentrated modal estimator keeps the system stable.

The Galileo scan platform pointing control system - A modern control theoretic viewpoint

NASA Technical Reports Server (NTRS)

Sevaston, G. E.; Macala, G. A.; Man, G. K.

1985-01-01

The current Galileo scan platform pointing control system (SPPCS) is described, and ways in which modern control concepts could serve to enhance it are considered. Of particular interest are: the multi-variable design model and overall control system architecture, command input filtering, feedback compensator and command input design, stability robustness constraint for both continuous time control systems and for sampled data control systems, and digital implementation of the control system. The proposed approach leads to the design of a system that is similar to current Galileo SPPCS configuration, but promises to be more systematic.

Performance of Modified Test Statistics in Covariance and Correlation Structure Analysis under Conditions of Multivariate Nonnormality.

ERIC Educational Resources Information Center

Fouladi, Rachel T.

2000-01-01

Provides an overview of standard and modified normal theory and asymptotically distribution-free covariance and correlation structure analysis techniques and details Monte Carlo simulation results on Type I and Type II error control. Demonstrates through the simulation that robustness and nonrobustness of structure analysis techniques vary as a…

Design and test of three active flutter suppression controllers

NASA Technical Reports Server (NTRS)

Christhilf, David M.; Waszak, Martin R.; Adams, William M.; Srinathkumar, S.; Mukhopadhyay, Vivek

1991-01-01

Three flutter suppression control law design techniques are presented. Each uses multiple control surfaces and/or sensors. The first uses linear combinations of several accelerometer signals together with dynamic compensation to synthesize the modal rate of the critical mode for feedback to distributed control surfaces. The second uses traditional tools (pole/zero loci and Nyquist diagrams) to develop a good understanding of the flutter mechanism and produce a controller with minimal complexity and good robustness to plant uncertainty. The third starts with a minimum energy Linear Quadratic Gaussian controller, applies controller order reduction, and then modifies weight and noise covariance matrices to improve multi-variable robustness. The resulting designs were implemented digitally and tested subsonically on the Active Flexible Wing (AFW) wind tunnel model. Test results presented here include plant characteristics, maximum attained closed-loop dynamic pressure, and Root Mean Square control surface activity. A key result is that simultaneous symmetric and antisymmetric flutter suppression was achieved by the second control law, with a 24 percent increase in attainable dynamic pressure.

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

PubMed

Ghabraei, Soheil; Moradi, Hamed; Vossoughi, Gholamreza

2015-09-01

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

Power independent EMG based gesture recognition for robotics.

PubMed

Li, Ling; Looney, David; Park, Cheolsoo; Rehman, Naveed U; Mandic, Danilo P

2011-01-01

A novel method for detecting muscle contraction is presented. This method is further developed for identifying four different gestures to facilitate a hand gesture controlled robot system. It is achieved based on surface Electromyograph (EMG) measurements of groups of arm muscles. The cross-information is preserved through a simultaneous processing of EMG channels using a recent multivariate extension of Empirical Mode Decomposition (EMD). Next, phase synchrony measures are employed to make the system robust to different power levels due to electrode placements and impedances. The multiple pairwise muscle synchronies are used as features of a discrete gesture space comprising four gestures (flexion, extension, pronation, supination). Simulations on real-time robot control illustrate the enhanced accuracy and robustness of the proposed methodology.

Linear quadratic servo control of a reusable rocket engine

NASA Technical Reports Server (NTRS)

Musgrave, Jeffrey L.

1991-01-01

A design method for a servo compensator is developed in the frequency domain using singular values. The method is applied to a reusable rocket engine. An intelligent control system for reusable rocket engines was proposed which includes a diagnostic system, a control system, and an intelligent coordinator which determines engine control strategies based on the identified failure modes. The method provides a means of generating various linear multivariable controllers capable of meeting performance and robustness specifications and accommodating failure modes identified by the diagnostic system. Command following with set point control is necessary for engine operation. A Kalman filter reconstructs the state while loop transfer recovery recovers the required degree of robustness while maintaining satisfactory rejection of sensor noise from the command error. The approach is applied to the design of a controller for a rocket engine satisfying performance constraints in the frequency domain. Simulation results demonstrate the performance of the linear design on a nonlinear engine model over all power levels during mainstage operation.

An approach to design controllers for MIMO fractional-order plants based on parameter optimization algorithm.

PubMed

Xue, Dingyü; Li, Tingxue

2017-04-27

The parameter optimization method for multivariable systems is extended to the controller design problems for multiple input multiple output (MIMO) square fractional-order plants. The algorithm can be applied to search for the optimal parameters of integer-order controllers for fractional-order plants with or without time delays. Two examples are given to present the controller design procedures for MIMO fractional-order systems. Simulation studies show that the integer-order controllers designed are robust to plant gain variations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Robust control of integrated motor-transmission powertrain system over controller area network for automotive applications

NASA Astrophysics Data System (ADS)

Zhu, Xiaoyuan; Zhang, Hui; Cao, Dongpu; Fang, Zongde

2015-06-01

Integrated motor-transmission (IMT) powertrain system with directly coupled motor and gearbox is a good choice for electric commercial vehicles (e.g., pure electric buses) due to its potential in motor size reduction and energy efficiency improvement. However, the controller design for powertrain oscillation damping becomes challenging due to the elimination of damping components. On the other hand, as controller area network (CAN) is commonly adopted in modern vehicle system, the network-induced time-varying delays that caused by bandwidth limitation will further lead to powertrain vibration or even destabilize the powertrain control system. Therefore, in this paper, a robust energy-to-peak controller is proposed for the IMT powertrain system to address the oscillation damping problem and also attenuate the external disturbance. The control law adopted here is based on a multivariable PI control, which ensures the applicability and performance of the proposed controller in engineering practice. With the linearized delay uncertainties characterized by polytopic inclusions, a delay-free closed-loop augmented system is established for the IMT powertrain system under discrete-time framework. The proposed controller design problem is then converted to a static output feedback (SOF) controller design problem where the feedback control gains are obtained by solving a set of linear matrix inequalities (LMIs). The effectiveness as well as robustness of the proposed controller is demonstrated by comparing its performance against that of a conventional PI controller.

Robust predictive control with optimal load tracking for critical applications. Final report

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

Tse, J.; Bentsman, J.; Miller, N.

1994-09-01

This report derives a multi-input multi-output (MIMO) version of a two-degree-of-freedom receding-horizon control law based on mixed H{sub 2}/H{infinity} minimization. First, the integrand in the frequency domain representation of the MIMO performance criterion is decomposed into disturbance and reference spectra. Then the controller is derived which minimizes the peak of the disturbance spectrum and the integral of the reference spectrum on the unit circle. The resulting two-degree-of-freedom MIMO control strategy, referred to as the minimax predictive multivariable control (MPC), is shown to have worst-case-disturbance-rejection and robust-stability properties superior to those of purely H{sub 2}-optimal controllers, such as Generalized Predictive Controlmore » (GPC), for identical horizons. An attractive feature of the receding horizon structure of MPC is that it can, in ways similar to GPC, directly incorporate input constraints and pre-programmed reference inputs, which are nontrivial tasks in the standard H{infinity} design.« less

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

NASA Astrophysics Data System (ADS)

Veeramani, Sudha; Wereley, Norman M.

1996-05-01

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

Modal control theory and application to aircraft lateral handling qualities design

NASA Technical Reports Server (NTRS)

Srinathkumar, S.

1978-01-01

A multivariable synthesis procedure based on eigenvalue/eigenvector assignment is reviewed and is employed to develop a systematic design procedure to meet the lateral handling qualities design objectives of a fighter aircraft over a wide range of flight conditions. The closed loop modal characterization developed provides significant insight into the design process and plays a pivotal role in the synthesis of robust feedback systems. The simplicity of the synthesis algorithm yields an efficient computer aided interactive design tool for flight control system synthesis.

H(2)- and H(infinity)-design tools for linear time-invariant systems

NASA Technical Reports Server (NTRS)

Ly, Uy-Loi

1989-01-01

Recent advances in optimal control have brought design techniques based on optimization of H(2) and H(infinity) norm criteria, closer to be attractive alternatives to single-loop design methods for linear time-variant systems. Significant steps forward in this technology are the deeper understanding of performance and robustness issues of these design procedures and means to perform design trade-offs. However acceptance of the technology is hindered by the lack of convenient design tools to exercise these powerful multivariable techniques, while still allowing single-loop design formulation. Presented is a unique computer tool for designing arbitrary low-order linear time-invarient controllers than encompasses both performance and robustness issues via the familiar H(2) and H(infinity) norm optimization. Application to disturbance rejection design for a commercial transport is demonstrated.

Nonlinear multivariable design by total synthesis. [of gas turbine engine control systems

NASA Technical Reports Server (NTRS)

Sain, M. K.; Peczkowski, J. L.

1982-01-01

The Nominal Design Problem (NDP) is extended to nonlinear cases, and a new case study of robust feedback synthesis for gas turbine control design is presented. The discussion of NDP extends and builds on earlier Total Synthesis Problem theory and ideas. Some mathematical preliminaries are given in which a bijection from a set S onto a set T is considered, with T admitting the structure of an F-vector space. NDP is then discussed for a nonlinear plant, and nonlinear nominal design is defined and characterized. The design of local controllers for a turbojet and the scheduling of these controls into a global control are addressed.

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 Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis.

PubMed

Collins, Tony J; Ylanko, Jarkko; Geng, Fei; Andrews, David W

2015-11-01

A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose-response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds.

A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis

PubMed Central

Collins, Tony J.; Ylanko, Jarkko; Geng, Fei

2015-01-01

Abstract A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose–response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds. PMID:26422066

Robustness properties of discrete time regulators, LOG regulators and hybrid systems

NASA Technical Reports Server (NTRS)

Stein, G.; Athans, M.

1979-01-01

Robustness properites of sample-data LQ regulators are derived which show that these regulators have fundamentally inferior uncertainty tolerances when compared to their continuous-time counterparts. Results are also presented in stability theory, multivariable frequency domain analysis, LQG robustness, and mathematical representations of hybrid systems.

Multivariable control of the Space Shuttle remote manipulator system using H2 and H(infinity) optimization. M.S. Thesis - Massachusetts Inst. of Tech.

NASA Technical Reports Server (NTRS)

Prakash, OM, II

1991-01-01

Three linear controllers are desiged to regulate the end effector of the Space Shuttle Remote Manipulator System (SRMS) operating in Position Hold Mode. In this mode of operation, jet firings of the Orbiter can be treated as disturbances while the controller tries to keep the end effector stationary in an orbiter-fixed reference frame. The three design techniques used include: the Linear Quadratic Regulator (LQR), H2 optimization, and H-infinity optimization. The nonlinear SRMS is linearized by modelling the effects of the significant nonlinearities as uncertain parameters. Each regulator design is evaluated for robust stability in light of the parametric uncertanties using both the small gain theorem with an H-infinity norm and the less conservative micro-analysis test. All three regulator designs offer significant improvement over the current system on the nominal plant. Unfortunately, even after dropping performance requirements and designing exclusively for robust stability, robust stability cannot be achieved. The SRMS suffers from lightly damped poles with real parametric uncertainties. Such a system renders the micro-analysis test, which allows for complex peturbations, too conservative.

Robustness of Thirty Meter Telescope primary mirror control

NASA Astrophysics Data System (ADS)

Macmynowski, Douglas G.; Thompson, Peter M.; Shelton, Chris; Roberts, Lewis C., Jr.

2010-07-01

The primary mirror control system for the Thirty Meter Telescope (TMT) maintains the alignment of the 492 segments in the presence of both quasi-static (gravity and thermal) and dynamic disturbances due to unsteady wind loads. The latter results in a desired control bandwidth of 1Hz at high spatial frequencies. The achievable bandwidth is limited by robustness to (i) uncertain telescope structural dynamics (control-structure interaction) and (ii) small perturbations in the ill-conditioned influence matrix that relates segment edge sensor response to actuator commands. Both of these effects are considered herein using models of TMT. The former is explored through multivariable sensitivity analysis on a reduced-order Zernike-basis representation of the structural dynamics. The interaction matrix ("A-matrix") uncertainty has been analyzed theoretically elsewhere, and is examined here for realistic amplitude perturbations due to segment and sensor installation errors, and gravity and thermal induced segment motion. The primary influence of A-matrix uncertainty is on the control of "focusmode"; this is the least observable mode, measurable only through the edge-sensor (gap-dependent) sensitivity to the dihedral angle between segments. Accurately estimating focus-mode will require updating the A-matrix as a function of the measured gap. A-matrix uncertainty also results in a higher gain-margin requirement for focus-mode, and hence the A-matrix and CSI robustness need to be understood simultaneously. Based on the robustness analysis, the desired 1 Hz bandwidth is achievable in the presence of uncertainty for all except the lowest spatial-frequency response patterns of the primary mirror.

A Multivariate Magnitude Robust Control Chart for Mean Shift Detection and Change Point Estimation

DTIC Science & Technology

2007-03-01

data like scratches on a desk to the width of lumber at a mill and intangible data like heart rate and microprocessor switching frequency. These...of historical data . The preferred method involves a series of designed experiments to understand what factors affect the process, and their...outcome. Since the QE is involved in every step of data collection, he/she receives exactly the data required, and possesses in-depth knowledge of the

SU-F-R-51: Radiomics in CT Perfusion Maps of Head and Neck Cancer

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

Nesteruk, M; Riesterer, O; Veit-Haibach, P

2016-06-15

Purpose: The aim of this study was to test the predictive value of radiomics features of CT perfusion (CTP) for tumor control, based on a preselection of radiomics features in a robustness study. Methods: 11 patients with head and neck cancer (HNC) and 11 patients with lung cancer were included in the robustness study to preselect stable radiomics parameters. Data from 36 HNC patients treated with definitive radiochemotherapy (median follow-up 30 months) was used to build a predictive model based on these parameters. All patients underwent pre-treatment CTP. 315 texture parameters were computed for three perfusion maps: blood volume, bloodmore » flow and mean transit time. The variability of texture parameters was tested with respect to non-standardizable perfusion computation factors (noise level and artery contouring) using intraclass correlation coefficients (ICC). The parameter with the highest ICC in the correlated group of parameters (inter-parameter Spearman correlations) was tested for its predictive value. The final model to predict tumor control was built using multivariate Cox regression analysis with backward selection of the variables. For comparison, a predictive model based on tumor volume was created. Results: Ten parameters were found to be stable in both HNC and lung cancer regarding potentially non-standardizable factors after the correction for inter-parameter correlations. In the multivariate backward selection of the variables, blood flow entropy showed a highly significant impact on tumor control (p=0.03) with concordance index (CI) of 0.76. Blood flow entropy was significantly lower in the patient group with controlled tumors at 18 months (p<0.1). The new model showed a higher concordance index compared to the tumor volume model (CI=0.68). Conclusion: The preselection of variables in the robustness study allowed building a predictive radiomics-based model of tumor control in HNC despite a small patient cohort. This model was found to be superior to the volume-based model. The project was supported by the KFSP Tumor Oxygenation of the University of Zurich, by a grant of the Center for Clinical Research, University and University Hospital Zurich and by a research grant from Merck (Schweiz) AG.« less

A methodology for formulating a minimal uncertainty model for robust control system design and analysis

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.; Chang, B.-C.; Fischl, Robert

1989-01-01

In the design and analysis of robust control systems for uncertain plants, the technique of formulating what is termed an M-delta model has become widely accepted and applied in the robust control literature. The M represents the transfer function matrix M(s) of the nominal system, and delta represents an uncertainty matrix acting on M(s). The uncertainty can arise from various sources, such as structured uncertainty from parameter variations or multiple unstructured uncertainties from unmodeled dynamics and other neglected phenomena. In general, delta is a block diagonal matrix, and for real parameter variations the diagonal elements are real. As stated in the literature, this structure can always be formed for any linear interconnection of inputs, outputs, transfer functions, parameter variations, and perturbations. However, very little of the literature addresses methods for obtaining this structure, and none of this literature addresses a general methodology for obtaining a minimal M-delta model for a wide class of uncertainty. Since have a delta matrix of minimum order would improve the efficiency of structured singular value (or multivariable stability margin) computations, a method of obtaining a minimal M-delta model would be useful. A generalized method of obtaining a minimal M-delta structure for systems with real parameter variations is given.

Multivariate factorial analysis to design a robust batch leaching test to assess the volcanic ash geochemical hazard.

PubMed

Ruggieri, Flavia; Gil, Raúl A; Fernandez-Turiel, Jose-Luis; Saavedra, Julio; Gimeno, Domingo; Lobo, Agustin; Martinez, Luis D; Rodriguez-Gonzalez, Alejandro

2012-04-30

A method to obtain robust information on short term leaching behaviour of volcanic ashes has been developed independently on the sample age. A mixed factorial design (MFD) was employed as a multivariate strategy for the evaluation of the effects of selected control factors and their interactions (amount of sample (A), contact time (B), and liquid to solid ratio or L/S (C)) on the leaching process of selected metals (Na, K, Mg, Ca, Si, Al, V, Mn, Fe, and Co) and anions (Cl(-) and SO(4)(2-)). Box plots of the data acquired were used to evaluate the reproducibility achieved at different experimental conditions. Both the amount of sample (A) and leaching time (B) had a significant effect on the element stripping whereas the L/S ratio influenced only few elements. The lowest dispersion values have been observed when 1.0 g was leached with an L/S ratio equal to 10, shaking during 4 h. The entire method is completed within few hours, and it is simple, feasible and reliable in laboratory conditions. Copyright © 2012 Elsevier B.V. All rights reserved.

Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.

PubMed

Grassi, Silvia; Amigo, José Manuel; Lyndgaard, Christian Bøge; Foschino, Roberto; Casiraghi, Ernestina

2014-07-15

This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer. Copyright © 2014 Elsevier Ltd. All rights reserved.

Adaptive control of large space structures using recursive lattice filters

NASA Technical Reports Server (NTRS)

Sundararajan, N.; Goglia, G. L.

1985-01-01

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

Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models

PubMed Central

Coelho, Antonio Augusto Rodrigues

2016-01-01

This paper introduces the Fuzzy Logic Hypercube Interpolator (FLHI) and demonstrates applications in control of multiple-input single-output (MISO) and multiple-input multiple-output (MIMO) processes with Hammerstein nonlinearities. FLHI consists of a Takagi-Sugeno fuzzy inference system where membership functions act as kernel functions of an interpolator. Conjunction of membership functions in an unitary hypercube space enables multivariable interpolation of N-dimensions. Membership functions act as interpolation kernels, such that choice of membership functions determines interpolation characteristics, allowing FLHI to behave as a nearest-neighbor, linear, cubic, spline or Lanczos interpolator, to name a few. The proposed interpolator is presented as a solution to the modeling problem of static nonlinearities since it is capable of modeling both a function and its inverse function. Three study cases from literature are presented, a single-input single-output (SISO) system, a MISO and a MIMO system. Good results are obtained regarding performance metrics such as set-point tracking, control variation and robustness. Results demonstrate applicability of the proposed method in modeling Hammerstein nonlinearities and their inverse functions for implementation of an output compensator with Model Based Predictive Control (MBPC), in particular Dynamic Matrix Control (DMC). PMID:27657723

Scout 2008 Version 1.0 User Guide

EPA Science Inventory

The Scout 2008 version 1.0 software package provides a wide variety of classical and robust statistical methods that are not typically available in other commercial software packages. A major part of Scout deals with classical, robust, and resistant univariate and multivariate ou...

An improved method for bivariate meta-analysis when within-study correlations are unknown.

PubMed

Hong, Chuan; D Riley, Richard; Chen, Yong

2018-03-01

Multivariate meta-analysis, which jointly analyzes multiple and possibly correlated outcomes in a single analysis, is becoming increasingly popular in recent years. An attractive feature of the multivariate meta-analysis is its ability to account for the dependence between multiple estimates from the same study. However, standard inference procedures for multivariate meta-analysis require the knowledge of within-study correlations, which are usually unavailable. This limits standard inference approaches in practice. Riley et al proposed a working model and an overall synthesis correlation parameter to account for the marginal correlation between outcomes, where the only data needed are those required for a separate univariate random-effects meta-analysis. As within-study correlations are not required, the Riley method is applicable to a wide variety of evidence synthesis situations. However, the standard variance estimator of the Riley method is not entirely correct under many important settings. As a consequence, the coverage of a function of pooled estimates may not reach the nominal level even when the number of studies in the multivariate meta-analysis is large. In this paper, we improve the Riley method by proposing a robust variance estimator, which is asymptotically correct even when the model is misspecified (ie, when the likelihood function is incorrect). Simulation studies of a bivariate meta-analysis, in a variety of settings, show a function of pooled estimates has improved performance when using the proposed robust variance estimator. In terms of individual pooled estimates themselves, the standard variance estimator and robust variance estimator give similar results to the original method, with appropriate coverage. The proposed robust variance estimator performs well when the number of studies is relatively large. Therefore, we recommend the use of the robust method for meta-analyses with a relatively large number of studies (eg, m≥50). When the sample size is relatively small, we recommend the use of the robust method under the working independence assumption. We illustrate the proposed method through 2 meta-analyses. Copyright © 2017 John Wiley & Sons, Ltd.

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

Differential flatness properties and multivariable adaptive control of ovarian system dynamics

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos

2016-12-01

The ovarian system exhibits nonlinear dynamics which is modeled by a set of coupled nonlinear differential equations. The paper proposes adaptive fuzzy control based on differential flatness theory for the complex dynamics of the ovarian system. It is proven that the dynamic model of the ovarian system, having as state variables the LH and the FSH hormones and their derivatives, is a differentially flat one. This means that all its state variables and its control inputs can be described as differential functions of the flat output. By exploiting differential flatness properties the system's dynamic model is written in the multivariable linear canonical (Brunovsky) form, for which the design of a state feedback controller becomes possible. After this transformation, the new control inputs of the system contain unknown nonlinear parts, which are identified with the use of neurofuzzy approximators. The learning procedure for these estimators is determined by the requirement the first derivative of the closed-loop's Lyapunov function to be a negative one. Moreover, Lyapunov stability analysis shows that H-infinity tracking performance is succeeded for the feedback control loop and this assures improved robustness to the aforementioned model uncertainty as well as to external perturbations. The efficiency of the proposed adaptive fuzzy control scheme is confirmed through simulation experiments.

Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models.

PubMed

Chen, Zewei; Zhang, Xin; Zhang, Zhuoyong

2016-12-01

Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.

Uncertainty Modeling for Robustness Analysis of Control Upset Prevention and Recovery Systems

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.; Khong, Thuan H.; Shin, Jong-Yeob; Kwatny, Harry; Chang, Bor-Chin; Balas, Gary J.

2005-01-01

Formal robustness analysis of aircraft control upset prevention and recovery systems could play an important role in their validation and ultimate certification. Such systems (developed for failure detection, identification, and reconfiguration, as well as upset recovery) need to be evaluated over broad regions of the flight envelope and under extreme flight conditions, and should include various sources of uncertainty. However, formulation of linear fractional transformation (LFT) models for representing system uncertainty can be very difficult for complex parameter-dependent systems. This paper describes a preliminary LFT modeling software tool which uses a matrix-based computational approach that can be directly applied to parametric uncertainty problems involving multivariate matrix polynomial dependencies. Several examples are presented (including an F-16 at an extreme flight condition, a missile model, and a generic example with numerous crossproduct terms), and comparisons are given with other LFT modeling tools that are currently available. The LFT modeling method and preliminary software tool presented in this paper are shown to compare favorably with these methods.

A new continuous sliding mode control approach with actuator saturation for control of 2-DOF helicopter system.

PubMed

Sadala, S P; Patre, B M

2018-03-01

The 2-degree of freedom (DOF) helicopter system is a typical higher-order, multi-variable, nonlinear and strong coupled control system. The helicopter dynamics also includes parametric uncertainties and is subject to unknown external disturbances. Such complicated system requires designing a sophisticated control algorithm that can handle these difficulties. This paper presents a new robust control algorithm which is a combination of two continuous control techniques, composite nonlinear feedback (CNF) and super-twisting control (STC) methods. In the existing integral sliding mode (ISM) based CNF control law, the discontinuous term exhibits chattering which is not desirable for many practical applications. As the continuity of well known STC reduces chattering in the system, the proposed strategy is beneficial over the current ISM based CNF control law which has a discontinuous term. Two controllers with integral sliding surface are designed to control the position of the pitch and the yaw angles of the 2- DOF helicopter. The adequacy of this specific combination has been exhibited through general analysis, simulation and experimental results of 2-DOF helicopter setup. The acquired results demonstrate the good execution of the proposed controller regarding stabilization, following reference input without overshoot against actuator saturation and robustness concerning to the limited matched disturbances. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Inference on the Ranks of the Canonical Correlation Matrices for Elliptically Symmetric Populations.

DTIC Science & Technology

1985-05-01

robust estimates of the covariance matrix, the reader is referred to Devlin, Gnanadesikan and Kettenring (1975) and Maronna (1976). Murihead and...contoured distributions. J. Multivariate Anal. 11, 368-385. 6. DEVLIN, S.J. GNANADESIKAN , R. and KETTENRING, J. (1975). Robust estima- tion and outlier

A methodology for the synthesis of robust feedback systems. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Milich, David Albert

1988-01-01

A new methodology is developed for the synthesis of linear, time-variant (LTI) controllers for multivariable LTI systems. The resulting closed-loop system is nominally stable and exhibits a known level of performance. In addition, robustness of the feedback system is guaranteed, i.e., stability and performance are retained in the presence of multiple unstructured uncertainty blocks located at various points in the feedback loop. The design technique is referred to as the Causality Recovery Methodology (CRM). The CRM relies on the Youla parameterization of all stabilizing compensators to ensure nominal stability of the feedback system. A frequency-domain inequality in terms of the structured singular value mu defines the robustness specification. The optimal compensator, with respect to the mu condition, is shown to be noncausal in general. The aim of the CRM is to find a stable, causal transfer function matrix that approximates the robustness characteristics of the optimal solution. The CRM, via a series of infinite-dimensional convex programs, produces a closed-loop system whose performance robustness is at least as good as that of any initial design. The algorithm is approximated by a finite dimensional process for the purposes of implementation. Two numerical examples confirm the potential viability of the CRM concept; however, the robustness improvement comes at the expense of increased computational burden and compensator complexity.

Adjustment of geochemical background by robust multivariate statistics

USGS Publications Warehouse

Zhou, D.

1985-01-01

Conventional analyses of exploration geochemical data assume that the background is a constant or slowly changing value, equivalent to a plane or a smoothly curved surface. However, it is better to regard the geochemical background as a rugged surface, varying with changes in geology and environment. This rugged surface can be estimated from observed geological, geochemical and environmental properties by using multivariate statistics. A method of background adjustment was developed and applied to groundwater and stream sediment reconnaissance data collected from the Hot Springs Quadrangle, South Dakota, as part of the National Uranium Resource Evaluation (NURE) program. Source-rock lithology appears to be a dominant factor controlling the chemical composition of groundwater or stream sediments. The most efficacious adjustment procedure is to regress uranium concentration on selected geochemical and environmental variables for each lithologic unit, and then to delineate anomalies by a common threshold set as a multiple of the standard deviation of the combined residuals. Robust versions of regression and RQ-mode principal components analysis techniques were used rather than ordinary techniques to guard against distortion caused by outliers Anomalies delineated by this background adjustment procedure correspond with uranium prospects much better than do anomalies delineated by conventional procedures. The procedure should be applicable to geochemical exploration at different scales for other metals. ?? 1985.

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

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

Winters, Scott Eric

2003-06-18

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

Two-sample tests and one-way MANOVA for multivariate biomarker data with nondetects.

PubMed

Thulin, M

2016-09-10

Testing whether the mean vector of a multivariate set of biomarkers differs between several populations is an increasingly common problem in medical research. Biomarker data is often left censored because some measurements fall below the laboratory's detection limit. We investigate how such censoring affects multivariate two-sample and one-way multivariate analysis of variance tests. Type I error rates, power and robustness to increasing censoring are studied, under both normality and non-normality. Parametric tests are found to perform better than non-parametric alternatives, indicating that the current recommendations for analysis of censored multivariate data may have to be revised. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Outlier Detection in Hyperspectral Imagery Using Closest Distance to Center with Ellipsoidal Multivariate Trimming

DTIC Science & Technology

2011-01-01

where r << P. The use of PCA for finding outliers in multivariate data is surveyed by Gnanadesikan and Kettenring16 and Rao.17 As alluded to earlier...1984. 16. Gnanadesikan R and Kettenring JR. Robust estimates, residu­ als, and outlier detection with multiresponse data. Biometrics 1972; 28: 81–124

Lateral control system design for VTOL landing on a DD963 in high sea states. M.S. Thesis

NASA Technical Reports Server (NTRS)

Bodson, M.

1982-01-01

The problem of designing lateral control systems for the safe landing of VTOL aircraft on small ships is addressed. A ship model is derived. The issues of estimation and prediction of ship motions are discussed, using optimal linear linear estimation techniques. The roll motion is the most important of the lateral motions, and it is found that it can be predicted for up to 10 seconds in perfect conditions. The automatic landing of the VTOL aircraft is considered, and a lateral controller, defined as a ship motion tracker, is designed, using optimal control techniqes. The tradeoffs between the tracking errors and the control authority are obtained. The important couplings between the lateral motions and controls are demonstrated, and it is shown that the adverse couplings between the sway and the roll motion at the landing pad are significant constraints in the tracking of the lateral ship motions. The robustness of the control system, including the optimal estimator, is studied, using the singular values analysis. Through a robustification procedure, a robust control system is obtained, and the usefulness of the singular values to define stability margins that take into account general types of unstructured modelling errors is demonstrated. The minimal destabilizing perturbations indicated by the singular values analysis are interpreted and related to the multivariable Nyquist diagrams.

Identification of Differential Item Functioning in Multiple-Group Settings: A Multivariate Outlier Detection Approach

ERIC Educational Resources Information Center

Magis, David; De Boeck, Paul

2011-01-01

We focus on the identification of differential item functioning (DIF) when more than two groups of examinees are considered. We propose to consider items as elements of a multivariate space, where DIF items are outlying elements. Following this approach, the situation of multiple groups is a quite natural case. A robust statistics technique is…

The Graphical Display of Simulation Results, with Applications to the Comparison of Robust IRT Estimators of Ability.

ERIC Educational Resources Information Center

Thissen, David; Wainer, Howard

Simulation studies of the performance of (potentially) robust statistical estimation produce large quantities of numbers in the form of performance indices of the various estimators under various conditions. This report presents a multivariate graphical display used to aid in the digestion of the plentiful results in a current study of Item…

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

NASA Technical Reports Server (NTRS)

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

1991-01-01

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

Fast computation of the multivariable stability margin for real interrelated uncertain parameters

NASA Technical Reports Server (NTRS)

Sideris, Athanasios; Sanchez Pena, Ricardo S.

1988-01-01

A novel algorithm for computing the multivariable stability margin for checking the robust stability of feedback systems with real parametric uncertainty is proposed. This method eliminates the need for the frequency search involved in another given algorithm by reducing it to checking a finite number of conditions. These conditions have a special structure, which allows a significant improvement on the speed of computations.

Generating Multivariate Ordinal Data via Entropy Principles.

PubMed

Lee, Yen; Kaplan, David

2018-03-01

When conducting robustness research where the focus of attention is on the impact of non-normality, the marginal skewness and kurtosis are often used to set the degree of non-normality. Monte Carlo methods are commonly applied to conduct this type of research by simulating data from distributions with skewness and kurtosis constrained to pre-specified values. Although several procedures have been proposed to simulate data from distributions with these constraints, no corresponding procedures have been applied for discrete distributions. In this paper, we present two procedures based on the principles of maximum entropy and minimum cross-entropy to estimate the multivariate observed ordinal distributions with constraints on skewness and kurtosis. For these procedures, the correlation matrix of the observed variables is not specified but depends on the relationships between the latent response variables. With the estimated distributions, researchers can study robustness not only focusing on the levels of non-normality but also on the variations in the distribution shapes. A simulation study demonstrates that these procedures yield excellent agreement between specified parameters and those of estimated distributions. A robustness study concerning the effect of distribution shape in the context of confirmatory factor analysis shows that shape can affect the robust [Formula: see text] and robust fit indices, especially when the sample size is small, the data are severely non-normal, and the fitted model is complex.

Estimating the ratio of multivariate recurrent event rates with application to a blood transfusion study.

PubMed

Ning, Jing; Rahbar, Mohammad H; Choi, Sangbum; Piao, Jin; Hong, Chuan; Del Junco, Deborah J; Rahbar, Elaheh; Fox, Erin E; Holcomb, John B; Wang, Mei-Cheng

2017-08-01

In comparative effectiveness studies of multicomponent, sequential interventions like blood product transfusion (plasma, platelets, red blood cells) for trauma and critical care patients, the timing and dynamics of treatment relative to the fragility of a patient's condition is often overlooked and underappreciated. While many hospitals have established massive transfusion protocols to ensure that physiologically optimal combinations of blood products are rapidly available, the period of time required to achieve a specified massive transfusion standard (e.g. a 1:1 or 1:2 ratio of plasma or platelets:red blood cells) has been ignored. To account for the time-varying characteristics of transfusions, we use semiparametric rate models for multivariate recurrent events to estimate blood product ratios. We use latent variables to account for multiple sources of informative censoring (early surgical or endovascular hemorrhage control procedures or death). The major advantage is that the distributions of latent variables and the dependence structure between the multivariate recurrent events and informative censoring need not be specified. Thus, our approach is robust to complex model assumptions. We establish asymptotic properties and evaluate finite sample performance through simulations, and apply the method to data from the PRospective Observational Multicenter Major Trauma Transfusion study.

Is the ML Chi-Square Ever Robust to Nonnormality? A Cautionary Note with Missing Data

ERIC Educational Resources Information Center

Savalei, Victoria

2008-01-01

Normal theory maximum likelihood (ML) is by far the most popular estimation and testing method used in structural equation modeling (SEM), and it is the default in most SEM programs. Even though this approach assumes multivariate normality of the data, its use can be justified on the grounds that it is fairly robust to the violations of the…

Adaptive control of large space structures using recursive lattice filters

NASA Technical Reports Server (NTRS)

Goglia, G. L.

1985-01-01

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

Finite time control for MIMO nonlinear system based on higher-order sliding mode.

PubMed

Liu, Xiangjie; Han, Yaozhen

2014-11-01

Considering a class of MIMO uncertain nonlinear system, a novel finite time stable control algorithm is proposed based on higher-order sliding mode concept. The higher-order sliding mode control problem of MIMO nonlinear system is firstly transformed into finite time stability problem of multivariable system. Then continuous control law, which can guarantee finite time stabilization of nominal integral chain system, is employed. The second-order sliding mode is used to overcome the system uncertainties. High frequency chattering phenomenon of sliding mode is greatly weakened, and the arbitrarily fast convergence is reached. The finite time stability is proved based on the quadratic form Lyapunov function. Examples concerning the triple integral chain system with uncertainty and the hovercraft trajectory tracking are simulated respectively to verify the effectiveness and the robustness of the proposed algorithm. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Design and experiment of data-driven modeling and flutter control of a prototype wing

NASA Astrophysics Data System (ADS)

Lum, Kai-Yew; Xu, Cai-Lin; Lu, Zhenbo; Lai, Kwok-Leung; Cui, Yongdong

2017-06-01

This paper presents an approach for data-driven modeling of aeroelasticity and its application to flutter control design of a wind-tunnel wing model. Modeling is centered on system identification of unsteady aerodynamic loads using computational fluid dynamics data, and adopts a nonlinear multivariable extension of the Hammerstein-Wiener system. The formulation is in modal coordinates of the elastic structure, and yields a reduced-order model of the aeroelastic feedback loop that is parametrized by airspeed. Flutter suppression is thus cast as a robust stabilization problem over uncertain airspeed, for which a low-order H∞ controller is computed. The paper discusses in detail parameter sensitivity and observability of the model, the former to justify the chosen model structure, and the latter to provide a criterion for physical sensor placement. Wind tunnel experiments confirm the validity of the modeling approach and the effectiveness of the control design.

Robust tests for multivariate factorial designs under heteroscedasticity.

PubMed

Vallejo, Guillermo; Ato, Manuel

2012-06-01

The question of how to analyze several multivariate normal mean vectors when normality and covariance homogeneity assumptions are violated is considered in this article. For the two-way MANOVA layout, we address this problem adapting results presented by Brunner, Dette, and Munk (BDM; 1997) and Vallejo and Ato (modified Brown-Forsythe [MBF]; 2006) in the context of univariate factorial and split-plot designs and a multivariate version of the linear model (MLM) to accommodate heterogeneous data. Furthermore, we compare these procedures with the Welch-James (WJ) approximate degrees of freedom multivariate statistics based on ordinary least squares via Monte Carlo simulation. Our numerical studies show that of the methods evaluated, only the modified versions of the BDM and MBF procedures were robust to violations of underlying assumptions. The MLM approach was only occasionally liberal, and then by only a small amount, whereas the WJ procedure was often liberal if the interactive effects were involved in the design, particularly when the number of dependent variables increased and total sample size was small. On the other hand, it was also found that the MLM procedure was uniformly more powerful than its most direct competitors. The overall success rate was 22.4% for the BDM, 36.3% for the MBF, and 45.0% for the MLM.

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

PubMed

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

2015-03-01

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

Theoretical constraints in the design of multivariable control systems

NASA Technical Reports Server (NTRS)

Rynaski, E. G.; Mook, D. J.

1993-01-01

The theoretical constraints inherent in the design of multivariable control systems were defined and investigated. These constraints are manifested by the system transmission zeros that limit or bound the areas in which closed loop poles and individual transfer function zeros may be placed. These constraints were investigated primarily in the context of system decoupling or non-interaction. It was proven that decoupling requires the placement of closed loop poles at the system transmission zeros. Therefore, the system transmission zeros must be minimum phase to guarantee a stable decoupled system. Once decoupling has been accomplished, the remaining part of the system exhibits transmission zeros at infinity, so nearly complete design freedom is possible in terms of placing both poles and zeros of individual closed loop transfer functions. A general, dynamic inversion model following system architecture was developed that encompasses both the implicit and explicit configuration. Robustness properties are developed along with other attributes of this type of system. Finally, a direct design is developed for the longitudinal-vertical degrees of freedom of aircraft motion to show how a direct lift flap can be used to improve the pitch-heave maneuvering coordination for enhanced flying qualities.

Modeling inflation rates and exchange rates in Ghana: application of multivariate GARCH models.

PubMed

Nortey, Ezekiel Nn; Ngoh, Delali D; Doku-Amponsah, Kwabena; Ofori-Boateng, Kenneth

2015-01-01

This paper was aimed at investigating the volatility and conditional relationship among inflation rates, exchange rates and interest rates as well as to construct a model using multivariate GARCH DCC and BEKK models using Ghana data from January 1990 to December 2013. The study revealed that the cumulative depreciation of the cedi to the US dollar from 1990 to 2013 is 7,010.2% and the yearly weighted depreciation of the cedi to the US dollar for the period is 20.4%. There was evidence that, the fact that inflation rate was stable, does not mean that exchange rates and interest rates are expected to be stable. Rather, when the cedi performs well on the forex, inflation rates and interest rates react positively and become stable in the long run. The BEKK model is robust to modelling and forecasting volatility of inflation rates, exchange rates and interest rates. The DCC model is robust to model the conditional and unconditional correlation among inflation rates, exchange rates and interest rates. The BEKK model, which forecasted high exchange rate volatility for the year 2014, is very robust for modelling the exchange rates in Ghana. The mean equation of the DCC model is also robust to forecast inflation rates in Ghana.

Robust Control of Wide Bandgap Power Electronics Device Enabled Smart Grid

NASA Astrophysics Data System (ADS)

Yao, Tong

In recent years, wide bandgap (WBG) devices enable power converters with higher power density and higher efficiency. On the other hand, smart grid technologies are getting mature due to new battery technology and computer technology. In the near future, the two technologies will form the next generation of smart grid enabled by WBG devices. This dissertation deals with two applications: silicon carbide (SiC) device used for medium voltage level interface (7.2 kV to 240 V) and gallium nitride (GaN) device used for low voltage level interface (240 V/120 V). A 20 kW solid state transformer (SST) is designed with 6 kHz switching frequency SiC rectifier. Then three robust control design methods are proposed for each of its smart grid operation modes. In grid connected mode, a new LCL filter design method is proposed considering grid voltage THD, grid current THD and current regulation loop robust stability with respect to the grid impedance change. In grid islanded mode, micro synthesis method combined with variable structure control is used to design a robust controller for grid voltage regulation. For grid emergency mode, multivariable controller designed using Hinfinity synthesis method is proposed for accurate power sharing. Controller-hardware-in-the-loop (CHIL) testbed considering 7-SST system is setup with Real Time Digital Simulator (RTDS). The real TMS320F28335 DSP and Spartan 6 FPGA control board is used to interface a switching model SST in RTDS. And the proposed control methods are tested. For low voltage level application, a 3.3 kW smart grid hardware is built with 3 GaN inverters. The inverters are designed with the GaN device characterized using the proposed multi-function double pulse tester. The inverter is controlled by onboard TMS320F28379D dual core DSP with 200 kHz sampling frequency. Each inverter is tested to process 2.2 kW power with overall efficiency of 96.5 % at room temperature. The smart grid monitor system and fault interrupt devices (FID) based on Arduino Mega2560 are built and tested. The smart grid cooperates with GaN inverters through CAN bus communication. At last, the three GaN inverters smart grid achieved the function of grid connected to islanded mode smooth transition.

Systematic design of membership functions for fuzzy-logic control: A case study on one-stage partial nitritation/anammox treatment systems.

PubMed

Boiocchi, Riccardo; Gernaey, Krist V; Sin, Gürkan

2016-10-01

A methodology is developed to systematically design the membership functions of fuzzy-logic controllers for multivariable systems. The methodology consists of a systematic derivation of the critical points of the membership functions as a function of predefined control objectives. Several constrained optimization problems corresponding to different qualitative operation states of the system are defined and solved to identify, in a consistent manner, the critical points of the membership functions for the input variables. The consistently identified critical points, together with the linguistic rules, determine the long term reachability of the control objectives by the fuzzy logic controller. The methodology is highlighted using a single-stage side-stream partial nitritation/Anammox reactor as a case study. As a result, a new fuzzy-logic controller for high and stable total nitrogen removal efficiency is designed. Rigorous simulations are carried out to evaluate and benchmark the performance of the controller. The results demonstrate that the novel control strategy is capable of rejecting the long-term influent disturbances, and can achieve a stable and high TN removal efficiency. Additionally, the controller was tested, and showed robustness, against measurement noise levels typical for wastewater sensors. A feedforward-feedback configuration using the present controller would give even better performance. In comparison, a previously developed fuzzy-logic controller using merely expert and intuitive knowledge performed worse. This proved the importance of using a systematic methodology for the derivation of the membership functions for multivariable systems. These results are promising for future applications of the controller in real full-scale plants. Furthermore, the methodology can be used as a tool to help systematically design fuzzy logic control applications for other biological processes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Some Integrated Squared Error Procedures for Multivariate Normal Data,

DTIC Science & Technology

1986-01-01

a lnear regresmion or experimental design model). Our procedures have &lSO been usned wcelyOn non -linear models but we do not addres nan-lnear...of fit, outliers, influence functions, experimental design , cluster analysis, robustness 24L A =TO ACT (VCefme - pvre alli of magsy MW identif by...structured data such as multivariate experimental designs . Several illustrations are provided. * 0 %41 %-. 4.’. * " , -.--, ,. -,, ., -, ’v ’ , " ,,- ,, . -,-. . ., * . - tAma- t

Self-Critical, and Robust, Procedures for the Analysis of Multivariate Normal Data.

DTIC Science & Technology

1982-06-01

Influence Functions The influence function is the most important tt of qual- itative zobustness since many other robustness characteristics of an estimator...may be derived from it. The influence function characterizes the (asymptotic) response of an estimator to an additional observation as a function of...the influence function be bounded. It is also advantageous, in our opinion, if the influence functions are re-descending to zero. The influence function for

Combustion distribution control using the extremum seeking algorithm

NASA Astrophysics Data System (ADS)

Marjanovic, A.; Krstic, M.; Djurovic, Z.; Kvascev, G.; Papic, V.

2014-12-01

Quality regulation of the combustion process inside the furnace is the basis of high demands for increasing robustness, safety and efficiency of thermal power plants. The paper considers the possibility of spatial temperature distribution control inside the boiler, based on the correction of distribution of coal over the mills. Such control system ensures the maintenance of the flame focus away from the walls of the boiler, and thus preserves the equipment and reduces the possibility of ash slugging. At the same time, uniform heat dissipation over mills enhances the energy efficiency of the boiler, while reducing the pollution of the system. A constrained multivariable extremum seeking algorithm is proposed as a tool for combustion process optimization with the main objective of centralizing the flame in the furnace. Simulations are conducted on a model corresponding to the 350MW boiler of the Nikola Tesla Power Plant, in Obrenovac, Serbia.

Vibration attenuation of the NASA Langley evolutionary structure experiment using H(sub infinity) and structured singular value (micron) robust multivariable control techniques

NASA Technical Reports Server (NTRS)

Balas, Gary J.

1992-01-01

The use is studied of active control to attenuate structural vibrations of the NASA Langley Phase Zero Evolutionary Structure due to external disturbance excitations. H sub infinity and structured singular value (mu) based control techniques are used to analyze and synthesize control laws for the NASA Langley Controls Structures Interaction (CSI) Evolutionary Model (CEM). The CEM structure experiment provides an excellent test bed to address control design issues for large space structures. Specifically, control design for structures with numerous lightly damped, coupled flexible modes, collocated and noncollocated sensors and actuators and stringent performance specifications. The performance objectives are to attenuate the vibration of the structure due to external disturbances, and minimize the actuator control force. The control design problem formulation for the CEM Structure uses a mathematical model developed with finite element techniques. A reduced order state space model for the control design is formulated from the finite element model. It is noted that there are significant variations between the design model and the experimentally derived transfer function data.

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.

Conic Sector Analysis of Hybrid Control Systems. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Thompson, P. M.

1982-01-01

A hybrid control system contains an analog plant and a hybrid (or sampled-data) compensator. In this thesis a new conic sector is determined which is constructive and can be used to: (1) determine closed loop stability, (2) analyze robustness with respect to modelling uncertainties, (3) analyze steady state response to commands, and (4) select the sample rate. The use of conic sectors allows the designer to treat hybrid control systems as though they were analog control systems. The center of the conic sector can be used as a rigorous linear time invariant approximation of the hybrid control system, and the radius places a bound on the errors of this approximation. The hybrid feedback system can be multivariable, and the sampler is assumed to be synchronous. Algorithms to compute the conic sector are presented. Several examples demonstrate how the conic sector analysis techniques are applied. Extensions to single loop multirate hybrid feedback systems are presented. Further extensions are proposed for multiloop multirate hybrid feedback system and for single rate systems with asynchronous sampling.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a 'black box' approach and how to avoid them

NASA Astrophysics Data System (ADS)

Safi, A.; Campanella, B.; Grifoni, E.; Legnaioli, S.; Lorenzetti, G.; Pagnotta, S.; Poggialini, F.; Ripoll-Seguer, L.; Hidalgo, M.; Palleschi, V.

2018-06-01

The introduction of multivariate calibration curve approach in Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis has led to a general improvement of the LIBS analytical performances, since a multivariate approach allows to exploit the redundancy of elemental information that are typically present in a LIBS spectrum. Software packages implementing multivariate methods are available in the most diffused commercial and open source analytical programs; in most of the cases, the multivariate algorithms are robust against noise and operate in unsupervised mode. The reverse of the coin of the availability and ease of use of such packages is the (perceived) difficulty in assessing the reliability of the results obtained which often leads to the consideration of the multivariate algorithms as 'black boxes' whose inner mechanism is supposed to remain hidden to the user. In this paper, we will discuss the dangers of a 'black box' approach in LIBS multivariate analysis, and will discuss how to overcome them using the chemical-physical knowledge that is at the base of any LIBS quantitative analysis.

Application of reflectance spectroscopies (FTIR-ATR & FT-NIR) coupled with multivariate methods for robust in vivo detection of begomovirus infection in papaya leaves

NASA Astrophysics Data System (ADS)

Haq, Quazi M. I.; Mabood, Fazal; Naureen, Zakira; Al-Harrasi, Ahmed; Gilani, Sayed A.; Hussain, Javid; Jabeen, Farah; Khan, Ajmal; Al-Sabari, Ruqaya S. M.; Al-khanbashi, Fatema H. S.; Al-Fahdi, Amira A. M.; Al-Zaabi, Ahoud K. A.; Al-Shuraiqi, Fatma A. M.; Al-Bahaisi, Iman M.

2018-06-01

Nucleic acid & serology based methods have revolutionized plant disease detection, however, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic infection, in addition, they need at least 1-2 days for sample harvesting, processing, and analysis. In this study, two reflectance spectroscopies i.e. Near Infrared reflectance spectroscopy (NIR) and Fourier-Transform-Infrared spectroscopy with Attenuated Total Reflection (FT-IR, ATR) coupled with multivariate exploratory methods like Principle Component Analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been deployed to detect begomovirus infection in papaya leaves. The application of those techniques demonstrates that they are very useful for robust in vivo detection of plant begomovirus infection. These methods are simple, sensitive, reproducible, precise, and do not require any lengthy samples preparation procedures.

Child and adult outcomes of chronic child maltreatment.

PubMed

Jonson-Reid, Melissa; Kohl, Patricia L; Drake, Brett

2012-05-01

To describe how child maltreatment chronicity is related to negative outcomes in later childhood and early adulthood. The study included 5994 low-income children from St Louis, including 3521 with child maltreatment reports, who were followed from 1993-1994 through 2009. Children were 1.5 to 11 years of age at sampling. Data include administrative and treatment records indicating substance abuse, mental health treatment, brain injury, sexually transmitted disease, suicide attempts, and violent delinquency before age 18 and child maltreatment perpetration, mental health treatment, or substance abuse in adulthood. Multivariate analysis controlled for potential confounders. Child maltreatment chronicity predicted negative childhood outcomes in a linear fashion (eg, percentage with at least 1 negative outcome: no maltreatment = 29.7%, 1 report = 39.5%, 4 reports = 67.1%). Suicide attempts before age 18 showed the largest proportionate increase with repeated maltreatment (no report versus 4+ reports = +625%, P < .0001). The dose-response relationship was reduced once controls for other adverse child outcomes were added in multivariate models of child maltreatment perpetration and mental health issues. The relationship between adult substance abuse and maltreatment report history disappeared after controlling for adverse child outcomes. Child maltreatment chronicity as measured by official reports is a robust indicator of future negative outcomes across a range of systems, but this relationship may desist for certain adult outcomes once childhood adverse events are controlled. Although primary and secondary prevention remain important approaches, this study suggests that enhanced tertiary prevention may pay high dividends across a range of medical and behavioral domains.

Synthesis and operation of an FFT-decoupled fixed-order reversed-field pinch plasma control system based on identification data

NASA Astrophysics Data System (ADS)

Olofsson, K. Erik J.; Brunsell, Per R.; Witrant, Emmanuel; Drake, James R.

2010-10-01

Recent developments and applications of system identification methods for the reversed-field pinch (RFP) machine EXTRAP T2R have yielded plasma response parameters for decoupled dynamics. These data sets are fundamental for a real-time implementable fast Fourier transform (FFT) decoupled discrete-time fixed-order strongly stabilizing synthesis as described in this work. Robustness is assessed over the data set by bootstrap calculation of the sensitivity transfer function worst-case H_{\\infty} -gain distribution. Output tracking and magnetohydrodynamic mode m = 1 tracking are considered in the same framework simply as two distinct weighted traces of a performance channel output-covariance matrix as derived from the closed-loop discrete-time Lyapunov equation. The behaviour of the resulting multivariable controller is investigated with dedicated T2R experiments.

On the Directional Dependence and Null Space Freedom in Uncertainty Bound Identification

NASA Technical Reports Server (NTRS)

Lim, K. B.; Giesy, D. P.

1997-01-01

In previous work, the determination of uncertainty models via minimum norm model validation is based on a single set of input and output measurement data. Since uncertainty bounds at each frequency is directionally dependent for multivariable systems, this will lead to optimistic uncertainty levels. In addition, the design freedom in the uncertainty model has not been utilized to further reduce uncertainty levels. The above issues are addressed by formulating a min- max problem. An analytical solution to the min-max problem is given to within a generalized eigenvalue problem, thus avoiding a direct numerical approach. This result will lead to less conservative and more realistic uncertainty models for use in robust control.

Robust Control of Multivariable and Large Scale Systems.

DTIC Science & Technology

1988-03-23

D, and let A E [0, 1]. We need to show that h ((1 - A)D1 + AD2 ) < (1 - A)h(D 1 ) + Ah(D 2) Define f:R-+R by f(t) := h((1 - t)D 1 + tD 2 ) = ebt...SVD -S M = 3UV * + U2E2 V2 *. (13.11) In this setting, / is any singular value of M, not necessarily &(M), but none of the singular values in E 2 should...a)f (x) -afr(y)] and let A be the largest value in [0, 1] that achieves #3. Obviously, since /3 > 0, A E (0, 1). Define tD := (1 - A)x + Ay. Hence f

A single sensor and single actuator approach to performance tailoring over a prescribed frequency band.

PubMed

Wang, Jiqiang

2016-03-01

Restricted sensing and actuation control represents an important area of research that has been overlooked in most of the design methodologies. In many practical control engineering problems, it is necessitated to implement the design through a single sensor and single actuator for multivariate performance variables. In this paper, a novel approach is proposed for the solution to the single sensor and single actuator control problem where performance over any prescribed frequency band can also be tailored. The results are obtained for the broad band control design based on the formulation for discrete frequency control. It is shown that the single sensor and single actuator control problem over a frequency band can be cast into a Nevanlinna-Pick interpolation problem. An optimal controller can then be obtained via the convex optimization over LMIs. Even remarkable is that robustness issues can also be tackled in this framework. A numerical example is provided for the broad band attenuation of rotor blade vibration to illustrate the proposed design procedures. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Structure and Correlates of Cognitive Aging in a Narrow Age Cohort

PubMed Central

2014-01-01

Aging-related changes occur for multiple domains of cognitive functioning. An accumulating body of research indicates that, rather than representing statistically independent phenomena, aging-related cognitive changes are moderately to strongly correlated across domains. However, previous studies have typically been conducted in age-heterogeneous samples over longitudinal time lags of 6 or more years, and have failed to consider whether results are robust to a comprehensive set of controls. Capitalizing on 3-year longitudinal data from the Lothian Birth Cohort of 1936, we took a longitudinal narrow age cohort approach to examine cross-domain cognitive change interrelations from ages 70 to 73 years. We fit multivariate latent difference score models to factors representing visuospatial ability, processing speed, memory, and crystallized ability. Changes were moderately interrelated, with a general factor of change accounting for 47% of the variance in changes across domains. Change interrelations persisted at close to full strength after controlling for a comprehensive set of demographic, physical, and medical factors including educational attainment, childhood intelligence, physical function, APOE genotype, smoking status, diagnosis of hypertension, diagnosis of cardiovascular disease, and diagnosis of diabetes. Thus, the positive manifold of aging-related cognitive changes is highly robust in that it can be detected in a narrow age cohort followed over a relatively brief longitudinal period, and persists even after controlling for many potential confounders. PMID:24955992

Structure and correlates of cognitive aging in a narrow age cohort.

PubMed

Tucker-Drob, Elliot M; Briley, Daniel A; Starr, John M; Deary, Ian J

2014-06-01

Aging-related changes occur for multiple domains of cognitive functioning. An accumulating body of research indicates that, rather than representing statistically independent phenomena, aging-related cognitive changes are moderately to strongly correlated across domains. However, previous studies have typically been conducted in age-heterogeneous samples over longitudinal time lags of 6 or more years, and have failed to consider whether results are robust to a comprehensive set of controls. Capitalizing on 3-year longitudinal data from the Lothian Birth Cohort of 1936, we took a longitudinal narrow age cohort approach to examine cross-domain cognitive change interrelations from ages 70 to 73 years. We fit multivariate latent difference score models to factors representing visuospatial ability, processing speed, memory, and crystallized ability. Changes were moderately interrelated, with a general factor of change accounting for 47% of the variance in changes across domains. Change interrelations persisted at close to full strength after controlling for a comprehensive set of demographic, physical, and medical factors including educational attainment, childhood intelligence, physical function, APOE genotype, smoking status, diagnosis of hypertension, diagnosis of cardiovascular disease, and diagnosis of diabetes. Thus, the positive manifold of aging-related cognitive changes is highly robust in that it can be detected in a narrow age cohort followed over a relatively brief longitudinal period, and persists even after controlling for many potential confounders. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Predictors of effects of lifestyle intervention on diabetes mellitus type 2 patients.

PubMed

Jacobsen, Ramune; Vadstrup, Eva; Røder, Michael; Frølich, Anne

2012-01-01

The main aim of the study was to identify predictors of the effects of lifestyle intervention on diabetes mellitus type 2 patients by means of multivariate analysis. Data from a previously published randomised clinical trial, which compared the effects of a rehabilitation programme including standardised education and physical training sessions in the municipality's health care centre with the same duration of individual counseling in the diabetes outpatient clinic, were used. Data from 143 diabetes patients were analysed. The merged lifestyle intervention resulted in statistically significant improvements in patients' systolic blood pressure, waist circumference, exercise capacity, glycaemic control, and some aspects of general health-related quality of life. The linear multivariate regression models explained 45% to 80% of the variance in these improvements. The baseline outcomes in accordance to the logic of the regression to the mean phenomenon were the only statistically significant and robust predictors in all regression models. These results are important from a clinical point of view as they highlight the more urgent need for and better outcomes following lifestyle intervention for those patients who have worse general and disease-specific health.

Multivariate data analysis on historical IPV production data for better process understanding and future improvements.

PubMed

Thomassen, Yvonne E; van Sprang, Eric N M; van der Pol, Leo A; Bakker, Wilfried A M

2010-09-01

Historical manufacturing data can potentially harbor a wealth of information for process optimization and enhancement of efficiency and robustness. To extract useful data multivariate data analysis (MVDA) using projection methods is often applied. In this contribution, the results obtained from applying MVDA on data from inactivated polio vaccine (IPV) production runs are described. Data from over 50 batches at two different production scales (700-L and 1,500-L) were available. The explorative analysis performed on single unit operations indicated consistent manufacturing. Known outliers (e.g., rejected batches) were identified using principal component analysis (PCA). The source of operational variation was pinpointed to variation of input such as media. Other relevant process parameters were in control and, using this manufacturing data, could not be correlated to product quality attributes. The gained knowledge of the IPV production process, not only from the MVDA, but also from digitalizing the available historical data, has proven to be useful for troubleshooting, understanding limitations of available data and seeing the opportunity for improvements. 2010 Wiley Periodicals, Inc.

Quick Overview Scout 2008 Version 1.0

EPA Science Inventory

The Scout 2008 version 1.0 statistical software package has been updated from past DOS and Windows versions to provide classical and robust univariate and multivariate graphical and statistical methods that are not typically available in commercial or freeware statistical softwar...

Modeling and Control for Microgrids

NASA Astrophysics Data System (ADS)

Steenis, Joel

Traditional approaches to modeling microgrids include the behavior of each inverter operating in a particular network configuration and at a particular operating point. Such models quickly become computationally intensive for large systems. Similarly, traditional approaches to control do not use advanced methodologies and suffer from poor performance and limited operating range. In this document a linear model is derived for an inverter connected to the Thevenin equivalent of a microgrid. This model is then compared to a nonlinear simulation model and analyzed using the open and closed loop systems in both the time and frequency domains. The modeling error is quantified with emphasis on its use for controller design purposes. Control design examples are given using a Glover McFarlane controller, gain scheduled Glover McFarlane controller, and bumpless transfer controller which are compared to the standard droop control approach. These examples serve as a guide to illustrate the use of multi-variable modeling techniques in the context of robust controller design and show that gain scheduled MIMO control techniques can extend the operating range of a microgrid. A hardware implementation is used to compare constant gain droop controllers with Glover McFarlane controllers and shows a clear advantage of the Glover McFarlane approach.

Fragility of Results in Ophthalmology Randomized Controlled Trials: A Systematic Review.

PubMed

Shen, Carl; Shamsudeen, Isabel; Farrokhyar, Forough; Sabri, Kourosh

2018-05-01

Evidence-based medicine is guided by our interpretation of randomized controlled trials (RCTs) that address important clinical questions. Evaluation of the robustness of statistically significant outcomes adds a crucial element to the global assessment of trial findings. The purpose of this systematic review was to determine the robustness of ophthalmology RCTs through application of the Fragility Index (FI), a novel metric of the robustness of statistically significant outcomes. Systematic review. A literature search (MEDLINE) was performed for all RCTs published in top ophthalmology journals and ophthalmology-related RCTs published in high-impact journals in the past 10 years. Two reviewers independently screened 1811 identified articles for inclusion if they (1) were a human ophthalmology-related trial, (2) had a 1:1 prospective study design, and (3) reported a statistically significant dichotomous outcome in the abstract. All relevant data, including outcome, P value, number of patients in each group, number of events in each group, number of patients lost to follow-up, and trial characteristics, were extracted. The FI of each RCT was calculated and multivariate regression applied to determine predictive factors. The 156 trials had a median sample size of 91.5 (range, 13-2593) patients/eyes, and a median of 28 (range, 4-2217) events. The median FI of the included trials was 2 (range, 0-48), meaning that if 2 non-events were switched to events in the treatment group, the result would lose its statistical significance. A quarter of all trials had an FI of 1 or less, and 75% of trials had an FI of 6 or less. The FI was less than the number of missing data points in 52.6% of trials. Predictive factors for FI by multivariate regression included smaller P value (P < 0.001), larger sample size (P = 0.001), larger number of events (P = 0.011), and journal impact factor (P = 0.029). In ophthalmology trials, statistically significant dichotomous results are often fragile, meaning that a difference of only a couple of events can change the statistical significance. An application of the FI in RCTs may aid in the interpretation of results and assessment of quality of evidence. Copyright © 2017 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Vehicle active steering control research based on two-DOF robust internal model control

NASA Astrophysics Data System (ADS)

Wu, Jian; Liu, Yahui; Wang, Fengbo; Bao, Chunjiang; Sun, Qun; Zhao, Youqun

2016-07-01

Because of vehicle's external disturbances and model uncertainties, robust control algorithms have obtained popularity in vehicle stability control. The robust control usually gives up performance in order to guarantee the robustness of the control algorithm, therefore an improved robust internal model control(IMC) algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness. The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties. In order to separate the design process of model tracking from the robustness design process, the improved 2 degree of freedom(DOF) robust internal model controller structure is given from the standard Youla parameterization. Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm, on the basis of a nonlinear vehicle simulation model with a magic tyre model. Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance, which can enhance the vehicle stability and handling, regardless of variations of the vehicle model parameters and the external crosswind interferences. Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

Beyond singular values and loop shapes

NASA Technical Reports Server (NTRS)

Stein, G.

1985-01-01

The status of singular value loop-shaping as a design paradigm for multivariable feedback systems is reviewed. It shows that this paradigm is an effective design tool whenever the problem specifications are spacially round. The tool can be arbitrarily conservative, however, when they are not. This happens because singular value conditions for robust performance are not tight (necessary and sufficient) and can severely overstate actual requirements. An alternate paradign is discussed which overcomes these limitations. The alternative includes a more general problem formulation, a new matrix function mu, and tight conditions for both robust stability and robust performance. The state of the art currently permits analysis of feedback systems within this new paradigm. Synthesis remains a subject of research.

On the formulation of a minimal uncertainty model for robust control with structured uncertainty

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.; Chang, B.-C.; Fischl, Robert

1991-01-01

In the design and analysis of robust control systems for uncertain plants, representing the system transfer matrix in the form of what has come to be termed an M-delta model has become widely accepted and applied in the robust control literature. The M represents a transfer function matrix M(s) of the nominal closed loop system, and the delta represents an uncertainty matrix acting on M(s). The nominal closed loop system M(s) results from closing the feedback control system, K(s), around a nominal plant interconnection structure P(s). The uncertainty can arise from various sources, such as structured uncertainty from parameter variations or multiple unsaturated uncertainties from unmodeled dynamics and other neglected phenomena. In general, delta is a block diagonal matrix, but for real parameter variations delta is a diagonal matrix of real elements. Conceptually, the M-delta structure can always be formed for any linear interconnection of inputs, outputs, transfer functions, parameter variations, and perturbations. However, very little of the currently available literature addresses computational methods for obtaining this structure, and none of this literature addresses a general methodology for obtaining a minimal M-delta model for a wide class of uncertainty, where the term minimal refers to the dimension of the delta matrix. Since having a minimally dimensioned delta matrix would improve the efficiency of structured singular value (or multivariable stability margin) computations, a method of obtaining a minimal M-delta would be useful. Hence, a method of obtaining the interconnection system P(s) is required. A generalized procedure for obtaining a minimal P-delta structure for systems with real parameter variations is presented. Using this model, the minimal M-delta model can then be easily obtained by closing the feedback loop. The procedure involves representing the system in a cascade-form state-space realization, determining the minimal uncertainty matrix, delta, and constructing the state-space representation of P(s). Three examples are presented to illustrate the procedure.

Comparison of sensitivity to artificial spectral errors and multivariate LOD in NIR spectroscopy - Determining the performance of miniaturizations on melamine in milk powder.

PubMed

Henn, Raphael; Kirchler, Christian G; Grossgut, Maria-Elisabeth; Huck, Christian W

2017-05-01

This study compared three commercially available spectrometers - whereas two of them were miniaturized - in terms of prediction ability of melamine in milk powder (infant formula). Therefore all spectra were split into calibration- and validation-set using Kennard Stone and Duplex algorithm in comparison. For each instrument the three best performing PLSR models were constructed using SNV and Savitzky Golay derivatives. The best RMSEP values were 0.28g/100g, 0.33g/100g and 0.27g/100g for the NIRFlex N-500, the microPHAZIR and the microNIR2200 respectively. Furthermore the multivariate LOD interval [LOD min , LOD max ] was calculated for all the PLSR models unveiling significant differences among the spectrometers showing values of 0.20g/100g - 0.27g/100g, 0.28g/100g - 0.54g/100g and 0.44g/100g - 1.01g/100g for the NIRFlex N-500, the microPHAZIR and the microNIR2200 respectively. To assess the robustness of all models, artificial introduction of white noise, baseline shift, multiplicative effect, spectral shrink and stretch, stray light and spectral shift were applied. Monitoring the RMSEP as function of the perturbation gave indication of robustness of the models and helped to compare the performances of the spectrometers. Not taking the additional information from the LOD calculations into account one could falsely assume that all the spectrometers perform equally well which is not the case when the multivariate evaluation and robustness data were considered. Copyright © 2017 Elsevier B.V. All rights reserved.

Experimental validation of docking and capture using space robotics testbeds

NASA Technical Reports Server (NTRS)

Spofford, John; Schmitz, Eric; Hoff, William

1991-01-01

This presentation describes the application of robotic and computer vision systems to validate docking and capture operations for space cargo transfer vehicles. Three applications are discussed: (1) air bearing systems in two dimensions that yield high quality free-flying, flexible, and contact dynamics; (2) validation of docking mechanisms with misalignment and target dynamics; and (3) computer vision technology for target location and real-time tracking. All the testbeds are supported by a network of engineering workstations for dynamic and controls analyses. Dynamic simulation of multibody rigid and elastic systems are performed with the TREETOPS code. MATRIXx/System-Build and PRO-MATLAB/Simulab are the tools for control design and analysis using classical and modern techniques such as H-infinity and LQG/LTR. SANDY is a general design tool to optimize numerically a multivariable robust compensator with a user-defined structure. Mathematica and Macsyma are used to derive symbolically dynamic and kinematic equations.

Predicting clinical diagnosis in Huntington's disease: An imaging polymarker

PubMed Central

Daws, Richard E.; Soreq, Eyal; Johnson, Eileanoir B.; Scahill, Rachael I.; Tabrizi, Sarah J.; Barker, Roger A.; Hampshire, Adam

2018-01-01

Objective Huntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real‐life clinical diagnosis in HD. Method A multivariate machine learning approach was applied to resting‐state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross‐group comparisons between preHD and controls, and within the preHD group in relation to “estimated” and “actual” proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy. Results Classification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models. Interpretation We propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials. Ann Neurol 2018;83:532–543 PMID:29405351

Robust Multivariable Estimation of the Relevant Information Coming from a Wheel Speed Sensor and an Accelerometer Embedded in a Car under Performance Tests

PubMed Central

Hernandez, Wilmar

2005-01-01

In the present paper, in order to estimate the response of both a wheel speed sensor and an accelerometer placed in a car under performance tests, robust and optimal multivariable estimation techniques are used. In this case, the disturbances and noises corrupting the relevant information coming from the sensors' outputs are so dangerous that their negative influence on the electrical systems impoverish the general performance of the car. In short, the solution to this problem is a safety related problem that deserves our full attention. Therefore, in order to diminish the negative effects of the disturbances and noises on the car's electrical and electromechanical systems, an optimum observer is used. The experimental results show a satisfactory improvement in the signal-to-noise ratio of the relevant signals and demonstrate the importance of the fusion of several intelligent sensor design techniques when designing the intelligent sensors that today's cars need.

Effects of intranasal oxytocin on symptoms of schizophrenia: A multivariate Bayesian meta-analysis.

PubMed

Williams, Donald R; Bürkner, Paul-Christian

2017-01-01

Schizophrenia is a heterogeneous disorder in which psychiatric symptoms are classified into two general subgroups-positive and negative symptoms. Current antipsychotic drugs are effective for treating positive symptoms, whereas negative symptoms are less responsive. Since the neuropeptide oxytocin (OT) has been shown to mediate social behavior in animals and humans, it has been used as an experimental therapeutic for treating schizophrenia and in particular negative symptoms which includes social deficits. Through eight randomized controlled trials (RCTs) and three meta-analyses, evidence for an effect of intranasal OT (IN-OT) has been inconsistent. We therefore conducted an updated meta-analysis that offers several advantages when compared to those done previously: (1) We used a multivariate analysis which allows for comparisons between symptoms and accounts for correlations between symptoms; (2) We controlled for baseline scores; (3) We used a fully Bayesian framework that allows for assessment of evidence in favor of the null hypothesis using Bayes factors; and (4) We addressed inconsistencies in the primary studies and previous meta-analyses. Eight RCTs (n=238) were included in the present study and we found that oxytocin did not improve any aspect of symptomology in schizophrenic patients and there was moderate evidence in favor of the null (no effect of oxytocin) for negative symptoms. Multivariate comparisons between symptom types revealed that oxytocin was not especially beneficial for treating negative symptoms. The effect size estimates were not moderated, publication bias was absent, and our estimates were robust to sensitivity analyses. These results suggest that IN-OT is not an effective therapeutic for schizophrenia. Copyright © 2016 Elsevier Ltd. All rights reserved.

Nonlinear fractional order proportion-integral-derivative active disturbance rejection control method design for hypersonic vehicle attitude control

NASA Astrophysics Data System (ADS)

Song, Jia; Wang, Lun; Cai, Guobiao; Qi, Xiaoqiang

2015-06-01

Near space hypersonic vehicle model is nonlinear, multivariable and couples in the reentry process, which are challenging for the controller design. In this paper, a nonlinear fractional order proportion integral derivative (NFOPIλDμ) active disturbance rejection control (ADRC) strategy based on a natural selection particle swarm (NSPSO) algorithm is proposed for the hypersonic vehicle flight control. The NFOPIλDμ ADRC method consists of a tracking-differentiator (TD), an NFOPIλDμ controller and an extended state observer (ESO). The NFOPIλDμ controller designed by combining an FOPIλDμ method and a nonlinear states error feedback control law (NLSEF) is to overcome concussion caused by the NLSEF and conversely compensate the insufficiency for relatively simple and rough signal processing caused by the FOPIλDμ method. The TD is applied to coordinate the contradiction between rapidity and overshoot. By attributing all uncertain factors to unknown disturbances, the ESO can achieve dynamic feedback compensation for these disturbances and thus reduce their effects. Simulation results show that the NFOPIλDμ ADRC method can make the hypersonic vehicle six-degree-of-freedom nonlinear model track desired nominal signals accurately and fast, has good stability, dynamic properties and strong robustness against external environmental disturbances.

A robust bayesian estimate of the concordance correlation coefficient.

PubMed

Feng, Dai; Baumgartner, Richard; Svetnik, Vladimir

2015-01-01

A need for assessment of agreement arises in many situations including statistical biomarker qualification or assay or method validation. Concordance correlation coefficient (CCC) is one of the most popular scaled indices reported in evaluation of agreement. Robust methods for CCC estimation currently present an important statistical challenge. Here, we propose a novel Bayesian method of robust estimation of CCC based on multivariate Student's t-distribution and compare it with its alternatives. Furthermore, we extend the method to practically relevant settings, enabling incorporation of confounding covariates and replications. The superiority of the new approach is demonstrated using simulation as well as real datasets from biomarker application in electroencephalography (EEG). This biomarker is relevant in neuroscience for development of treatments for insomnia.

Multivariate singular spectrum analysis and the road to phase synchronization

NASA Astrophysics Data System (ADS)

Groth, Andreas; Ghil, Michael

2010-05-01

Singular spectrum analysis (SSA) and multivariate SSA (M-SSA) are based on the classical work of Kosambi (1943), Loeve (1945) and Karhunen (1946) and are closely related to principal component analysis. They have been introduced into information theory by Bertero, Pike and co-workers (1982, 1984) and into dynamical systems analysis by Broomhead and King (1986a,b). Ghil, Vautard and associates have applied SSA and M-SSA to the temporal and spatio-temporal analysis of short and noisy time series in climate dynamics and other fields in the geosciences since the late 1980s. M-SSA provides insight into the unknown or partially known dynamics of the underlying system by decomposing the delay-coordinate phase space of a given multivariate time series into a set of data-adaptive orthonormal components. These components can be classified essentially into trends, oscillatory patterns and noise, and allow one to reconstruct a robust "skeleton" of the dynamical system's structure. For an overview we refer to Ghil et al. (Rev. Geophys., 2002). In this talk, we present M-SSA in the context of synchronization analysis and illustrate its ability to unveil information about the mechanisms behind the adjustment of rhythms in coupled dynamical systems. The focus of the talk is on the special case of phase synchronization between coupled chaotic oscillators (Rosenblum et al., PRL, 1996). Several ways of measuring phase synchronization are in use, and the robust definition of a reasonable phase for each oscillator is critical in each of them. We illustrate here the advantages of M-SSA in the automatic identification of oscillatory modes and in drawing conclusions about the transition to phase synchronization. Without using any a priori definition of a suitable phase, we show that M-SSA is able to detect phase synchronization in a chain of coupled chaotic oscillators (Osipov et al., PRE, 1996). Recently, Muller et al. (PRE, 2005) and Allefeld et al. (Intl. J. Bif. Chaos, 2007) have demonstrated the usefulness of principal component analysis in detecting phase synchronization from multivariate time series. The present talk provides a generalization of this idea and presents a robust implementation thereof via M-SSA.

Advanced Design Methodology for Robust Aircraft Sizing and Synthesis

NASA Technical Reports Server (NTRS)

Mavris, Dimitri N.

1997-01-01

Contract efforts are focused on refining the Robust Design Methodology for Conceptual Aircraft Design. Robust Design Simulation (RDS) was developed earlier as a potential solution to the need to do rapid trade-offs while accounting for risk, conflict, and uncertainty. The core of the simulation revolved around Response Surface Equations as approximations of bounded design spaces. An ongoing investigation is concerned with the advantages of using Neural Networks in conceptual design. Thought was also given to the development of systematic way to choose or create a baseline configuration based on specific mission requirements. Expert system was developed, which selects aerodynamics, performance and weights model from several configurations based on the user's mission requirements for subsonic civil transport. The research has also resulted in a step-by-step illustration on how to use the AMV method for distribution generation and the search for robust design solutions to multivariate constrained problems.

F100 multivariable control synthesis program: Evaluation of a multivariable control using a real-time engine simulation

NASA Technical Reports Server (NTRS)

Szuch, J. R.; Soeder, J. F.; Seldner, K.; Cwynar, D. S.

1977-01-01

The design, evaluation, and testing of a practical, multivariable, linear quadratic regulator control for the F100 turbofan engine were accomplished. NASA evaluation of the multivariable control logic and implementation are covered. The evaluation utilized a real time, hybrid computer simulation of the engine. Results of the evaluation are presented, and recommendations concerning future engine testing of the control are made. Results indicated that the engine testing of the control should be conducted as planned.

Designing robust control laws using genetic algorithms

NASA Technical Reports Server (NTRS)

Marrison, Chris

1994-01-01

The purpose of this research is to create a method of finding practical, robust control laws. The robustness of a controller is judged by Stochastic Robustness metrics and the level of robustness is optimized by searching for design parameters that minimize a robustness cost function.

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

NASA Technical Reports Server (NTRS)

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

2010-01-01

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

Assessing the sensitivity and robustness of prediction models for apple firmness using spectral scattering technique

USDA-ARS?s Scientific Manuscript database

Spectral scattering is useful for nondestructive sensing of fruit firmness. Prediction models, however, are typically built using multivariate statistical methods such as partial least squares regression (PLSR), whose performance generally depends on the characteristics of the data. The aim of this ...

On Restructurable Control System Theory

NASA Technical Reports Server (NTRS)

Athans, M.

1983-01-01

The state of stochastic system and control theory as it impacts restructurable control issues is addressed. The multivariable characteristics of the control problem are addressed. The failure detection/identification problem is discussed as a multi-hypothesis testing problem. Control strategy reconfiguration, static multivariable controls, static failure hypothesis testing, dynamic multivariable controls, fault-tolerant control theory, dynamic hypothesis testing, generalized likelihood ratio (GLR) methods, and adaptive control are discussed.

Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: an alternative to the skew-t distribution

PubMed Central

Lo, Kenneth

2011-01-01

Cluster analysis is the automated search for groups of homogeneous observations in a data set. A popular modeling approach for clustering is based on finite normal mixture models, which assume that each cluster is modeled as a multivariate normal distribution. However, the normality assumption that each component is symmetric is often unrealistic. Furthermore, normal mixture models are not robust against outliers; they often require extra components for modeling outliers and/or give a poor representation of the data. To address these issues, we propose a new class of distributions, multivariate t distributions with the Box-Cox transformation, for mixture modeling. This class of distributions generalizes the normal distribution with the more heavy-tailed t distribution, and introduces skewness via the Box-Cox transformation. As a result, this provides a unified framework to simultaneously handle outlier identification and data transformation, two interrelated issues. We describe an Expectation-Maximization algorithm for parameter estimation along with transformation selection. We demonstrate the proposed methodology with three real data sets and simulation studies. Compared with a wealth of approaches including the skew-t mixture model, the proposed t mixture model with the Box-Cox transformation performs favorably in terms of accuracy in the assignment of observations, robustness against model misspecification, and selection of the number of components. PMID:22125375

Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: an alternative to the skew-t distribution.

PubMed

Lo, Kenneth; Gottardo, Raphael

2012-01-01

Cluster analysis is the automated search for groups of homogeneous observations in a data set. A popular modeling approach for clustering is based on finite normal mixture models, which assume that each cluster is modeled as a multivariate normal distribution. However, the normality assumption that each component is symmetric is often unrealistic. Furthermore, normal mixture models are not robust against outliers; they often require extra components for modeling outliers and/or give a poor representation of the data. To address these issues, we propose a new class of distributions, multivariate t distributions with the Box-Cox transformation, for mixture modeling. This class of distributions generalizes the normal distribution with the more heavy-tailed t distribution, and introduces skewness via the Box-Cox transformation. As a result, this provides a unified framework to simultaneously handle outlier identification and data transformation, two interrelated issues. We describe an Expectation-Maximization algorithm for parameter estimation along with transformation selection. We demonstrate the proposed methodology with three real data sets and simulation studies. Compared with a wealth of approaches including the skew-t mixture model, the proposed t mixture model with the Box-Cox transformation performs favorably in terms of accuracy in the assignment of observations, robustness against model misspecification, and selection of the number of components.

Unemployment and prostate cancer mortality in the OECD, 1990–2009

PubMed Central

Maruthappu, Mahiben; Watkins, Johnathan; Taylor, Abigail; Williams, Callum; Ali, Raghib; Zeltner, Thomas; Atun, Rifat

2015-01-01

The global economic downturn has been associated with increased unemployment in many countries. Insights into the impact of unemployment on specific health conditions remain limited. We determined the association between unemployment and prostate cancer mortality in members of the Organisation for Economic Co-operation and Development (OECD). We used multivariate regression analysis to assess the association between changes in unemployment and prostate cancer mortality in OECD member states between 1990 and 2009. Country-specific differences in healthcare infrastructure, population structure, and population size were controlled for and lag analyses conducted. Several robustness checks were also performed. Time trend analyses were used to predict the number of excess deaths from prostate cancer following the 2008 global recession. Between 1990 and 2009, a 1% rise in unemployment was associated with an increase in prostate cancer mortality. Lag analysis showed a continued increase in mortality years after unemployment rises. The association between unemployment and prostate cancer mortality remained significant in robustness checks with 46 controls. Eight of the 21 OECD countries for which a time trend analysis was conducted, exhibited an estimated excess of prostate cancer deaths in at least one of 2008, 2009, or 2010, based on 2000–2007 trends. Rises in unemployment are associated with significant increases in prostate cancer mortality. Initiatives that bolster employment may help to minimise prostate cancer mortality during times of economic hardship. PMID:26045715

Unemployment and prostate cancer mortality in the OECD, 1990-2009.

PubMed

Maruthappu, Mahiben; Watkins, Johnathan; Taylor, Abigail; Williams, Callum; Ali, Raghib; Zeltner, Thomas; Atun, Rifat

2015-01-01

The global economic downturn has been associated with increased unemployment in many countries. Insights into the impact of unemployment on specific health conditions remain limited. We determined the association between unemployment and prostate cancer mortality in members of the Organisation for Economic Co-operation and Development (OECD). We used multivariate regression analysis to assess the association between changes in unemployment and prostate cancer mortality in OECD member states between 1990 and 2009. Country-specific differences in healthcare infrastructure, population structure, and population size were controlled for and lag analyses conducted. Several robustness checks were also performed. Time trend analyses were used to predict the number of excess deaths from prostate cancer following the 2008 global recession. Between 1990 and 2009, a 1% rise in unemployment was associated with an increase in prostate cancer mortality. Lag analysis showed a continued increase in mortality years after unemployment rises. The association between unemployment and prostate cancer mortality remained significant in robustness checks with 46 controls. Eight of the 21 OECD countries for which a time trend analysis was conducted, exhibited an estimated excess of prostate cancer deaths in at least one of 2008, 2009, or 2010, based on 2000-2007 trends. Rises in unemployment are associated with significant increases in prostate cancer mortality. Initiatives that bolster employment may help to minimise prostate cancer mortality during times of economic hardship.

Causal nexus between energy consumption and carbon dioxide emission for Malaysia using maximum entropy bootstrap approach.

PubMed

Gul, Sehrish; Zou, Xiang; Hassan, Che Hashim; Azam, Muhammad; Zaman, Khalid

2015-12-01

This study investigates the relationship between energy consumption and carbon dioxide emission in the causal framework, as the direction of causality remains has a significant policy implication for developed and developing countries. The study employed maximum entropy bootstrap (Meboot) approach to examine the causal nexus between energy consumption and carbon dioxide emission using bivariate as well as multivariate framework for Malaysia, over a period of 1975-2013. This is a unified approach without requiring the use of conventional techniques based on asymptotical theory such as testing for possible unit root and cointegration. In addition, it can be applied in the presence of non-stationary of any type including structural breaks without any type of data transformation to achieve stationary. Thus, it provides more reliable and robust inferences which are insensitive to time span as well as lag length used. The empirical results show that there is a unidirectional causality running from energy consumption to carbon emission both in the bivariate model and multivariate framework, while controlling for broad money supply and population density. The results indicate that Malaysia is an energy-dependent country and hence energy is stimulus to carbon emissions.

Multivariable control of vapor compression systems

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

He, X.D.; Liu, S.; Asada, H.H.

1999-07-01

This paper presents the results of a study of multi-input multi-output (MIMO) control of vapor compression cycles that have multiple actuators and sensors for regulating multiple outputs, e.g., superheat and evaporating temperature. The conventional single-input single-output (SISO) control was shown to have very limited performance. A low order lumped-parameter model was developed to describe the significant dynamics of vapor compression cycles. Dynamic modes were analyzed based on the low order model to provide physical insight of system dynamic behavior. To synthesize a MIMO control system, the Linear-Quadratic Gaussian (LQG) technique was applied to coordinate compressor speed and expansion valve openingmore » with guaranteed stability robustness in the design. Furthermore, to control a vapor compression cycle over a wide range of operating conditions where system nonlinearities become evident, a gain scheduling scheme was used so that the MIMO controller could adapt to changing operating conditions. Both analytical studies and experimental tests showed that the MIMO control could significantly improve the transient behavior of vapor compression cycles compared to the conventional SISO control scheme. The MIMO control proposed in this paper could be extended to the control of vapor compression cycles in a variety of HVAC and refrigeration applications to improve system performance and energy efficiency.« less

Robust tumor morphometry in multispectral fluorescence microscopy

NASA Astrophysics Data System (ADS)

Tabesh, Ali; Vengrenyuk, Yevgen; Teverovskiy, Mikhail; Khan, Faisal M.; Sapir, Marina; Powell, Douglas; Mesa-Tejada, Ricardo; Donovan, Michael J.; Fernandez, Gerardo

2009-02-01

Morphological and architectural characteristics of primary tissue compartments, such as epithelial nuclei (EN) and cytoplasm, provide important cues for cancer diagnosis, prognosis, and therapeutic response prediction. We propose two feature sets for the robust quantification of these characteristics in multiplex immunofluorescence (IF) microscopy images of prostate biopsy specimens. To enable feature extraction, EN and cytoplasm regions were first segmented from the IF images. Then, feature sets consisting of the characteristics of the minimum spanning tree (MST) connecting the EN and the fractal dimension (FD) of gland boundaries were obtained from the segmented compartments. We demonstrated the utility of the proposed features in prostate cancer recurrence prediction on a multi-institution cohort of 1027 patients. Univariate analysis revealed that both FD and one of the MST features were highly effective for predicting cancer recurrence (p <= 0.0001). In multivariate analysis, an MST feature was selected for a model incorporating clinical and image features. The model achieved a concordance index (CI) of 0.73 on the validation set, which was significantly higher than the CI of 0.69 for the standard multivariate model based solely on clinical features currently used in clinical practice (p < 0.0001). The contributions of this work are twofold. First, it is the first demonstration of the utility of the proposed features in morphometric analysis of IF images. Second, this is the largest scale study of the efficacy and robustness of the proposed features in prostate cancer prognosis.

Creation and validation of a novel body condition scoring method for the magellanic penguin (Spheniscus magellanicus) in the zoo setting.

PubMed

Clements, Julie; Sanchez, Jessica N

2015-11-01

This research aims to validate a novel, visual body scoring system created for the Magellanic penguin (Spheniscus magellanicus) suitable for the zoo practitioner. Magellanics go through marked seasonal fluctuations in body mass gains and losses. A standardized multi-variable visual body condition guide may provide a more sensitive and objective assessment tool compared to the previously used single variable method. Accurate body condition scores paired with seasonal weight variation measurements give veterinary and keeper staff a clearer understanding of an individual's nutritional status. San Francisco Zoo staff previously used a nine-point body condition scale based on the classic bird standard of a single point of keel palpation with the bird restrained in hand, with no standard measure of reference assigned to each scoring category. We created a novel, visual body condition scoring system that does not require restraint to assesses subcutaneous fat and muscle at seven body landmarks using illustrations and descriptive terms. The scores range from one, the least robust or under-conditioned, to five, the most robust, or over-conditioned. The ratio of body weight to wing length was used as a "gold standard" index of body condition and compared to both the novel multi-variable and previously used single-variable body condition scores. The novel multi-variable scale showed improved agreement with weight:wing ratio compared to the single-variable scale, demonstrating greater accuracy, and reliability when a trained assessor uses the multi-variable body condition scoring system. Zoo staff may use this tool to manage both the colony and the individual to assist in seasonally appropriate Magellanic penguin nutrition assessment. © 2015 Wiley Periodicals, Inc.

F100 Multivariable Control Synthesis Program. Computer Implementation of the F100 Multivariable Control Algorithm

NASA Technical Reports Server (NTRS)

Soeder, J. F.

1983-01-01

As turbofan engines become more complex, the development of controls necessitate the use of multivariable control techniques. A control developed for the F100-PW-100(3) turbofan engine by using linear quadratic regulator theory and other modern multivariable control synthesis techniques is described. The assembly language implementation of this control on an SEL 810B minicomputer is described. This implementation was then evaluated by using a real-time hybrid simulation of the engine. The control software was modified to run with a real engine. These modifications, in the form of sensor and actuator failure checks and control executive sequencing, are discussed. Finally recommendations for control software implementations are presented.

Multivariate statistical process control in product quality review assessment - A case study.

PubMed

Kharbach, M; Cherrah, Y; Vander Heyden, Y; Bouklouze, A

2017-11-01

According to the Food and Drug Administration and the European Good Manufacturing Practices (GMP) guidelines, Annual Product Review (APR) is a mandatory requirement in GMP. It consists of evaluating a large collection of qualitative or quantitative data in order to verify the consistency of an existing process. According to the Code of Federal Regulation Part 11 (21 CFR 211.180), all finished products should be reviewed annually for the quality standards to determine the need of any change in specification or manufacturing of drug products. Conventional Statistical Process Control (SPC) evaluates the pharmaceutical production process by examining only the effect of a single factor at the time using a Shewhart's chart. It neglects to take into account the interaction between the variables. In order to overcome this issue, Multivariate Statistical Process Control (MSPC) can be used. Our case study concerns an APR assessment, where 164 historical batches containing six active ingredients, manufactured in Morocco, were collected during one year. Each batch has been checked by assaying the six active ingredients by High Performance Liquid Chromatography according to European Pharmacopoeia monographs. The data matrix was evaluated both by SPC and MSPC. The SPC indicated that all batches are under control, while the MSPC, based on Principal Component Analysis (PCA), for the data being either autoscaled or robust scaled, showed four and seven batches, respectively, out of the Hotelling T 2 95% ellipse. Also, an improvement of the capability of the process is observed without the most extreme batches. The MSPC can be used for monitoring subtle changes in the manufacturing process during an APR assessment. Copyright © 2017 Académie Nationale de Pharmacie. Published by Elsevier Masson SAS. All rights reserved.

Correlation of Biomarker Expression in Colonic Mucosa with Disease Phenotype in Crohn's Disease and Ulcerative Colitis.

PubMed

Bruno, Maria E C; Rogier, Eric W; Arsenescu, Razvan I; Flomenhoft, Deborah R; Kurkjian, Cathryn J; Ellis, Gavin I; Kaetzel, Charlotte S

2015-10-01

Inflammatory bowel diseases (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), are characterized by chronic intestinal inflammation due to immunological, microbial, and environmental factors in genetically predisposed individuals. Advances in the diagnosis, prognosis, and treatment of IBD require the identification of robust biomarkers that can be used for molecular classification of diverse disease presentations. We previously identified five genes, RELA, TNFAIP3 (A20), PIGR, TNF, and IL8, whose mRNA levels in colonic mucosal biopsies could be used in a multivariate analysis to classify patients with CD based on disease behavior and responses to therapy. We compared expression of these five biomarkers in IBD patients classified as having CD or UC, and in healthy controls. Patients with CD were characterized as having decreased median expression of TNFAIP3, PIGR, and TNF in non-inflamed colonic mucosa as compared to healthy controls. By contrast, UC patients exhibited decreased expression of PIGR and elevated expression of IL8 in colonic mucosa compared to healthy controls. A multivariate analysis combining mRNA levels for all five genes resulted in segregation of individuals based on disease presentation (CD vs. UC) as well as severity, i.e., patients in remission versus those with acute colitis at the time of biopsy. We propose that this approach could be used as a model for molecular classification of IBD patients, which could further be enhanced by the inclusion of additional genes that are identified by functional studies, global gene expression analyses, and genome-wide association studies.

Designing a risk-based surveillance program for Mycobacterium avium ssp. paratuberculosis in Norwegian dairy herds using multivariate statistical process control analysis.

PubMed

Whist, A C; Liland, K H; Jonsson, M E; Sæbø, S; Sviland, S; Østerås, O; Norström, M; Hopp, P

2014-11-01

Surveillance programs for animal diseases are critical to early disease detection and risk estimation and to documenting a population's disease status at a given time. The aim of this study was to describe a risk-based surveillance program for detecting Mycobacterium avium ssp. paratuberculosis (MAP) infection in Norwegian dairy cattle. The included risk factors for detecting MAP were purchase of cattle, combined cattle and goat farming, and location of the cattle farm in counties containing goats with MAP. The risk indicators included production data [culling of animals >3 yr of age, carcass conformation of animals >3 yr of age, milk production decrease in older lactating cows (lactations 3, 4, and 5)], and clinical data (diarrhea, enteritis, or both, in animals >3 yr of age). Except for combined cattle and goat farming and cattle farm location, all data were collected at the cow level and summarized at the herd level. Predefined risk factors and risk indicators were extracted from different national databases and combined in a multivariate statistical process control to obtain a risk assessment for each herd. The ordinary Hotelling's T(2) statistic was applied as a multivariate, standardized measure of difference between the current observed state and the average state of the risk factors for a given herd. To make the analysis more robust and adapt it to the slowly developing nature of MAP, monthly risk calculations were based on data accumulated during a 24-mo period. Monitoring of these variables was performed to identify outliers that may indicate deviance in one or more of the underlying processes. The highest-ranked herds were scattered all over Norway and clustered in high-density dairy cattle farm areas. The resulting rankings of herds are being used in the national surveillance program for MAP in 2014 to increase the sensitivity of the ongoing surveillance program in which 5 fecal samples for bacteriological examination are collected from 25 dairy herds. The use of multivariate statistical process control for selection of herds will be beneficial when a diagnostic test suitable for mass screening is available and validated on the Norwegian cattle population, thus making it possible to increase the number of sampled herds. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

The Influence of Judgment Calls on Meta-Analytic Findings.

PubMed

Tarrahi, Farid; Eisend, Martin

2016-01-01

Previous research has suggested that judgment calls (i.e., methodological choices made in the process of conducting a meta-analysis) have a strong influence on meta-analytic findings and question their robustness. However, prior research applies case study comparison or reanalysis of a few meta-analyses with a focus on a few selected judgment calls. These studies neglect the fact that different judgment calls are related to each other and simultaneously influence the outcomes of a meta-analysis, and that meta-analytic findings can vary due to non-judgment call differences between meta-analyses (e.g., variations of effects over time). The current study analyzes the influence of 13 judgment calls in 176 meta-analyses in marketing research by applying a multivariate, multilevel meta-meta-analysis. The analysis considers simultaneous influences from different judgment calls on meta-analytic effect sizes and controls for alternative explanations based on non-judgment call differences between meta-analyses. The findings suggest that judgment calls have only a minor influence on meta-analytic findings, whereas non-judgment call differences between meta-analyses are more likely to explain differences in meta-analytic findings. The findings support the robustness of meta-analytic results and conclusions.

Robust Selectivity for Faces in the Human Amygdala in the Absence of Expressions

PubMed Central

Mende-Siedlecki, Peter; Verosky, Sara C.; Turk-Browne, Nicholas B.; Todorov, Alexander

2014-01-01

There is a well-established posterior network of cortical regions that plays a central role in face processing and that has been investigated extensively. In contrast, although responsive to faces, the amygdala is not considered a core face-selective region, and its face selectivity has never been a topic of systematic research in human neuroimaging studies. Here, we conducted a large-scale group analysis of fMRI data from 215 participants. We replicated the posterior network observed in prior studies but found equally robust and reliable responses to faces in the amygdala. These responses were detectable in most individual participants, but they were also highly sensitive to the initial statistical threshold and habituated more rapidly than the responses in posterior face-selective regions. A multivariate analysis showed that the pattern of responses to faces across voxels in the amygdala had high reliability over time. Finally, functional connectivity analyses showed stronger coupling between the amygdala and posterior face-selective regions during the perception of faces than during the perception of control visual categories. These findings suggest that the amygdala should be considered a core face-selective region. PMID:23984945

Robust Optimum Invariant Tests for Random MANOVA Models.

DTIC Science & Technology

1986-10-01

are assumed to be independent normal with zero mean and dispersion o2 and o72 respectively, Roy and Gnanadesikan (1959) considered the prob- 2 2 lem of...Part II: The multivariate case. Ann. Math. Statist. 31, 939-968. [7] Roy, S.N. and Gnanadesikan , R. (1959). Some contributions to ANOVA in one or more

Fitting Nonlinear Curves by use of Optimization Techniques

NASA Technical Reports Server (NTRS)

Hill, Scott A.

2005-01-01

MULTIVAR is a FORTRAN 77 computer program that fits one of the members of a set of six multivariable mathematical models (five of which are nonlinear) to a multivariable set of data. The inputs to MULTIVAR include the data for the independent and dependent variables plus the user s choice of one of the models, one of the three optimization engines, and convergence criteria. By use of the chosen optimization engine, MULTIVAR finds values for the parameters of the chosen model so as to minimize the sum of squares of the residuals. One of the optimization engines implements a routine, developed in 1982, that utilizes the Broydon-Fletcher-Goldfarb-Shanno (BFGS) variable-metric method for unconstrained minimization in conjunction with a one-dimensional search technique that finds the minimum of an unconstrained function by polynomial interpolation and extrapolation without first finding bounds on the solution. The second optimization engine is a faster and more robust commercially available code, denoted Design Optimization Tool, that also uses the BFGS method. The third optimization engine is a robust and relatively fast routine that implements the Levenberg-Marquardt algorithm.

A new robust adaptive controller for vibration control of active engine mount subjected to large uncertainties

NASA Astrophysics Data System (ADS)

Fakhari, Vahid; Choi, Seung-Bok; Cho, Chang-Hyun

2015-04-01

This work presents a new robust model reference adaptive control (MRAC) for vibration control caused from vehicle engine using an electromagnetic type of active engine mount. Vibration isolation performances of the active mount associated with the robust controller are evaluated in the presence of large uncertainties. As a first step, an active mount with linear solenoid actuator is prepared and its dynamic model is identified via experimental test. Subsequently, a new robust MRAC based on the gradient method with σ-modification is designed by selecting a proper reference model. In designing the robust adaptive control, structured (parametric) uncertainties in the stiffness of the passive part of the mount and in damping ratio of the active part of the mount are considered to investigate the robustness of the proposed controller. Experimental and simulation results are presented to evaluate performance focusing on the robustness behavior of the controller in the face of large uncertainties. The obtained results show that the proposed controller can sufficiently provide the robust vibration control performance even in the presence of large uncertainties showing an effective vibration isolation.

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

NASA Astrophysics Data System (ADS)

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

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

Relationship of Personality Disorders to the Course of Major Depressive Disorder in a Nationally Representative Sample

PubMed Central

Skodol, Andrew E.; Grilo, Carlos M.; Keyes, Katherine; Geier, Timothy; Grant, Bridget F.; Hasin, Deborah S.

2011-01-01

Objective The purpose of this study was to examine the effects of specific personality disorder co-morbidity on the course of major depressive disorder in a nationally-representative sample. Method Data were drawn from 1,996 participants in a national survey. Participants who met criteria for major depressive disorder at baseline in face-to-face interviews (2001–2002) were re-interviewed three years later (2004–2005) to determine persistence and recurrence. Predictors included all DSM-IV personality disorders. Control variables included demographic characteristics, other Axis I disorders, family and treatment histories, and previously established predictors of the course of major depressive disorder. Results 15.1% of participants had persistent major depressive disorder and 7.3% of those who remitted had a recurrence. Univariate analyses indicated that avoidant, borderline, histrionic, paranoid, schizoid, and schizotypal personality disorders all elevated the risk for persistence. With Axis I co-morbidity controlled, all but histrionic personality disorder remained significant. With all other personality disorders controlled, borderline and schizotypal remained significant predictors. In final, multivariate analyses that controlled for age at onset of major depressive disorder, number of previous episodes, duration of current episode, family history, and treatment, borderline personality disorder remained a robust predictor of major depressive disorder persistence. Neither personality disorders nor other clinical variables predicted recurrence. Conclusions In this nationally-representative sample of adults with major depressive disorder, borderline personality disorder robustly predicted persistence, a finding that converges with recent clinical studies. Personality psychopathology, particularly borderline personality disorder, should be assessed in all patients with major depressive disorder, considered in prognosis, and addressed in treatment. PMID:21245088

Robust output tracking control of a laboratory helicopter for automatic landing

NASA Astrophysics Data System (ADS)

Liu, Hao; Lu, Geng; Zhong, Yisheng

2014-11-01

In this paper, robust output tracking control problem of a laboratory helicopter for automatic landing in high seas is investigated. The motion of the helicopter is required to synchronise with that of an oscillating platform, e.g. the deck of a vessel subject to wave-induced motions. A robust linear time-invariant output feedback controller consisting of a nominal controller and a robust compensator is designed. The robust compensator is introduced to restrain the influences of parametric uncertainties, nonlinearities and external disturbances. It is shown that robust stability and robust tracking property can be achieved simultaneously. Experimental results on the laboratory helicopter for automatic landing demonstrate the effectiveness of the designed control approach.

What Is Robustness?: Problem Framing Challenges for Water Systems Planning Under Change

NASA Astrophysics Data System (ADS)

Herman, J. D.; Reed, P. M.; Zeff, H. B.; Characklis, G. W.

2014-12-01

Water systems planners have long recognized the need for robust solutions capable of withstanding deviations from the conditions for which they were designed. Faced with a set of alternatives to choose from—for example, resulting from a multi-objective optimization—existing analysis frameworks offer competing definitions of robustness under change. Robustness analyses have moved from expected utility to exploratory "bottom-up" approaches in which vulnerable scenarios are identified prior to assigning likelihoods; examples include Robust Decision Making (RDM), Decision Scaling, Info-Gap, and Many-Objective Robust Decision Making (MORDM). We propose a taxonomy of robustness frameworks to compare and contrast these approaches, based on their methods of (1) alternative selection, (2) sampling of states of the world, (3) quantification of robustness measures, and (4) identification of key uncertainties using sensitivity analysis. Using model simulations from recent work in multi-objective urban water supply portfolio planning, we illustrate the decision-relevant consequences that emerge from each of these choices. Results indicate that the methodological choices in the taxonomy lead to substantially different planning alternatives, underscoring the importance of an informed definition of robustness. We conclude with a set of recommendations for problem framing: that alternatives should be searched rather than prespecified; dominant uncertainties should be discovered rather than assumed; and that a multivariate satisficing measure of robustness allows stakeholders to achieve their problem-specific performance requirements. This work highlights the importance of careful problem formulation, and provides a common vocabulary to link the robustness frameworks widely used in the field of water systems planning.

Effect of interaction strength on robustness of controlling edge dynamics in complex networks

NASA Astrophysics Data System (ADS)

Pang, Shao-Peng; Hao, Fei

2018-05-01

Robustness plays a critical role in the controllability of complex networks to withstand failures and perturbations. Recent advances in the edge controllability show that the interaction strength among edges plays a more important role than network structure. Therefore, we focus on the effect of interaction strength on the robustness of edge controllability. Using three categories of all edges to quantify the robustness, we develop a universal framework to evaluate and analyze the robustness in complex networks with arbitrary structures and interaction strengths. Applying our framework to a large number of model and real-world networks, we find that the interaction strength is a dominant factor for the robustness in undirected networks. Meanwhile, the strongest robustness and the optimal edge controllability in undirected networks can be achieved simultaneously. Different from the case of undirected networks, the robustness in directed networks is determined jointly by the interaction strength and the network's degree distribution. Moreover, a stronger robustness is usually associated with a larger number of driver nodes required to maintain full control in directed networks. This prompts us to provide an optimization method by adjusting the interaction strength to optimize the robustness of edge controllability.

Robust Fixed-Structure Controller Synthesis

NASA Technical Reports Server (NTRS)

Corrado, Joseph R.; Haddad, Wassim M.; Gupta, Kajal (Technical Monitor)

2000-01-01

The ability to develop an integrated control system design methodology for robust high performance controllers satisfying multiple design criteria and real world hardware constraints constitutes a challenging task. The increasingly stringent performance specifications required for controlling such systems necessitates a trade-off between controller complexity and robustness. The principle challenge of the minimal complexity robust control design is to arrive at a tractable control design formulation in spite of the extreme complexity of such systems. Hence, design of minimal complexitY robust controllers for systems in the face of modeling errors has been a major preoccupation of system and control theorists and practitioners for the past several decades.

A robust functional-data-analysis method for data recovery in multichannel sensor systems.

PubMed

Sun, Jian; Liao, Haitao; Upadhyaya, Belle R

2014-08-01

Multichannel sensor systems are widely used in condition monitoring for effective failure prevention of critical equipment or processes. However, loss of sensor readings due to malfunctions of sensors and/or communication has long been a hurdle to reliable operations of such integrated systems. Moreover, asynchronous data sampling and/or limited data transmission are usually seen in multiple sensor channels. To reliably perform fault diagnosis and prognosis in such operating environments, a data recovery method based on functional principal component analysis (FPCA) can be utilized. However, traditional FPCA methods are not robust to outliers and their capabilities are limited in recovering signals with strongly skewed distributions (i.e., lack of symmetry). This paper provides a robust data-recovery method based on functional data analysis to enhance the reliability of multichannel sensor systems. The method not only considers the possibly skewed distribution of each channel of signal trajectories, but is also capable of recovering missing data for both individual and correlated sensor channels with asynchronous data that may be sparse as well. In particular, grand median functions, rather than classical grand mean functions, are utilized for robust smoothing of sensor signals. Furthermore, the relationship between the functional scores of two correlated signals is modeled using multivariate functional regression to enhance the overall data-recovery capability. An experimental flow-control loop that mimics the operation of coolant-flow loop in a multimodular integral pressurized water reactor is used to demonstrate the effectiveness and adaptability of the proposed data-recovery method. The computational results illustrate that the proposed method is robust to outliers and more capable than the existing FPCA-based method in terms of the accuracy in recovering strongly skewed signals. In addition, turbofan engine data are also analyzed to verify the capability of the proposed method in recovering non-skewed signals.

Qualitative and quantitative analysis of milk for the detection of adulteration by Laser Induced Breakdown Spectroscopy (LIBS).

PubMed

Moncayo, S; Manzoor, S; Rosales, J D; Anzano, J; Caceres, J O

2017-10-01

The present work focuses on the development of a fast and cost effective method based on Laser Induced Breakdown Spectroscopy (LIBS) to the quality control, traceability and detection of adulteration in milk. Two adulteration cases have been studied; a qualitative analysis for the discrimination between different milk blends and quantification of melamine in adulterated toddler milk powder. Principal Component Analysis (PCA) and neural networks (NN) have been used to analyze LIBS spectra obtaining a correct classification rate of 98% with a 100% of robustness. For the quantification of melamine, two methodologies have been developed; univariate analysis using CN emission band and multivariate calibration NN model obtaining correlation coefficient (R 2 ) values of 0.982 and 0.999 respectively. The results of the use of LIBS technique coupled with chemometric analysis are discussed in terms of its potential use in the food industry to perform the quality control of this dairy product. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluation of an F100 multivariable control using a real-time engine simulation

NASA Technical Reports Server (NTRS)

Szuch, J. R.; Skira, C.; Soeder, J. F.

1977-01-01

A multivariable control design for the F100 turbofan engine was evaluated, as part of the F100 multivariable control synthesis (MVCS) program. The evaluation utilized a real-time, hybrid computer simulation of the engine and a digital computer implementation of the control. Significant results of the evaluation are presented and recommendations concerning future engine testing of the control are made.

Macroinvertebrate Taxonomic and Functional Trait Compositions within Lotic Habitats Affected By River Restoration Practices

NASA Astrophysics Data System (ADS)

White, J. C.; Hill, M. J.; Bickerton, M. A.; Wood, P. J.

2017-09-01

The widespread degradation of lotic ecosystems has prompted extensive river restoration efforts globally, but many studies have reported modest ecological responses to rehabilitation practices. The functional properties of biotic communities are rarely examined within post-project appraisals, which would provide more ecological information underpinning ecosystem responses to restoration practices and potentially pinpoint project limitations. This study examines macroinvertebrate community responses to three projects which aimed to physically restore channel morphologies. Taxonomic and functional trait compositions supported by widely occurring lotic habitats (biotopes) were examined across paired restored and non-restored (control) reaches. The multivariate location (average community composition) of taxonomic and functional trait compositions differed marginally between control and restored reaches. However, changes in the amount of multivariate dispersion were more robust and indicated greater ecological heterogeneity within restored reaches, particularly when considering functional trait compositions. Organic biotopes (macrophyte stands and macroalgae) occurred widely across all study sites and supported a high alpha (within-habitat) taxonomic diversity compared to mineralogical biotopes (sand and gravel patches), which were characteristic of restored reaches. However, mineralogical biotopes possessed a higher beta (between-habitat) functional diversity, although this was less pronounced for taxonomic compositions. This study demonstrates that examining the functional and structural properties of taxa across distinct biotopes can provide a greater understanding of biotic responses to river restoration works. Such information could be used to better understand the ecological implications of rehabilitation practices and guide more effective management strategies.

Integrated direct/indirect adaptive robust motion trajectory tracking control of pneumatic cylinders

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Zhu, Xiaocong

2013-09-01

This paper studies the precision motion trajectory tracking control of a pneumatic cylinder driven by a proportional-directional control valve. An integrated direct/indirect adaptive robust controller is proposed. The controller employs a physical model based indirect-type parameter estimation to obtain reliable estimates of unknown model parameters, and utilises a robust control method with dynamic compensation type fast adaptation to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. Due to the use of projection mapping, the robust control law and the parameter adaption algorithm can be designed separately. Since the system model uncertainties are unmatched, the recursive backstepping technology is adopted to design the robust control law. Extensive comparative experimental results are presented to illustrate the effectiveness of the proposed controller and its performance robustness to parameter variations and sudden disturbances.

Intelligent robust control for uncertain nonlinear time-varying systems and its application to robotic systems.

PubMed

Chang, Yeong-Chan

2005-12-01

This paper addresses the problem of designing adaptive fuzzy-based (or neural network-based) robust controls for a large class of uncertain nonlinear time-varying systems. This class of systems can be perturbed by plant uncertainties, unmodeled perturbations, and external disturbances. Nonlinear H(infinity) control technique incorporated with adaptive control technique and VSC technique is employed to construct the intelligent robust stabilization controller such that an H(infinity) control is achieved. The problem of the robust tracking control design for uncertain robotic systems is employed to demonstrate the effectiveness of the developed robust stabilization control scheme. Therefore, an intelligent robust tracking controller for uncertain robotic systems in the presence of high-degree uncertainties can easily be implemented. Its solution requires only to solve a linear algebraic matrix inequality and a satisfactorily transient and asymptotical tracking performance is guaranteed. A simulation example is made to confirm the performance of the developed control algorithms.

Capital market based warning indicators of bank runs

NASA Astrophysics Data System (ADS)

Vakhtina, Elena; Wosnitza, Jan Henrik

2015-01-01

In this investigation, we examine the univariate as well as the multivariate capabilities of the log-periodic [super-exponential] power law (LPPL) for the prediction of bank runs. The research is built upon daily CDS spreads of 40 international banks for the period from June 2007 to March 2010, i.e. at the heart of the global financial crisis. For this time period, 20 of the financial institutions received federal bailouts and are labeled as defaults while the remaining institutions are categorized as non-defaults. The employed multivariate pattern recognition approach represents a modification of the CORA3 algorithm. The approach is found to be robust regardless of reasonable changes of its inputs. Despite the fact that distinct alarm indices for banks do not clearly demonstrate predictive capabilities of the LPPL, the synchronized alarm indices confirm the multivariate discriminative power of LPPL patterns in CDS spread developments acknowledged by bootstrap intervals with 70% confidence level.

Fusion And Inference From Multiple And Massive Disparate Distributed Dynamic Data Sets

DTIC Science & Technology

2017-07-01

principled methodology for two-sample graph testing; designed a provably almost-surely perfect vertex clustering algorithm for block model graphs; proved...3.7 Semi-Supervised Clustering Methodology ...................................................................... 9 3.8 Robust Hypothesis Testing...dimensional Euclidean space – allows the full arsenal of statistical and machine learning methodology for multivariate Euclidean data to be deployed for

NIRS Identification of Swietenia Macrophylla is Robust Across Specimens from 27 Countries

Treesearch

Maria C.J. Bergo; Tereza C.M. Pastore; Vera T.R. Coradin; Alex C. Wiedenhoeft; Jez W.B. Braga

2016-01-01

Big-leaf mahogany is the world’s most valuable widely traded tropical timber species and Near Infrared Spectroscopy (NIRS) has been applied as a tool for discriminating its wood from similar species using multivariate analysis. In this study four look-alike timbers of Swietenia macrophylla (mahogany or big-leaf mahogany), Carapa...

Success Stories in Control: Nonlinear Dynamic Inversion Control

NASA Technical Reports Server (NTRS)

Bosworth, John T.

2010-01-01

NASA plays an important role in advancing the state of the art in flight control systems. In the case of Nonlinear Dynamic Inversion (NDI) NASA supported initial implementation of the theory in an aircraft and demonstration in a space vehicle. Dr. Dale Enns of Honeywell Aerospace Advanced Technology performed this work in cooperation with NASA and under NASA contract. Honeywell and Lockheed Martin were subsequently contracted by AFRL to create "Design Guidelines for Multivariable Control Theory". This foundational work directly contributed to the advancement of the technology and the credibility of the control law as a design option. As a result Honeywell collaborated with Lockheed Martin to produce a Nonlinear Dynamic Inversion controller for the X-35 and subsequently Lockheed Martin did the same for the production Lockheed Martin F-35 vehicle. The theory behind NDI is to use a systematic generalized approach to controlling a vehicle. Using general aircraft nonlinear equations of motion and onboard aerodynamic, mass properties, and engine models specific to the vehicle, a relationship between control effectors and desired aircraft motion can be formulated. Using this formulation a control combination is used that provides a predictable response to commanded motion. Control loops around this formulation shape the response as desired and provide robustness to modeling errors. Once the control law is designed it can be used on a similar class of vehicle with only an update to the vehicle specific onboard models.

Feedforward/feedback control synthesis for performance and robustness

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang

1990-01-01

Both feedforward and feedback control approaches for uncertain dynamical systems are investigated. The control design objective is to achieve a fast settling time (high performance) and robustness (insensitivity) to plant modeling uncertainty. Preshapong of an ideal, time-optimal control input using a 'tapped-delay' filter is shown to provide a rapid maneuver with robust performance. A robust, non-minimum-phase feedback controller is synthesized with particular emphasis on its proper implementation for a non-zero set-point control problem. The proposed feedforward/feedback control approach is robust for a certain class of uncertain dynamical systems, since the control input command computed for a given desired output does not depend on the plant parameters.

Robust Multivariable Controller Design via Implicit Model-Following Methods.

DTIC Science & Technology

1983-12-01

followers, such as: II-7 , Ai ".......... . - ,. * . . , -. ,., 4’ . L7 XJ •:-, - J - / ( -+(Y -m)-- ( _ym + UTUc-]dt (11-13) A design method based on...Im r4 aI AI wS I I C.) 0 N 0N ON0N% I4 A’. I 0L- t-00 o% 0 ON 000 LA0 Naj UzN W4 c * r- In Go .% m * m . \\ C-. 0 cm a 0 ’no L- 00 *- \\ 14 0 0 ’-C Go N...0 t’. ~ - V’’. LALA -f . ONZ LA 41 0 \\ NL 0n I a A A N aI ’ nN? Z . =. 4 1 -I -0 I. I %a .0 1 - .a to. ot .b t o ob oto ob o11 cc-. "-f cccX dc c t I

Correlates of Illicit Drug Use Among Indigenous Peoples in Canada: A Test of Social Support Theory.

PubMed

Cao, Liqun; Burton, Velmer S; Liu, Liu

2018-02-01

Relying on a national stratified random sample of Indigenous peoples aged 19 years old and above in Canada, this study investigates the correlates of illicit drug use among Indigenous peoples, paying special attention to the association between social support measures and illegal drug use. Results from multivariate logistical regression show that measures of social support, such as residential mobility, strength of ties within communities, and lack of timely counseling, are statistically significant correlates of illicit drug use. Those identifying as Christian are significantly less likely to use illegal drugs. This is the first nationwide analysis of the illicit drug usage of Indigenous peoples in Canada. The results are robust because we have controlled for a range of comorbidity variables as well as a series of sociodemographic variables. Policy implications from these findings are discussed.

The impact of covariance misspecification in multivariate Gaussian mixtures on estimation and inference: an application to longitudinal modeling.

PubMed

Heggeseth, Brianna C; Jewell, Nicholas P

2013-07-20

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence-that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.

Hot spots of multivariate extreme anomalies in Earth observations

NASA Astrophysics Data System (ADS)

Flach, M.; Sippel, S.; Bodesheim, P.; Brenning, A.; Denzler, J.; Gans, F.; Guanche, Y.; Reichstein, M.; Rodner, E.; Mahecha, M. D.

2016-12-01

Anomalies in Earth observations might indicate data quality issues, extremes or the change of underlying processes within a highly multivariate system. Thus, considering the multivariate constellation of variables for extreme detection yields crucial additional information over conventional univariate approaches. We highlight areas in which multivariate extreme anomalies are more likely to occur, i.e. hot spots of extremes in global atmospheric Earth observations that impact the Biosphere. In addition, we present the year of the most unusual multivariate extreme between 2001 and 2013 and show that these coincide with well known high impact extremes. Technically speaking, we account for multivariate extremes by using three sophisticated algorithms adapted from computer science applications. Namely an ensemble of the k-nearest neighbours mean distance, a kernel density estimation and an approach based on recurrences is used. However, the impact of atmosphere extremes on the Biosphere might largely depend on what is considered to be normal, i.e. the shape of the mean seasonal cycle and its inter-annual variability. We identify regions with similar mean seasonality by means of dimensionality reduction in order to estimate in each region both the `normal' variance and robust thresholds for detecting the extremes. In addition, we account for challenges like heteroscedasticity in Northern latitudes. Apart from hot spot areas, those anomalies in the atmosphere time series are of particular interest, which can only be detected by a multivariate approach but not by a simple univariate approach. Such an anomalous constellation of atmosphere variables is of interest if it impacts the Biosphere. The multivariate constellation of such an anomalous part of a time series is shown in one case study indicating that multivariate anomaly detection can provide novel insights into Earth observations.

Teacher Pupil Control Ideology and Behavior as Predictors of Classroom Robustness.

ERIC Educational Resources Information Center

Estep, Linda E.; And Others

1980-01-01

It was hypothesized that confrontations between a strict teacher and misbehaving students would add drama and robustness to the classroom. In 88 secondary classrooms, robustness and teacher's control ideology and behavior were measured. The hypothesis was rejected; humanistic control behavior related to high robustness. A companion elementary…

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

PubMed

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-07-01

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

PubMed Central

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J.; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T.; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-01-01

Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco Contacts: anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153689

Robust Control Design for Systems With Probabilistic Uncertainty

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.

2005-01-01

This paper presents a reliability- and robustness-based formulation for robust control synthesis for systems with probabilistic uncertainty. In a reliability-based formulation, the probability of violating design requirements prescribed by inequality constraints is minimized. In a robustness-based formulation, a metric which measures the tendency of a random variable/process to cluster close to a target scalar/function is minimized. A multi-objective optimization procedure, which combines stability and performance requirements in time and frequency domains, is used to search for robustly optimal compensators. Some of the fundamental differences between the proposed strategy and conventional robust control methods are: (i) unnecessary conservatism is eliminated since there is not need for convex supports, (ii) the most likely plants are favored during synthesis allowing for probabilistic robust optimality, (iii) the tradeoff between robust stability and robust performance can be explored numerically, (iv) the uncertainty set is closely related to parameters with clear physical meaning, and (v) compensators with improved robust characteristics for a given control structure can be synthesized.

H∞ robust fault-tolerant controller design for an autonomous underwater vehicle's navigation control system

NASA Astrophysics Data System (ADS)

Cheng, Xiang-Qin; Qu, Jing-Yuan; Yan, Zhe-Ping; Bian, Xin-Qian

2010-03-01

In order to improve the security and reliability for autonomous underwater vehicle (AUV) navigation, an H∞ robust fault-tolerant controller was designed after analyzing variations in state-feedback gain. Operating conditions and the design method were then analyzed so that the control problem could be expressed as a mathematical optimization problem. This permitted the use of linear matrix inequalities (LMI) to solve for the H∞ controller for the system. When considering different actuator failures, these conditions were then also mathematically expressed, allowing the H∞ robust controller to solve for these events and thus be fault-tolerant. Finally, simulation results showed that the H∞ robust fault-tolerant controller could provide precise AUV navigation control with strong robustness.

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.

Development of a Comprehensive Digital Avionics Curriculum for the Aeronautical Engineer

DTIC Science & Technology

2006-03-01

able to analyze and design aircraft and missile guidance and control systems, including feedback stabilization schemes and stochastic processes, using ...Uncertainty modeling for robust control; Robust closed-loop stability and performance; Robust H- infinity control; Robustness check using mu-analysis...Controlled feedback (reduces noise) 3. Statistical group response (reduce pressure toward conformity) When used as a tool to study a complex problem

Robust control of flexible space vehicles with minimum structural excitation: On-off pulse control of flexible space vehicles

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang

1992-01-01

Both feedback and feedforward control approaches for uncertain dynamical systems (in particular, with uncertainty in structural mode frequency) are investigated. The control objective is to achieve a fast settling time (high performance) and robustness (insensitivity) to plant uncertainty. Preshaping of an ideal, time optimal control input using a tapped-delay filter is shown to provide a fast settling time with robust performance. A robust, non-minimum-phase feedback controller is synthesized with particular emphasis on its proper implementation for a non-zero set-point control problem. It is shown that a properly designed, feedback controller performs well, as compared with a time optimal open loop controller with special preshaping for performance robustness. Also included are two separate papers by the same authors on this subject.

Nonlinear robust control of hypersonic aircrafts with interactions between flight dynamics and propulsion systems.

PubMed

Li, Zhaoying; Zhou, Wenjie; Liu, Hao

2016-09-01

This paper addresses the nonlinear robust tracking controller design problem for hypersonic vehicles. This problem is challenging due to strong coupling between the aerodynamics and the propulsion system, and the uncertainties involved in the vehicle dynamics including parametric uncertainties, unmodeled model uncertainties, and external disturbances. By utilizing the feedback linearization technique, a linear tracking error system is established with prescribed references. For the linear model, a robust controller is proposed based on the signal compensation theory to guarantee that the tracking error dynamics is robustly stable. Numerical simulation results are given to show the advantages of the proposed nonlinear robust control method, compared to the robust loop-shaping control approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A Course in... Multivariable Control Methods.

ERIC Educational Resources Information Center

Deshpande, Pradeep B.

1988-01-01

Describes an engineering course for graduate study in process control. Lists four major topics: interaction analysis, multiloop controller design, decoupling, and multivariable control strategies. Suggests a course outline and gives information about each topic. (MVL)

Robustness Analysis and Optimally Robust Control Design via Sum-of-Squares

NASA Technical Reports Server (NTRS)

Dorobantu, Andrei; Crespo, Luis G.; Seiler, Peter J.

2012-01-01

A control analysis and design framework is proposed for systems subject to parametric uncertainty. The underlying strategies are based on sum-of-squares (SOS) polynomial analysis and nonlinear optimization to design an optimally robust controller. The approach determines a maximum uncertainty range for which the closed-loop system satisfies a set of stability and performance requirements. These requirements, de ned as inequality constraints on several metrics, are restricted to polynomial functions of the uncertainty. To quantify robustness, SOS analysis is used to prove that the closed-loop system complies with the requirements for a given uncertainty range. The maximum uncertainty range, calculated by assessing a sequence of increasingly larger ranges, serves as a robustness metric for the closed-loop system. To optimize the control design, nonlinear optimization is used to enlarge the maximum uncertainty range by tuning the controller gains. Hence, the resulting controller is optimally robust to parametric uncertainty. This approach balances the robustness margins corresponding to each requirement in order to maximize the aggregate system robustness. The proposed framework is applied to a simple linear short-period aircraft model with uncertain aerodynamic coefficients.

Robust H(∞) positional control of 2-DOF robotic arm driven by electro-hydraulic servo system.

PubMed

Guo, Qing; Yu, Tian; Jiang, Dan

2015-11-01

In this paper an H∞ positional feedback controller is developed to improve the robust performance under structural and parametric uncertainty disturbance in electro-hydraulic servo system (EHSS). The robust control model is described as the linear state-space equation by upper linear fractional transformation. According to the solution of H∞ sub-optimal control problem, the robust controller is designed and simplified to lower order linear model which is easily realized in EHSS. The simulation and experimental results can validate the robustness of this proposed method. The comparison result with PI control shows that the robust controller is suitable for this EHSS under the critical condition where the desired system bandwidth is higher and the external load of the hydraulic actuator is closed to its limited capability. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Panaceas, uncertainty, and the robust control framework in sustainability science

PubMed Central

Anderies, John M.; Rodriguez, Armando A.; Janssen, Marco A.; Cifdaloz, Oguzhan

2007-01-01

A critical challenge faced by sustainability science is to develop strategies to cope with highly uncertain social and ecological dynamics. This article explores the use of the robust control framework toward this end. After briefly outlining the robust control framework, we apply it to the traditional Gordon–Schaefer fishery model to explore fundamental performance–robustness and robustness–vulnerability trade-offs in natural resource management. We find that the classic optimal control policy can be very sensitive to parametric uncertainty. By exploring a large class of alternative strategies, we show that there are no panaceas: even mild robustness properties are difficult to achieve, and increasing robustness to some parameters (e.g., biological parameters) results in decreased robustness with respect to others (e.g., economic parameters). On the basis of this example, we extract some broader themes for better management of resources under uncertainty and for sustainability science in general. Specifically, we focus attention on the importance of a continual learning process and the use of robust control to inform this process. PMID:17881574

A robust fractional-order PID controller design based on active queue management for TCP network

NASA Astrophysics Data System (ADS)

Hamidian, Hamideh; Beheshti, Mohammad T. H.

2018-01-01

In this paper, a robust fractional-order controller is designed to control the congestion in transmission control protocol (TCP) networks with time-varying parameters. Fractional controllers can increase the stability and robustness. Regardless of advantages of fractional controllers, they are still not common in congestion control in TCP networks. The network parameters are time-varying, so the robust stability is important in congestion controller design. Therefore, we focused on the robust controller design. The fractional PID controller is developed based on active queue management (AQM). D-partition technique is used. The most important property of designed controller is the robustness to the time-varying parameters of the TCP network. The vertex quasi-polynomials of the closed-loop characteristic equation are obtained, and the stability boundaries are calculated for each vertex quasi-polynomial. The intersection of all stability regions is insensitive to network parameter variations, and results in robust stability of TCP/AQM system. NS-2 simulations show that the proposed algorithm provides a stable queue length. Moreover, simulations show smaller oscillations of the queue length and less packet drop probability for FPID compared to PI and PID controllers. We can conclude from NS-2 simulations that the average packet loss probability variations are negligible when the network parameters change.

A diagnostic analysis of the VVP single-doppler retrieval technique

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.

1995-01-01

A diagnostic analysis of the VVP (volume velocity processing) retrieval method is presented, with emphasis on understanding the technique as a linear, multivariate regression. Similarities and differences to the velocity-azimuth display and extended velocity-azimuth display retrieval techniques are discussed, using this framework. Conventional regression diagnostics are then employed to quantitatively determine situations in which the VVP technique is likely to fail. An algorithm for preparation and analysis of a robust VVP retrieval is developed and applied to synthetic and actual datasets with high temporal and spatial resolution. A fundamental (but quantifiable) limitation to some forms of VVP analysis is inadequate sampling dispersion in the n space of the multivariate regression, manifest as a collinearity between the basis functions of some fitted parameters. Such collinearity may be present either in the definition of these basis functions or in their realization in a given sampling configuration. This nonorthogonality may cause numerical instability, variance inflation (decrease in robustness), and increased sensitivity to bias from neglected wind components. It is shown that these effects prevent the application of VVP to small azimuthal sectors of data. The behavior of the VVP regression is further diagnosed over a wide range of sampling constraints, and reasonable sector limits are established.

Robust adaptive vibration control of a flexible structure.

PubMed

Khoshnood, A M; Moradi, H M

2014-07-01

Different types of L1 adaptive control systems show that using robust theories with adaptive control approaches has produced high performance controllers. In this study, a model reference adaptive control scheme considering robust theories is used to propose a practical control system for vibration suppression of a flexible launch vehicle (FLV). In this method, control input of the system is shaped from the dynamic model of the vehicle and components of the control input are adaptively constructed by estimating the undesirable vibration frequencies. Robust stability of the adaptive vibration control system is guaranteed by using the L1 small gain theorem. Simulation results of the robust adaptive vibration control strategy confirm that the effects of vibration on the vehicle performance considerably decrease without the loss of the phase margin of the system. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

PubMed Central

Xu, Rui; Zhen, Zonglei; Liu, Jia

2010-01-01

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. PMID:21152081

Identification and robust control of an experimental servo motor.

PubMed

Adam, E J; Guestrin, E D

2002-04-01

In this work, the design of a robust controller for an experimental laboratory-scale position control system based on a dc motor drive as well as the corresponding identification and robust stability analysis are presented. In order to carry out the robust design procedure, first, a classic closed-loop identification technique is applied and then, the parametrization by internal model control is used. The model uncertainty is evaluated under both parametric and global representation. For the latter case, an interesting discussion about the conservativeness of this description is presented by means of a comparison between the uncertainty disk and the critical perturbation radius approaches. Finally, conclusions about the performance of the experimental system with the robust controller are discussed using comparative graphics of the controlled variable and the Nyquist stability margin as a robustness measurement.

Design of multivariable feedback control systems via spectral assignment. [as applied to aircraft flight control

NASA Technical Reports Server (NTRS)

Liberty, S. R.; Mielke, R. R.; Tung, L. J.

1981-01-01

Applied research in the area of spectral assignment in multivariable systems is reported. A frequency domain technique for determining the set of all stabilizing controllers for a single feedback loop multivariable system is described. It is shown that decoupling and tracking are achievable using this procedure. The technique is illustrated with a simple example.

Design and Experimental Evaluation of a Robust Position Controller for an Electrohydrostatic Actuator Using Adaptive Antiwindup Sliding Mode Scheme

PubMed Central

Lee, Ji Min; Park, Sung Hwan; Kim, Jong Shik

2013-01-01

A robust control scheme is proposed for the position control of the electrohydrostatic actuator (EHA) when considering hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. To reduce overshoot due to a saturation of electric motor and to realize robustness against load disturbance and lumped system uncertainties such as varying parameters and modeling error, this paper proposes an adaptive antiwindup PID sliding mode scheme as a robust position controller for the EHA system. An optimal PID controller and an optimal anti-windup PID controller are also designed to compare control performance. An EHA prototype is developed, carrying out system modeling and parameter identification in designing the position controller. The simply identified linear model serves as the basis for the design of the position controllers, while the robustness of the control systems is compared by experiments. The adaptive anti-windup PID sliding mode controller has been found to have the desired performance and become robust against hardware saturation, load disturbance, and lumped system uncertainties and nonlinearities. PMID:23983640

The brain functional connectome is robustly altered by lack of sleep.

PubMed

Kaufmann, Tobias; Elvsåshagen, Torbjørn; Alnæs, Dag; Zak, Nathalia; Pedersen, Per Ø; Norbom, Linn B; Quraishi, Sophia H; Tagliazucchi, Enzo; Laufs, Helmut; Bjørnerud, Atle; Malt, Ulrik F; Andreassen, Ole A; Roussos, Evangelos; Duff, Eugene P; Smith, Stephen M; Groote, Inge R; Westlye, Lars T

2016-02-15

Sleep is a universal phenomenon necessary for maintaining homeostasis and function across a range of organs. Lack of sleep has severe health-related consequences affecting whole-body functioning, yet no other organ is as severely affected as the brain. The neurophysiological mechanisms underlying these deficits are poorly understood. Here, we characterize the dynamic changes in brain connectivity profiles inflicted by sleep deprivation and how they deviate from regular daily variability. To this end, we obtained functional magnetic resonance imaging data from 60 young, adult male participants, scanned in the morning and evening of the same day and again the following morning. 41 participants underwent total sleep deprivation before the third scan, whereas the remainder had another night of regular sleep. Sleep deprivation strongly altered the connectivity of several resting-state networks, including dorsal attention, default mode, and hippocampal networks. Multivariate classification based on connectivity profiles predicted deprivation state with high accuracy, corroborating the robustness of the findings on an individual level. Finally, correlation analysis suggested that morning-to-evening connectivity changes were reverted by sleep (control group)-a pattern which did not occur after deprivation. We conclude that both, a day of waking and a night of sleep deprivation dynamically alter the brain functional connectome. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

GC × GC-TOFMS and supervised multivariate approaches to study human cadaveric decomposition olfactive signatures.

PubMed

Stefanuto, Pierre-Hugues; Perrault, Katelynn A; Stadler, Sonja; Pesesse, Romain; LeBlanc, Helene N; Forbes, Shari L; Focant, Jean-François

2015-06-01

In forensic thanato-chemistry, the understanding of the process of soft tissue decomposition is still limited. A better understanding of the decomposition process and the characterization of the associated volatile organic compounds (VOC) can help to improve the training of victim recovery (VR) canines, which are used to search for trapped victims in natural disasters or to locate corpses during criminal investigations. The complexity of matrices and the dynamic nature of this process require the use of comprehensive analytical methods for investigation. Moreover, the variability of the environment and between individuals creates additional difficulties in terms of normalization. The resolution of the complex mixture of VOCs emitted by a decaying corpse can be improved using comprehensive two-dimensional gas chromatography (GC × GC), compared to classical single-dimensional gas chromatography (1DGC). This study combines the analytical advantages of GC × GC coupled to time-of-flight mass spectrometry (TOFMS) with the data handling robustness of supervised multivariate statistics to investigate the VOC profile of human remains during early stages of decomposition. Various supervised multivariate approaches are compared to interpret the large data set. Moreover, early decomposition stages of pig carcasses (typically used as human surrogates in field studies) are also monitored to obtain a direct comparison of the two VOC profiles and estimate the robustness of this human decomposition analog model. In this research, we demonstrate that pig and human decomposition processes can be described by the same trends for the major compounds produced during the early stages of soft tissue decomposition.

H∞ Robust Control of a Large-Piston MEMS Micromirror for Compact Fourier Transform Spectrometer Systems.

PubMed

Chen, Huipeng; Li, Mengyuan; Zhang, Yi; Xie, Huikai; Chen, Chang; Peng, Zhangming; Su, Shaohui

2018-02-08

Incorporating linear-scanning micro-electro-mechanical systems (MEMS) micromirrors into Fourier transform spectral acquisition systems can greatly reduce the size of the spectrometer equipment, making portable Fourier transform spectrometers (FTS) possible. How to minimize the tilting of the MEMS mirror plate during its large linear scan is a major problem in this application. In this work, an FTS system has been constructed based on a biaxial MEMS micromirror with a large-piston displacement of 180 μm, and a biaxial H∞ robust controller is designed. Compared with open-loop control and proportional-integral-derivative (PID) closed-loop control, H∞ robust control has good stability and robustness. The experimental results show that the stable scanning displacement reaches 110.9 μm under the H∞ robust control, and the tilting angle of the MEMS mirror plate in that full scanning range falls within ±0.0014°. Without control, the FTS system cannot generate meaningful spectra. In contrast, the FTS yields a clean spectrum with a full width at half maximum (FWHM) spectral linewidth of 96 cm -1 under the H∞ robust control. Moreover, the FTS system can maintain good stability and robustness under various driving conditions.

H∞ Robust Control of a Large-Piston MEMS Micromirror for Compact Fourier Transform Spectrometer Systems

PubMed Central

Li, Mengyuan; Zhang, Yi; Chen, Chang; Peng, Zhangming; Su, Shaohui

2018-01-01

Incorporating linear-scanning micro-electro-mechanical systems (MEMS) micromirrors into Fourier transform spectral acquisition systems can greatly reduce the size of the spectrometer equipment, making portable Fourier transform spectrometers (FTS) possible. How to minimize the tilting of the MEMS mirror plate during its large linear scan is a major problem in this application. In this work, an FTS system has been constructed based on a biaxial MEMS micromirror with a large-piston displacement of 180 μm, and a biaxial H∞ robust controller is designed. Compared with open-loop control and proportional-integral-derivative (PID) closed-loop control, H∞ robust control has good stability and robustness. The experimental results show that the stable scanning displacement reaches 110.9 μm under the H∞ robust control, and the tilting angle of the MEMS mirror plate in that full scanning range falls within ±0.0014°. Without control, the FTS system cannot generate meaningful spectra. In contrast, the FTS yields a clean spectrum with a full width at half maximum (FWHM) spectral linewidth of 96 cm−1 under the H∞ robust control. Moreover, the FTS system can maintain good stability and robustness under various driving conditions. PMID:29419765

Design and implementation of robust controllers for a gait trainer.

PubMed

Wang, F C; Yu, C H; Chou, T Y

2009-08-01

This paper applies robust algorithms to control an active gait trainer for children with walking disabilities. Compared with traditional rehabilitation procedures, in which two or three trainers are required to assist the patient, a motor-driven mechanism was constructed to improve the efficiency of the procedures. First, a six-bar mechanism was designed and constructed to mimic the trajectory of children's ankles in walking. Second, system identification techniques were applied to obtain system transfer functions at different operating points by experiments. Third, robust control algorithms were used to design Hinfinity robust controllers for the system. Finally, the designed controllers were implemented to verify experimentally the system performance. From the results, the proposed robust control strategies are shown to be effective.

Quality-by-design III: application of near-infrared spectroscopy to monitor roller compaction in-process and product quality attributes of immediate release tablets.

PubMed

Kona, Ravikanth; Fahmy, Raafat M; Claycamp, Gregg; Polli, James E; Martinez, Marilyn; Hoag, Stephen W

2015-02-01

The objective of this study is to use near-infrared spectroscopy (NIRS) coupled with multivariate chemometric models to monitor granule and tablet quality attributes in the formulation development and manufacturing of ciprofloxacin hydrochloride (CIP) immediate release tablets. Critical roller compaction process parameters, compression force (CFt), and formulation variables identified from our earlier studies were evaluated in more detail. Multivariate principal component analysis (PCA) and partial least square (PLS) models were developed during the development stage and used as a control tool to predict the quality of granules and tablets. Validated models were used to monitor and control batches manufactured at different sites to assess their robustness to change. The results showed that roll pressure (RP) and CFt played a critical role in the quality of the granules and the finished product within the range tested. Replacing binder source did not statistically influence the quality attributes of the granules and tablets. However, lubricant type has significantly impacted the granule size. Blend uniformity, crushing force, disintegration time during the manufacturing was predicted using validated PLS regression models with acceptable standard error of prediction (SEP) values, whereas the models resulted in higher SEP for batches obtained from different manufacturing site. From this study, we were able to identify critical factors which could impact the quality attributes of the CIP IR tablets. In summary, we demonstrated the ability of near-infrared spectroscopy coupled with chemometrics as a powerful tool to monitor critical quality attributes (CQA) identified during formulation development.

Robust control of combustion instabilities

NASA Astrophysics Data System (ADS)

Hong, Boe-Shong

Several interactive dynamical subsystems, each of which has its own time-scale and physical significance, are decomposed to build a feedback-controlled combustion- fluid robust dynamics. On the fast-time scale, the phenomenon of combustion instability is corresponding to the internal feedback of two subsystems: acoustic dynamics and flame dynamics, which are parametrically dependent on the slow-time-scale mean-flow dynamics controlled for global performance by a mean-flow controller. This dissertation constructs such a control system, through modeling, analysis and synthesis, to deal with model uncertainties, environmental noises and time- varying mean-flow operation. Conservation law is decomposed as fast-time acoustic dynamics and slow-time mean-flow dynamics, served for synthesizing LPV (linear parameter varying)- L2-gain robust control law, in which a robust observer is embedded for estimating and controlling the internal status, while achieving trade- offs among robustness, performances and operation. The robust controller is formulated as two LPV-type Linear Matrix Inequalities (LMIs), whose numerical solver is developed by finite-element method. Some important issues related to physical understanding and engineering application are discussed in simulated results of the control system.

Dynamic detection of window starting positions and its implementation within an activity recognition framework.

PubMed

Ni, Qin; Patterson, Timothy; Cleland, Ian; Nugent, Chris

2016-08-01

Activity recognition is an intrinsic component of many pervasive computing and ambient intelligent solutions. This has been facilitated by an explosion of technological developments in the area of wireless sensor network, wearable and mobile computing. Yet, delivering robust activity recognition, which could be deployed at scale in a real world environment, still remains an active research challenge. Much of the existing literature to date has focused on applying machine learning techniques to pre-segmented data collected in controlled laboratory environments. Whilst this approach can provide valuable ground truth information from which to build recognition models, these techniques often do not function well when implemented in near real time applications. This paper presents the application of a multivariate online change detection algorithm to dynamically detect the starting position of windows for the purposes of activity recognition. Copyright © 2016 Elsevier Inc. All rights reserved.

Rapid neural discrimination of communicative gestures

PubMed Central

Carlson, Thomas A.

2015-01-01

Humans are biased toward social interaction. Behaviorally, this bias is evident in the rapid effects that self-relevant communicative signals have on attention and perceptual systems. The processing of communicative cues recruits a wide network of brain regions, including mentalizing systems. Relatively less work, however, has examined the timing of the processing of self-relevant communicative cues. In the present study, we used multivariate pattern analysis (decoding) approach to the analysis of magnetoencephalography (MEG) to study the processing dynamics of social-communicative actions. Twenty-four participants viewed images of a woman performing actions that varied on a continuum of communicative factors including self-relevance (to the participant) and emotional valence, while their brain activity was recorded using MEG. Controlling for low-level visual factors, we found early discrimination of emotional valence (70 ms) and self-relevant communicative signals (100 ms). These data offer neural support for the robust and rapid effects of self-relevant communicative cues on behavior. PMID:24958087

Analysis of ethnic disparities in workers' compensation claims using data linkage.

PubMed

Friedman, Lee S; Ruestow, Peter; Forst, Linda

2012-10-01

The overall goal of this research project was to assess ethnic disparities in monetary compensation among construction workers injured on the job through the linkage of medical records and workers' compensation data. Probabilistic linkage of medical records with workers' compensation claim data. In the final multivariable robust regression model, compensation was $5824 higher (P = 0.030; 95% confidence interval: 551 to 11,097) for white non-Hispanic workers than for other ethnic groups when controlling for injury severity, affected body region, type of injury, average weekly wage, weeks of temporary total disability, percent permanent partial disability, death, or attorney use. The analysis indicates that white non-Hispanic construction workers are awarded higher monetary settlements despite the observation that for specific injuries the mean temporary total disability and permanent partial disability were equivalent to or lower than those in Hispanic and black construction workers.

Centralized PI control for high dimensional multivariable systems based on equivalent transfer function.

PubMed

Luan, Xiaoli; Chen, Qiang; Liu, Fei

2014-09-01

This article presents a new scheme to design full matrix controller for high dimensional multivariable processes based on equivalent transfer function (ETF). Differing from existing ETF method, the proposed ETF is derived directly by exploiting the relationship between the equivalent closed-loop transfer function and the inverse of open-loop transfer function. Based on the obtained ETF, the full matrix controller is designed utilizing the existing PI tuning rules. The new proposed ETF model can more accurately represent the original processes. Furthermore, the full matrix centralized controller design method proposed in this paper is applicable to high dimensional multivariable systems with satisfactory performance. Comparison with other multivariable controllers shows that the designed ETF based controller is superior with respect to design-complexity and obtained performance. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive Critic Nonlinear Robust Control: A Survey.

PubMed

Wang, Ding; He, Haibo; Liu, Derong

2017-10-01

Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear H ∞ control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.

Generalized internal model robust control for active front steering intervention

NASA Astrophysics Data System (ADS)

Wu, Jian; Zhao, Youqun; Ji, Xuewu; Liu, Yahui; Zhang, Lipeng

2015-03-01

Because of the tire nonlinearity and vehicle's parameters' uncertainties, robust control methods based on the worst cases, such as H ∞, µ synthesis, have been widely used in active front steering control, however, in order to guarantee the stability of active front steering system (AFS) controller, the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control. In this paper, a generalized internal model robust control (GIMC) that can overcome the contradiction between performance and stability is used in the AFS control. In GIMC, the Youla parameterization is used in an improved way. And GIMC controller includes two sections: a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters' uncertainties and some external disturbances. Simulations of double lane change (DLC) maneuver and that of braking on split- µ road are conducted to compare the performance and stability of the GIMC control, the nominal performance PID controller and the H ∞ controller. Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle's parameters variations, H ∞ controller is conservative so that the performance is a little low, and only the GIMC controller overcomes the contradiction between performance and robustness, which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller. Therefore, the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system, that is, can solve the instability of PID or LQP control methods and the low performance of the standard H ∞ controller.

Effect of intermittent feedback control on robustness of human-like postural control system

NASA Astrophysics Data System (ADS)

Tanabe, Hiroko; Fujii, Keisuke; Suzuki, Yasuyuki; Kouzaki, Motoki

2016-03-01

Humans have to acquire postural robustness to maintain stability against internal and external perturbations. Human standing has been recently modelled using an intermittent feedback control. However, the causality inside of the closed-loop postural control system associated with the neural control strategy is still unknown. Here, we examined the effect of intermittent feedback control on postural robustness and of changes in active/passive components on joint coordinative structure. We implemented computer simulation of a quadruple inverted pendulum that is mechanically close to human tiptoe standing. We simulated three pairs of joint viscoelasticity and three choices of neural control strategies for each joint: intermittent, continuous, or passive control. We examined postural robustness for each parameter set by analysing the region of active feedback gain. We found intermittent control at the hip joint was necessary for model stabilisation and model parameters affected the robustness of the pendulum. Joint sways of the pendulum model were partially smaller than or similar to those of experimental data. In conclusion, intermittent feedback control was necessary for the stabilisation of the quadruple inverted pendulum. Also, postural robustness of human-like multi-link standing would be achieved by both passive joint viscoelasticity and neural joint control strategies.

Effect of intermittent feedback control on robustness of human-like postural control system.

PubMed

Tanabe, Hiroko; Fujii, Keisuke; Suzuki, Yasuyuki; Kouzaki, Motoki

2016-03-02

Humans have to acquire postural robustness to maintain stability against internal and external perturbations. Human standing has been recently modelled using an intermittent feedback control. However, the causality inside of the closed-loop postural control system associated with the neural control strategy is still unknown. Here, we examined the effect of intermittent feedback control on postural robustness and of changes in active/passive components on joint coordinative structure. We implemented computer simulation of a quadruple inverted pendulum that is mechanically close to human tiptoe standing. We simulated three pairs of joint viscoelasticity and three choices of neural control strategies for each joint: intermittent, continuous, or passive control. We examined postural robustness for each parameter set by analysing the region of active feedback gain. We found intermittent control at the hip joint was necessary for model stabilisation and model parameters affected the robustness of the pendulum. Joint sways of the pendulum model were partially smaller than or similar to those of experimental data. In conclusion, intermittent feedback control was necessary for the stabilisation of the quadruple inverted pendulum. Also, postural robustness of human-like multi-link standing would be achieved by both passive joint viscoelasticity and neural joint control strategies.

Effect of intermittent feedback control on robustness of human-like postural control system

PubMed Central

Tanabe, Hiroko; Fujii, Keisuke; Suzuki, Yasuyuki; Kouzaki, Motoki

2016-01-01

Humans have to acquire postural robustness to maintain stability against internal and external perturbations. Human standing has been recently modelled using an intermittent feedback control. However, the causality inside of the closed-loop postural control system associated with the neural control strategy is still unknown. Here, we examined the effect of intermittent feedback control on postural robustness and of changes in active/passive components on joint coordinative structure. We implemented computer simulation of a quadruple inverted pendulum that is mechanically close to human tiptoe standing. We simulated three pairs of joint viscoelasticity and three choices of neural control strategies for each joint: intermittent, continuous, or passive control. We examined postural robustness for each parameter set by analysing the region of active feedback gain. We found intermittent control at the hip joint was necessary for model stabilisation and model parameters affected the robustness of the pendulum. Joint sways of the pendulum model were partially smaller than or similar to those of experimental data. In conclusion, intermittent feedback control was necessary for the stabilisation of the quadruple inverted pendulum. Also, postural robustness of human-like multi-link standing would be achieved by both passive joint viscoelasticity and neural joint control strategies. PMID:26931281

Real-time control systems: feedback, scheduling and robustness

NASA Astrophysics Data System (ADS)

Simon, Daniel; Seuret, Alexandre; Sename, Olivier

2017-08-01

The efficient control of real-time distributed systems, where continuous components are governed through digital devices and communication networks, needs a careful examination of the constraints arising from the different involved domains inside co-design approaches. Thanks to the robustness of feedback control, both new control methodologies and slackened real-time scheduling schemes are proposed beyond the frontiers between these traditionally separated fields. A methodology to design robust aperiodic controllers is provided, where the sampling interval is considered as a control variable of the system. Promising experimental results are provided to show the feasibility and robustness of the approach.

Multivariate Analysis of Seismic Field Data

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

Alam, M. Kathleen

1999-06-01

This report includes the details of the model building procedure and prediction of seismic field data. Principal Components Regression, a multivariate analysis technique, was used to model seismic data collected as two pieces of equipment were cycled on and off. Models built that included only the two pieces of equipment of interest had trouble predicting data containing signals not included in the model. Evidence for poor predictions came from the prediction curves as well as spectral F-ratio plots. Once the extraneous signals were included in the model, predictions improved dramatically. While Principal Components Regression performed well for the present datamore » sets, the present data analysis suggests further work will be needed to develop more robust modeling methods as the data become more complex.« less

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

Robustness of a distributed neural network controller for locomotion in a hexapod robot

NASA Technical Reports Server (NTRS)

Chiel, Hillel J.; Beer, Randall D.; Quinn, Roger D.; Espenschied, Kenneth S.

1992-01-01

A distributed neural-network controller for locomotion, based on insect neurobiology, has been used to control a hexapod robot. How robust is this controller? Disabling any single sensor, effector, or central component did not prevent the robot from walking. Furthermore, statically stable gaits could be established using either sensor input or central connections. Thus, a complex interplay between central neural elements and sensor inputs is responsible for the robustness of the controller and its ability to generate a continuous range of gaits. These results suggest that biologically inspired neural-network controllers may be a robust method for robotic control.

Assessing Principal Component Regression Prediction of Neurochemicals Detected with Fast-Scan Cyclic Voltammetry

PubMed Central

2011-01-01

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook’s distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards. PMID:21966586

Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry.

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

Principal component regression is a multivariate data analysis approach routinely used to predict neurochemical concentrations from in vivo fast-scan cyclic voltammetry measurements. This mathematical procedure can rapidly be employed with present day computer programming languages. Here, we evaluate several methods that can be used to evaluate and improve multivariate concentration determination. The cyclic voltammetric representation of the calculated regression vector is shown to be a valuable tool in determining whether the calculated multivariate model is chemically appropriate. The use of Cook's distance successfully identified outliers contained within in vivo fast-scan cyclic voltammetry training sets. This work also presents the first direct interpretation of a residual color plot and demonstrated the effect of peak shifts on predicted dopamine concentrations. Finally, separate analyses of smaller increments of a single continuous measurement could not be concatenated without substantial error in the predicted neurochemical concentrations due to electrode drift. Taken together, these tools allow for the construction of more robust multivariate calibration models and provide the first approach to assess the predictive ability of a procedure that is inherently impossible to validate because of the lack of in vivo standards.

Robust nonlinear control of vectored thrust aircraft

NASA Technical Reports Server (NTRS)

Doyle, John C.; Murray, Richard; Morris, John

1993-01-01

An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations.

Non-fragile multivariable PID controller design via system augmentation

NASA Astrophysics Data System (ADS)

Liu, Jinrong; Lam, James; Shen, Mouquan; Shu, Zhan

2017-07-01

In this paper, the issue of designing non-fragile H∞ multivariable proportional-integral-derivative (PID) controllers with derivative filters is investigated. In order to obtain the controller gains, the original system is associated with an extended system such that the PID controller design can be formulated as a static output-feedback control problem. By taking the system augmentation approach, the conditions with slack matrices for solving the non-fragile H∞ multivariable PID controller gains are established. Based on the results, linear matrix inequality -based iterative algorithms are provided to compute the controller gains. Simulations are conducted to verify the effectiveness of the proposed approaches.

Optimal robust control strategy of a solid oxide fuel cell system

NASA Astrophysics Data System (ADS)

Wu, Xiaojuan; Gao, Danhui

2018-01-01

Optimal control can ensure system safe operation with a high efficiency. However, only a few papers discuss optimal control strategies for solid oxide fuel cell (SOFC) systems. Moreover, the existed methods ignore the impact of parameter uncertainty on system instantaneous performance. In real SOFC systems, several parameters may vary with the variation of operation conditions and can not be identified exactly, such as load current. Therefore, a robust optimal control strategy is proposed, which involves three parts: a SOFC model with parameter uncertainty, a robust optimizer and robust controllers. During the model building process, boundaries of the uncertain parameter are extracted based on Monte Carlo algorithm. To achieve the maximum efficiency, a two-space particle swarm optimization approach is employed to obtain optimal operating points, which are used as the set points of the controllers. To ensure the SOFC safe operation, two feed-forward controllers and a higher-order robust sliding mode controller are presented to control fuel utilization ratio, air excess ratio and stack temperature afterwards. The results show the proposed optimal robust control method can maintain the SOFC system safe operation with a maximum efficiency under load and uncertainty variations.

Robust on-off pulse control of flexible space vehicles

NASA Technical Reports Server (NTRS)

Wie, Bong; Sinha, Ravi

1993-01-01

The on-off reaction jet control system is often used for attitude and orbital maneuvering of various spacecraft. Future space vehicles such as the orbital transfer vehicles, orbital maneuvering vehicles, and space station will extensively use reaction jets for orbital maneuvering and attitude stabilization. The proposed robust fuel- and time-optimal control algorithm is used for a three-mass spacing model of flexible spacecraft. A fuel-efficient on-off control logic is developed for robust rest-to-rest maneuver of a flexible vehicle with minimum excitation of structural modes. The first part of this report is concerned with the problem of selecting a proper pair of jets for practical trade-offs among the maneuvering time, fuel consumption, structural mode excitation, and performance robustness. A time-optimal control problem subject to parameter robustness constraints is formulated and solved. The second part of this report deals with obtaining parameter insensitive fuel- and time- optimal control inputs by solving a constrained optimization problem subject to robustness constraints. It is shown that sensitivity to modeling errors can be significantly reduced by the proposed, robustified open-loop control approach. The final part of this report deals with sliding mode control design for uncertain flexible structures. The benchmark problem of a flexible structure is used as an example for the feedback sliding mode controller design with bounded control inputs and robustness to parameter variations is investigated.

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

PubMed

Huang, X N; Ren, H P

2016-05-13

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

A Piloted Evaluation of Damage Accommodating Flight Control Using a Remotely Piloted Vehicle

NASA Technical Reports Server (NTRS)

Cunningham, Kevin; Cox, David E.; Murri, Daniel G.; Riddick, Stephen E.

2011-01-01

Toward the goal of reducing the fatal accident rate of large transport airplanes due to loss of control, the NASA Aviation Safety Program has conducted research into flight control technologies that can provide resilient control of airplanes under adverse flight conditions, including damage and failure. As part of the safety program s Integrated Resilient Aircraft Control Project, the NASA Airborne Subscale Transport Aircraft Research system was designed to address the challenges associated with the safe and efficient subscale flight testing of research control laws under adverse flight conditions. This paper presents the results of a series of pilot evaluations of several flight control algorithms used during an offset-to-landing task conducted at altitude. The purpose of this investigation was to assess the ability of various flight control technologies to prevent loss of control as stability and control characteristics were degraded. During the course of 8 research flights, data were recorded while one task was repeatedly executed by a single evaluation pilot. Two generic failures, which degraded stability and control characteristics, were simulated inflight for each of the 9 different flight control laws that were tested. The flight control laws included three different adaptive control methodologies, several linear multivariable designs, a linear robust design, a linear stability augmentation system, and a direct open-loop control mode. Based on pilot Cooper-Harper Ratings obtained for this test, the adaptive flight control laws provided the greatest overall benefit for the stability and control degradation scenarios that were considered. Also, all controllers tested provided a significant improvement in handling qualities over the direct open-loop control mode.

Robust Control for Microgravity Vibration Isolation using Fixed Order, Mixed H2/Mu Design

NASA Technical Reports Server (NTRS)

Whorton, Mark

2003-01-01

Many space-science experiments need an active isolation system to provide a sufficiently quiescent microgravity environment. Modern control methods provide the potential for both high-performance and robust stability in the presence of parametric uncertainties that are characteristic of microgravity vibration isolation systems. While H2 and H(infinity) methods are well established, neither provides the levels of attenuation performance and robust stability in a compensator with low order. Mixed H2/H(infinity), controllers provide a means for maximizing robust stability for a given level of mean-square nominal performance while directly optimizing for controller order constraints. This paper demonstrates the benefit of mixed norm design from the perspective of robustness to parametric uncertainties and controller order for microgravity vibration isolation. A nominal performance metric analogous to the mu measure, for robust stability assessment is also introduced in order to define an acceptable trade space from which different control methodologies can be compared.

A Robust H ∞ Controller for an UAV Flight Control System.

PubMed

López, J; Dormido, R; Dormido, S; Gómez, J P

2015-01-01

The objective of this paper is the implementation and validation of a robust H ∞ controller for an UAV to track all types of manoeuvres in the presence of noisy environment. A robust inner-outer loop strategy is implemented. To design the H ∞ robust controller in the inner loop, H ∞ control methodology is used. The two controllers that conform the outer loop are designed using the H ∞ Loop Shaping technique. The reference vector used in the control architecture formed by vertical velocity, true airspeed, and heading angle, suggests a nontraditional way to pilot the aircraft. The simulation results show that the proposed control scheme works well despite the presence of noise and uncertainties, so the control system satisfies the requirements.

Closed-loop and robust control of quantum systems.

PubMed

Chen, Chunlin; Wang, Lin-Cheng; Wang, Yuanlong

2013-01-01

For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. This work presents a self-contained survey on the closed-loop and robust control of quantum systems, as well as a brief introduction to a selection of basic theories and methods in this research area, to provide interested readers with a general idea for further studies. In the area of closed-loop learning control of quantum systems, we survey and introduce such learning control methods as gradient-based methods, genetic algorithms (GA), and reinforcement learning (RL) methods from a unified point of view of exploring the quantum control landscapes. For the feedback control approach, the paper surveys three control strategies including Lyapunov control, measurement-based control, and coherent-feedback control. Then such topics in the field of quantum robust control as H(∞) control, sliding mode control, quantum risk-sensitive control, and quantum ensemble control are reviewed. The paper concludes with a perspective of future research directions that are likely to attract more attention.

Proteomic Biomarkers for Acute Interstitial Lung Disease in Gefitinib-Treated Japanese Lung Cancer Patients

PubMed Central

Kawakami, Takao; Nagasaka, Keiko; Takami, Sachiko; Wada, Kazuya; Tu, Hsiao-Kun; Otsuji, Makiko; Kyono, Yutaka; Dobashi, Tae; Komatsu, Yasuhiko; Kihara, Makoto; Akimoto, Shingo; Peers, Ian S.; South, Marie C.; Higenbottam, Tim; Fukuoka, Masahiro; Nakata, Koichiro; Ohe, Yuichiro; Kudoh, Shoji; Clausen, Ib Groth; Nishimura, Toshihide; Marko-Varga, György; Kato, Harubumi

2011-01-01

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10−25), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control. PMID:21799770

Advanced modelling, monitoring, and process control of bioconversion systems

NASA Astrophysics Data System (ADS)

Schmitt, Elliott C.

Production of fuels and chemicals from lignocellulosic biomass is an increasingly important area of research and industrialization throughout the world. In order to be competitive with fossil-based fuels and chemicals, maintaining cost-effectiveness is critical. Advanced process control (APC) and optimization methods could significantly reduce operating costs in the biorefining industry. Two reasons APC has previously proven challenging to implement for bioprocesses include: lack of suitable online sensor technology of key system components, and strongly nonlinear first principal models required to predict bioconversion behavior. To overcome these challenges batch fermentations with the acetogen Moorella thermoacetica were monitored with Raman spectroscopy for the conversion of real lignocellulosic hydrolysates and a kinetic model for the conversion of synthetic sugars was developed. Raman spectroscopy was shown to be effective in monitoring the fermentation of sugarcane bagasse and sugarcane straw hydrolysate, where univariate models predicted acetate concentrations with a root mean square error of prediction (RMSEP) of 1.9 and 1.0 g L-1 for bagasse and straw, respectively. Multivariate partial least squares (PLS) models were employed to predict acetate, xylose, glucose, and total sugar concentrations for both hydrolysate fermentations. The PLS models were more robust than univariate models, and yielded a percent error of approximately 5% for both sugarcane bagasse and sugarcane straw. In addition, a screening technique was discussed for improving Raman spectra of hydrolysate samples prior to collecting fermentation data. Furthermore, a mechanistic model was developed to predict batch fermentation of synthetic glucose, xylose, and a mixture of the two sugars to acetate. The models accurately described the bioconversion process with an RMSEP of approximately 1 g L-1 for each model and provided insights into how kinetic parameters changed during dual substrate fermentation with diauxic growth. Model predictive control (MPC), an advanced process control strategy, is capable of utilizing nonlinear models and sensor feedback to provide optimal input while ensuring critical process constraints are met. Using the microorganism Saccharomyces cerevisiae, a commonly used microorganism for biofuel production, and work performed with M. thermoacetica, a nonlinear MPC was implemented on a continuous membrane cell-recycle bioreactor (MCRB) for the conversion of glucose to ethanol. The dilution rate was used to control the ethanol productivity of the system will maintaining total substrate conversion above the constraint of 98%. PLS multivariate models for glucose (RMSEP 1.5 g L-1) and ethanol (RMSEP 0.4 g L-1) were robust in predicting concentrations and a mechanistic kinetic model built accurately predicted continuous fermentation behavior. A setpoint trajectory, ranging from 2 - 4.5 g L-1 h-1 for productivity was closely tracked by the fermentation system using Raman measurements and an extended Kalman filter to estimate biomass concentrations. Overall, this work was able to demonstrate an effective approach for real-time monitoring and control of a complex fermentation system.

Robust Control for The G-Limit Microgravity Vibration Isolation System

NASA Technical Reports Server (NTRS)

Whorton, Mark S.

2004-01-01

Many microgravity science experiments need an active isolation system to provide a sufficiently quiescent acceleration environment. The g-LIMIT vibration isolation system will provide isolation for Microgravity Science Glovebox experiments in the International Space Station. While standard control system technologies have been demonstrated for these applications, modern control methods have the potential for meeting performance requirements while providing robust stability in the presence of parametric uncertainties that are characteristic of microgravity vibration isolation systems. While H2 and H infinity methods are well established, neither provides the levels of attenuation performance and robust stability in a compensator with low order. Mixed H2/mu controllers provide a means for maximizing robust stability for a given level of mean-square nominal performance while directly optimizing for controller order constraints. This paper demonstrates the benefit of mixed norm design from the perspective of robustness to parametric uncertainties and controller order for microgravity vibration isolation. A nominal performance metric analogous to the mu measure for robust stability assessment is also introduced in order to define an acceptable trade space from which different control methodologies can be compared.

Robust control for uncertain structures

NASA Technical Reports Server (NTRS)

Douglas, Joel; Athans, Michael

1991-01-01

Viewgraphs on robust control for uncertain structures are presented. Topics covered include: robust linear quadratic regulator (RLQR) formulas; mismatched LQR design; RLQR design; interpretations of RLQR design; disturbance rejection; and performance comparisons: RLQR vs. mismatched LQR.

Evaluation of Ares-I Control System Robustness to Uncertain Aerodynamics and Flex Dynamics

NASA Technical Reports Server (NTRS)

Jang, Jiann-Woei; VanTassel, Chris; Bedrossian, Nazareth; Hall, Charles; Spanos, Pol

2008-01-01

This paper discusses the application of robust control theory to evaluate robustness of the Ares-I control systems. Three techniques for estimating upper and lower bounds of uncertain parameters which yield stable closed-loop response are used here: (1) Monte Carlo analysis, (2) mu analysis, and (3) characteristic frequency response analysis. All three methods are used to evaluate stability envelopes of the Ares-I control systems with uncertain aerodynamics and flex dynamics. The results show that characteristic frequency response analysis is the most effective of these methods for assessing robustness.

Robust distributed model predictive control of linear systems with structured time-varying uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Langwen; Xie, Wei; Wang, Jingcheng

2017-11-01

In this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min-max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.

Method for factor analysis of GC/MS data

DOEpatents

Van Benthem, Mark H; Kotula, Paul G; Keenan, Michael R

2012-09-11

The method of the present invention provides a fast, robust, and automated multivariate statistical analysis of gas chromatography/mass spectroscopy (GC/MS) data sets. The method can involve systematic elimination of undesired, saturated peak masses to yield data that follow a linear, additive model. The cleaned data can then be subjected to a combination of PCA and orthogonal factor rotation followed by refinement with MCR-ALS to yield highly interpretable results.

Robust control of accelerators

NASA Astrophysics Data System (ADS)

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

1991-07-01

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

Improving the realism of hydrologic model through multivariate parameter estimation

NASA Astrophysics Data System (ADS)

Rakovec, Oldrich; Kumar, Rohini; Attinger, Sabine; Samaniego, Luis

2017-04-01

Increased availability and quality of near real-time observations should improve understanding of predictive skills of hydrological models. Recent studies have shown the limited capability of river discharge data alone to adequately constrain different components of distributed model parameterizations. In this study, the GRACE satellite-based total water storage (TWS) anomaly is used to complement the discharge data with an aim to improve the fidelity of mesoscale hydrologic model (mHM) through multivariate parameter estimation. The study is conducted in 83 European basins covering a wide range of hydro-climatic regimes. The model parameterization complemented with the TWS anomalies leads to statistically significant improvements in (1) discharge simulations during low-flow period, and (2) evapotranspiration estimates which are evaluated against independent (FLUXNET) data. Overall, there is no significant deterioration in model performance for the discharge simulations when complemented by information from the TWS anomalies. However, considerable changes in the partitioning of precipitation into runoff components are noticed by in-/exclusion of TWS during the parameter estimation. A cross-validation test carried out to assess the transferability and robustness of the calibrated parameters to other locations further confirms the benefit of complementary TWS data. In particular, the evapotranspiration estimates show more robust performance when TWS data are incorporated during the parameter estimation, in comparison with the benchmark model constrained against discharge only. This study highlights the value for incorporating multiple data sources during parameter estimation to improve the overall realism of hydrologic model and its applications over large domains. Rakovec, O., Kumar, R., Attinger, S. and Samaniego, L. (2016): Improving the realism of hydrologic model functioning through multivariate parameter estimation. Water Resour. Res., 52, http://dx.doi.org/10.1002/2016WR019430

Optimization-Based Robust Nonlinear Control

DTIC Science & Technology

2006-08-01

ABSTRACT New control algorithms were developed for robust stabilization of nonlinear dynamical systems . Novel, linear matrix inequality-based synthesis...was to further advance optimization-based robust nonlinear control design, for general nonlinear systems (especially in discrete time ), for linear...Teel, IEEE Transactions on Control Systems Technology, vol. 14, no. 3, p. 398-407, May 2006. 3. "A unified framework for input-to-state stability in

A Robust H ∞ Controller for an UAV Flight Control System

PubMed Central

López, J.

2015-01-01

The objective of this paper is the implementation and validation of a robust H ∞ controller for an UAV to track all types of manoeuvres in the presence of noisy environment. A robust inner-outer loop strategy is implemented. To design the H ∞ robust controller in the inner loop, H ∞ control methodology is used. The two controllers that conform the outer loop are designed using the H ∞ Loop Shaping technique. The reference vector used in the control architecture formed by vertical velocity, true airspeed, and heading angle, suggests a nontraditional way to pilot the aircraft. The simulation results show that the proposed control scheme works well despite the presence of noise and uncertainties, so the control system satisfies the requirements. PMID:26221622

Robust fractional order sliding mode control of doubly-fed induction generator (DFIG)-based wind turbines.

PubMed

Ebrahimkhani, Sadegh

2016-07-01

Wind power plants have nonlinear dynamics and contain many uncertainties such as unknown nonlinear disturbances and parameter uncertainties. Thus, it is a difficult task to design a robust reliable controller for this system. This paper proposes a novel robust fractional-order sliding mode (FOSM) controller for maximum power point tracking (MPPT) control of doubly fed induction generator (DFIG)-based wind energy conversion system. In order to enhance the robustness of the control system, uncertainties and disturbances are estimated using a fractional order uncertainty estimator. In the proposed method a continuous control strategy is developed to achieve the chattering free fractional order sliding-mode control, and also no knowledge of the uncertainties and disturbances or their bound is assumed. The boundedness and convergence properties of the closed-loop signals are proven using Lyapunov׳s stability theory. Simulation results in the presence of various uncertainties were carried out to evaluate the effectiveness and robustness of the proposed control scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Robust Stability and Control of Multi-Body Ground Vehicles with Uncertain Dynamics and Failures

DTIC Science & Technology

2010-01-01

and N. Zhang, 2008. “Robust stability control of vehicle rollover subject to actuator time delay”. Proc. IMechE Part I: J. of systems and control ...Dynamic Systems and Control Conference, Boston, MA, Sept 2010 R.K. Yedavalli,”Robust Stability of Linear Interval Parameter Matrix Family Problem...for control coupled output regulation for a class of systems is presented. In section 2.1.7, the control design algorithm developed in section

A robust H∞ control-based hierarchical mode transition control system for plug-in hybrid electric vehicle

NASA Astrophysics Data System (ADS)

Yang, Chao; Jiao, Xiaohong; Li, Liang; Zhang, Yuanbo; Chen, Zheng

2018-01-01

To realize a fast and smooth operating mode transition process from electric driving mode to engine-on driving mode, this paper presents a novel robust hierarchical mode transition control method for a plug-in hybrid electric bus (PHEB) with pre-transmission parallel hybrid powertrain. Firstly, the mode transition process is divided into five stages to clearly describe the powertrain dynamics. Based on the dynamics models of powertrain and clutch actuating mechanism, a hierarchical control structure including two robust H∞ controllers in both upper layer and lower layer is proposed. In upper layer, the demand clutch torque can be calculated by a robust H∞controller considering the clutch engaging time and the vehicle jerk. While in lower layer a robust tracking controller with L2-gain is designed to perform the accurate position tracking control, especially when the parameters uncertainties and external disturbance occur in the clutch actuating mechanism. Simulation and hardware-in-the-loop (HIL) test are carried out in a traditional driving condition of PHEB. Results show that the proposed hierarchical control approach can obtain the good control performance: mode transition time is greatly reduced with the acceptable jerk. Meanwhile, the designed control system shows the obvious robustness with the uncertain parameters and disturbance. Therefore, the proposed approach may offer a theoretical reference for the actual vehicle controller.

Multivariate normative comparisons using an aggregated database

PubMed Central

Murre, Jaap M. J.; Huizenga, Hilde M.

2017-01-01

In multivariate normative comparisons, a patient’s profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R. PMID:28267796

Aircraft ride quality controller design using new robust root clustering theory for linear uncertain systems

NASA Technical Reports Server (NTRS)

Yedavalli, R. K.

1992-01-01

The aspect of controller design for improving the ride quality of aircraft in terms of damping ratio and natural frequency specifications on the short period dynamics is addressed. The controller is designed to be robust with respect to uncertainties in the real parameters of the control design model such as uncertainties in the dimensional stability derivatives, imperfections in actuator/sensor locations and possibly variations in flight conditions, etc. The design is based on a new robust root clustering theory developed by the author by extending the nominal root clustering theory of Gutman and Jury to perturbed matrices. The proposed methodology allows to get an explicit relationship between the parameters of the root clustering region and the uncertainty radius of the parameter space. The current literature available for robust stability becomes a special case of this unified theory. The bounds derived on the parameter perturbation for robust root clustering are then used in selecting the robust controller.

Closed-Loop and Robust Control of Quantum Systems

PubMed Central

Wang, Lin-Cheng

2013-01-01

For most practical quantum control systems, it is important and difficult to attain robustness and reliability due to unavoidable uncertainties in the system dynamics or models. Three kinds of typical approaches (e.g., closed-loop learning control, feedback control, and robust control) have been proved to be effective to solve these problems. This work presents a self-contained survey on the closed-loop and robust control of quantum systems, as well as a brief introduction to a selection of basic theories and methods in this research area, to provide interested readers with a general idea for further studies. In the area of closed-loop learning control of quantum systems, we survey and introduce such learning control methods as gradient-based methods, genetic algorithms (GA), and reinforcement learning (RL) methods from a unified point of view of exploring the quantum control landscapes. For the feedback control approach, the paper surveys three control strategies including Lyapunov control, measurement-based control, and coherent-feedback control. Then such topics in the field of quantum robust control as H ∞ control, sliding mode control, quantum risk-sensitive control, and quantum ensemble control are reviewed. The paper concludes with a perspective of future research directions that are likely to attract more attention. PMID:23997680

Robust Fuzzy Logic Stabilization with Disturbance Elimination

PubMed Central

Danapalasingam, Kumeresan A.

2014-01-01

A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design. PMID:25177713

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

NASA Technical Reports Server (NTRS)

Anderson, Bernhard H.; Keller, Dennis J.

2001-01-01

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

Generating Nonnormal Multivariate Data Using Copulas: Applications to SEM.

PubMed

Mair, Patrick; Satorra, Albert; Bentler, Peter M

2012-07-01

This article develops a procedure based on copulas to simulate multivariate nonnormal data that satisfy a prespecified variance-covariance matrix. The covariance matrix used can comply with a specific moment structure form (e.g., a factor analysis or a general structural equation model). Thus, the method is particularly useful for Monte Carlo evaluation of structural equation models within the context of nonnormal data. The new procedure for nonnormal data simulation is theoretically described and also implemented in the widely used R environment. The quality of the method is assessed by Monte Carlo simulations. A 1-sample test on the observed covariance matrix based on the copula methodology is proposed. This new test for evaluating the quality of a simulation is defined through a particular structural model specification and is robust against normality violations.

Robust Learning Control Design for Quantum Unitary Transformations.

PubMed

Wu, Chengzhi; Qi, Bo; Chen, Chunlin; Dong, Daoyi

2017-12-01

Robust control design for quantum unitary transformations has been recognized as a fundamental and challenging task in the development of quantum information processing due to unavoidable decoherence or operational errors in the experimental implementation of quantum operations. In this paper, we extend the systematic methodology of sampling-based learning control (SLC) approach with a gradient flow algorithm for the design of robust quantum unitary transformations. The SLC approach first uses a "training" process to find an optimal control strategy robust against certain ranges of uncertainties. Then a number of randomly selected samples are tested and the performance is evaluated according to their average fidelity. The approach is applied to three typical examples of robust quantum transformation problems including robust quantum transformations in a three-level quantum system, in a superconducting quantum circuit, and in a spin chain system. Numerical results demonstrate the effectiveness of the SLC approach and show its potential applications in various implementation of quantum unitary transformations.

Robust Control Systems.

DTIC Science & Technology

1981-12-01

time control system algorithms that will perform adequately (i.e., at least maintain closed-loop system stability) when ucertain parameters in the...system design models vary significantly. Such a control algorithm is said to have stability robustness-or more simply is said to be "robust". This...cas6s above, the performance is analyzed using a covariance analysis. The development of all the controllers and the performance analysis algorithms is

Model reference tracking control of an aircraft: a robust adaptive approach

NASA Astrophysics Data System (ADS)

Tanyer, Ilker; Tatlicioglu, Enver; Zergeroglu, Erkan

2017-05-01

This work presents the design and the corresponding analysis of a nonlinear robust adaptive controller for model reference tracking of an aircraft that has parametric uncertainties in its system matrices and additive state- and/or time-dependent nonlinear disturbance-like terms in its dynamics. Specifically, robust integral of the sign of the error feedback term and an adaptive term is fused with a proportional integral controller. Lyapunov-based stability analysis techniques are utilised to prove global asymptotic convergence of the output tracking error. Extensive numerical simulations are presented to illustrate the performance of the proposed robust adaptive controller.

Efficient retrieval of landscape Hessian: Forced optimal covariance adaptive learning

NASA Astrophysics Data System (ADS)

Shir, Ofer M.; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel

2014-06-01

Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (≳30) with large Hessian condition numbers (≳104). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes.

A New Quality Control Method base on IRMCD for Wind Profiler Observation towards Future Assimilation Application

NASA Astrophysics Data System (ADS)

Chen, Min; Zhang, Yu

2017-04-01

A wind profiler network with a total of 65 profiling radars was operated by the MOC/CMA in China until July 2015. In this study, a quality control procedure is constructed to incorporate the profiler data from the wind-profiling network into the local data assimilation and forecasting system (BJRUC). The procedure applies a blacklisting check that removes stations with gross errors and an outlier check that rejects data with large deviations from the background. Instead of the bi-weighting method, which has been commonly implemented in outlier elimination for one-dimensional scalar observations, an outlier elimination method is developed based on the iterated reweighted minimum covariance determinant (IRMCD) for multi-variate observations such as wind profiler data. A quality control experiment is separately performed for subsets containing profiler data tagged in parallel with/without rain flags at every 00UTC/12UTC from 20 June to 30 Sep 2015. From the results, we find that with the quality control, the frequency distributions of the differences between the observations and model background become more Gaussian-like and meet the requirements of a Gaussian distribution for data assimilation. Further intensive assessment for each quality control step reveals that the stations rejected by blacklisting contain poor data quality, and the IRMCD rejects outliers in a robust and physically reasonable manner.

Uncertainty analysis and robust trajectory linearization control of a flexible air-breathing hypersonic vehicle

NASA Astrophysics Data System (ADS)

Pu, Zhiqiang; Tan, Xiangmin; Fan, Guoliang; Yi, Jianqiang

2014-08-01

Flexible air-breathing hypersonic vehicles feature significant uncertainties which pose huge challenges to robust controller designs. In this paper, four major categories of uncertainties are analyzed, that is, uncertainties associated with flexible effects, aerodynamic parameter variations, external environmental disturbances, and control-oriented modeling errors. A uniform nonlinear uncertainty model is explored for the first three uncertainties which lumps all uncertainties together and consequently is beneficial for controller synthesis. The fourth uncertainty is additionally considered in stability analysis. Based on these analyses, the starting point of the control design is to decompose the vehicle dynamics into five functional subsystems. Then a robust trajectory linearization control (TLC) scheme consisting of five robust subsystem controllers is proposed. In each subsystem controller, TLC is combined with the extended state observer (ESO) technique for uncertainty compensation. The stability of the overall closed-loop system with the four aforementioned uncertainties and additional singular perturbations is analyzed. Particularly, the stability of nonlinear ESO is also discussed from a Liénard system perspective. At last, simulations demonstrate the great control performance and the uncertainty rejection ability of the robust scheme.

Adjustment of automatic control systems of production facilities at coal processing plants using multivariant physico- mathematical models

NASA Astrophysics Data System (ADS)

Evtushenko, V. F.; Myshlyaev, L. P.; Makarov, G. V.; Ivushkin, K. A.; Burkova, E. V.

2016-10-01

The structure of multi-variant physical and mathematical models of control system is offered as well as its application for adjustment of automatic control system (ACS) of production facilities on the example of coal processing plant.

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

NASA Astrophysics Data System (ADS)

Kobravi, Hamid-Reza; Erfanian, Abbas

2009-08-01

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

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

PubMed

Kobravi, Hamid-Reza; Erfanian, Abbas

2009-08-01

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

Tryptophan hydroxylase type 2 variants modulate severity and outcome of addictive behaviors in Parkinson's disease.

PubMed

Cilia, Roberto; Benfante, Roberta; Asselta, Rosanna; Marabini, Laura; Cereda, Emanuele; Siri, Chiara; Pezzoli, Gianni; Goldwurm, Stefano; Fornasari, Diego

2016-08-01

Impulse control disorders and compulsive medication intake may occur in a minority of patients with Parkinson's disease (PD). We hypothesize that genetic polymorphisms associated with addiction in the general population may increase the risk for addictive behaviors also in PD. Sixteen polymorphisms in candidate genes belonging to five neurotransmitter systems (dopaminergic, catecholaminergic, serotonergic, glutamatergic, opioidergic) and the BDNF were screened in 154 PD patients with addictive behaviors and 288 PD control subjects. Multivariate analysis investigated clinical and genetic predictors of outcome (remission vs. persistence/relapse) after 1 year and at the last follow-up (5.1 ± 2.5 years). Addictive behaviors were associated with tryptophan hydroxylase type 2 (TPH2) and dopamine transporter gene variants. A subsequent analysis within the group of cases showed a robust association between TPH2 genotype and the severity of addictive behaviors, which survived Bonferroni correction for multiple testing. At multivariate analysis, TPH2 genotype resulted the strongest predictor of no remission at the last follow-up (OR[95%CI], 7.4[3.27-16.78] and 13.2[3.89-44.98] in heterozygous and homozygous carriers, respectively, p < 0.001). The extent of medication dose reduction was not a predictor. TPH2 haplotype analysis confirmed the association with more severe symptoms and lower remission rates in the short- and the long-term (p < 0.005 for all analyses). The serotonergic system is likely to be involved in the pathophysiology of addictive behaviors in PD, modulating the severity of symptoms and the rate of remission at follow-up. If confirmed in larger independent cohorts, TPH2 genotype may become a useful biomarker for the identification of at-risk individuals. Copyright © 2016 Elsevier Ltd. All rights reserved.

High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia

PubMed Central

Plis, Sergey M; Sui, Jing; Lane, Terran; Roy, Sushmita; Clark, Vincent P; Potluru, Vamsi K; Huster, Rene J; Michael, Andrew; Sponheim, Scott R; Weisend, Michael P; Calhoun, Vince D

2013-01-01

Identifying the complex activity relationships present in rich, modern neuroimaging data sets remains a key challenge for neuroscience. The problem is hard because (a) the underlying spatial and temporal networks may be nonlinear and multivariate and (b) the observed data may be driven by numerous latent factors. Further, modern experiments often produce data sets containing multiple stimulus contexts or tasks processed by the same subjects. Fusing such multi-session data sets may reveal additional structure, but raises further statistical challenges. We present a novel analysis method for extracting complex activity networks from such multifaceted imaging data sets. Compared to previous methods, we choose a new point in the trade-off space, sacrificing detailed generative probability models and explicit latent variable inference in order to achieve robust estimation of multivariate, nonlinear group factors (“network clusters”). We apply our method to identify relationships of task-specific intrinsic networks in schizophrenia patients and control subjects from a large fMRI study. After identifying network-clusters characterized by within- and between-task interactions, we find significant differences between patient and control groups in interaction strength among networks. Our results are consistent with known findings of brain regions exhibiting deviations in schizophrenic patients. However, we also find high-order, nonlinear interactions that discriminate groups but that are not detected by linear, pair-wise methods. We additionally identify high-order relationships that provide new insights into schizophrenia but that have not been found by traditional univariate or second-order methods. Overall, our approach can identify key relationships that are missed by existing analysis methods, without losing the ability to find relationships that are known to be important. PMID:23876245

Robust dynamic inversion controller design and analysis (using the X-38 vehicle as a case study)

NASA Astrophysics Data System (ADS)

Ito, Daigoro

A new way to approach robust Dynamic Inversion controller synthesis is addressed in this paper. A Linear Quadratic Gaussian outer-loop controller improves the robustness of a Dynamic Inversion inner-loop controller in the presence of uncertainties. Desired dynamics are given by the dynamic compensator, which shapes the loop. The selected dynamics are based on both performance and stability robustness requirements. These requirements are straightforwardly formulated as frequency-dependent singular value bounds during synthesis of the controller. Performance and robustness of the designed controller is tested using a worst case time domain quadratic index, which is a simple but effective way to measure robustness due to parameter variation. Using this approach, a lateral-directional controller for the X-38 vehicle is designed and its robustness to parameter variations and disturbances is analyzed. It is found that if full state measurements are available, the performance of the designed lateral-directional control system, measured by the chosen cost function, improves by approximately a factor of four. Also, it is found that the designed system is stable up to a parametric variation of 1.65 standard deviation with the set of uncertainty considered. The system robustness is determined to be highly sensitive to the dihedral derivative and the roll damping coefficients. The controller analysis is extended to the nonlinear system where both control input displacements and rates are bounded. In this case, the considered nonlinear system is stable up to 48.1° in bank angle and 1.59° in sideslip angle variations, indicating it is more sensitive to variations in sideslip angle than in bank angle. This nonlinear approach is further extended for the actuator failure mode analysis. The results suggest that the designed system maintains a high level of stability in the event of aileron failure. However, only 35% or less of the original stability range is maintained for the rudder failure case. Overall, this combination of controller synthesis and robustness criteria compares well with the mu-synthesis technique. It also is readily accessible to the practicing engineer, in terms of understanding and use.

A novel robust speed controller scheme for PMBLDC motor.

PubMed

Thirusakthimurugan, P; Dananjayan, P

2007-10-01

The design of speed and position controllers for permanent magnet brushless DC motor (PMBLDC) drive remains as an open problem in the field of motor drives. A precise speed control of PMBLDC motor is complex due to nonlinear coupling between winding currents and rotor speed. In addition, the nonlinearity present in the developed torque due to magnetic saturation of the rotor further complicates this issue. This paper presents a novel control scheme to the conventional PMBLDC motor drive, which aims at improving the robustness by complete decoupling of the design besides minimizing the mutual influence among the speed and current control loops. The interesting feature of this robust control scheme is its suitability for both static and dynamic aspects. The effectiveness of the proposed robust speed control scheme is verified through simulations.

Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems.

PubMed

Wang, Ding; Liu, Derong; Zhang, Yun; Li, Hongyi

2018-01-01

In this paper, we aim to tackle the neural robust tracking control problem for a class of nonlinear systems using the adaptive critic technique. The main contribution is that a neural-network-based robust tracking control scheme is established for nonlinear systems involving matched uncertainties. The augmented system considering the tracking error and the reference trajectory is formulated and then addressed under adaptive critic optimal control formulation, where the initial stabilizing controller is not needed. The approximate control law is derived via solving the Hamilton-Jacobi-Bellman equation related to the nominal augmented system, followed by closed-loop stability analysis. The robust tracking control performance is guaranteed theoretically via Lyapunov approach and also verified through simulation illustration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Robustness of Multiple Objective Decision Analysis Preference Functions

DTIC Science & Technology

2002-06-01

p p′ : The probability of some event. ,i ip q : The probability of event . i Π : An aggregation of proportional data used in calculating a test ...statistical tests of the significance of the term and also is conducted in a multivariate framework rather than the ROSA univariate approach. A...residual error is ˆ−e = y y (45) The coefficient provides a ready indicator of the contribution for the associated variable and statistical tests

New robust bilinear least squares method for the analysis of spectral-pH matrix data.

PubMed

Goicoechea, Héctor C; Olivieri, Alejandro C

2005-07-01

A new second-order multivariate method has been developed for the analysis of spectral-pH matrix data, based on a bilinear least-squares (BLLS) model achieving the second-order advantage and handling multiple calibration standards. A simulated Monte Carlo study of synthetic absorbance-pH data allowed comparison of the newly proposed BLLS methodology with constrained parallel factor analysis (PARAFAC) and with the combination multivariate curve resolution-alternating least-squares (MCR-ALS) technique under different conditions of sample-to-sample pH mismatch and analyte-background ratio. The results indicate an improved prediction ability for the new method. Experimental data generated by measuring absorption spectra of several calibration standards of ascorbic acid and samples of orange juice were subjected to second-order calibration analysis with PARAFAC, MCR-ALS, and the new BLLS method. The results indicate that the latter method provides the best analytical results in regard to analyte recovery in samples of complex composition requiring strict adherence to the second-order advantage. Linear dependencies appear when multivariate data are produced by using the pH or a reaction time as one of the data dimensions, posing a challenge to classical multivariate calibration models. The presently discussed algorithm is useful for these latter systems.

Robust model predictive control for constrained continuous-time nonlinear systems

NASA Astrophysics Data System (ADS)

Sun, Tairen; Pan, Yongping; Zhang, Jun; Yu, Haoyong

2018-02-01

In this paper, a robust model predictive control (MPC) is designed for a class of constrained continuous-time nonlinear systems with bounded additive disturbances. The robust MPC consists of a nonlinear feedback control and a continuous-time model-based dual-mode MPC. The nonlinear feedback control guarantees the actual trajectory being contained in a tube centred at the nominal trajectory. The dual-mode MPC is designed to ensure asymptotic convergence of the nominal trajectory to zero. This paper extends current results on discrete-time model-based tube MPC and linear system model-based tube MPC to continuous-time nonlinear model-based tube MPC. The feasibility and robustness of the proposed robust MPC have been demonstrated by theoretical analysis and applications to a cart-damper springer system and a one-link robot manipulator.

Robust approximation-free prescribed performance control for nonlinear systems and its application

NASA Astrophysics Data System (ADS)

Sun, Ruisheng; Na, Jing; Zhu, Bin

2018-02-01

This paper presents a robust prescribed performance control approach and its application to nonlinear tail-controlled missile systems with unknown dynamics and uncertainties. The idea of prescribed performance function (PPF) is incorporated into the control design, such that both the steady-state and transient control performance can be strictly guaranteed. Unlike conventional PPF-based control methods, we further tailor a recently proposed systematic control design procedure (i.e. approximation-free control) using the transformed tracking error dynamics, which provides a proportional-like control action. Hence, the function approximators (e.g. neural networks, fuzzy systems) that are widely used to address the unknown nonlinearities in the nonlinear control designs are not needed. The proposed control design leads to a robust yet simplified function approximation-free control for nonlinear systems. The closed-loop system stability and the control error convergence are all rigorously proved. Finally, comparative simulations are conducted based on nonlinear missile systems to validate the improved response and the robustness of the proposed control method.

Gap-metric-based robustness analysis of nonlinear systems with full and partial feedback linearisation

NASA Astrophysics Data System (ADS)

Al-Gburi, A.; Freeman, C. T.; French, M. C.

2018-06-01

This paper uses gap metric analysis to derive robustness and performance margins for feedback linearising controllers. Distinct from previous robustness analysis, it incorporates the case of output unstructured uncertainties, and is shown to yield general stability conditions which can be applied to both stable and unstable plants. It then expands on existing feedback linearising control schemes by introducing a more general robust feedback linearising control design which classifies the system nonlinearity into stable and unstable components and cancels only the unstable plant nonlinearities. This is done in order to preserve the stabilising action of the inherently stabilising nonlinearities. Robustness and performance margins are derived for this control scheme, and are expressed in terms of bounds on the plant nonlinearities and the accuracy of the cancellation of the unstable plant nonlinearity by the controller. Case studies then confirm reduced conservatism compared with standard methods.

Robust LOD scores for variance component-based linkage analysis.

PubMed

Blangero, J; Williams, J T; Almasy, L

2000-01-01

The variance component method is now widely used for linkage analysis of quantitative traits. Although this approach offers many advantages, the importance of the underlying assumption of multivariate normality of the trait distribution within pedigrees has not been studied extensively. Simulation studies have shown that traits with leptokurtic distributions yield linkage test statistics that exhibit excessive Type I error when analyzed naively. We derive analytical formulae relating the deviation from the expected asymptotic distribution of the lod score to the kurtosis and total heritability of the quantitative trait. A simple correction constant yields a robust lod score for any deviation from normality and for any pedigree structure, and effectively eliminates the problem of inflated Type I error due to misspecification of the underlying probability model in variance component-based linkage analysis.

Workers' compensation costs among construction workers: a robust regression analysis.

PubMed

Friedman, Lee S; Forst, Linda S

2009-11-01

Workers' compensation data are an important source for evaluating costs associated with construction injuries. We describe the characteristics of injured construction workers filing claims in Illinois between 2000 and 2005 and the factors associated with compensation costs using a robust regression model. In the final multivariable model, the cumulative percent temporary and permanent disability-measures of severity of injury-explained 38.7% of the variance of cost. Attorney costs explained only 0.3% of the variance of the dependent variable. The model used in this study clearly indicated that percent disability was the most important determinant of cost, although the method and uniformity of percent impairment allocation could be better elucidated. There is a need to integrate analytical methods that are suitable for skewed data when analyzing claim costs.

Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

PubMed Central

Svečko, Rajko

2014-01-01

This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749

Neural robust stabilization via event-triggering mechanism and adaptive learning technique.

PubMed

Wang, Ding; Liu, Derong

2018-06-01

The robust control synthesis of continuous-time nonlinear systems with uncertain term is investigated via event-triggering mechanism and adaptive critic learning technique. We mainly focus on combining the event-triggering mechanism with adaptive critic designs, so as to solve the nonlinear robust control problem. This can not only make better use of computation and communication resources, but also conduct controller design from the view of intelligent optimization. Through theoretical analysis, the nonlinear robust stabilization can be achieved by obtaining an event-triggered optimal control law of the nominal system with a newly defined cost function and a certain triggering condition. The adaptive critic technique is employed to facilitate the event-triggered control design, where a neural network is introduced as an approximator of the learning phase. The performance of the event-triggered robust control scheme is validated via simulation studies and comparisons. The present method extends the application domain of both event-triggered control and adaptive critic control to nonlinear systems possessing dynamical uncertainties. Copyright © 2018 Elsevier Ltd. All rights reserved.

Optimization of robustness of interdependent network controllability by redundant design

PubMed Central

2018-01-01

Controllability of complex networks has been a hot topic in recent years. Real networks regarded as interdependent networks are always coupled together by multiple networks. The cascading process of interdependent networks including interdependent failure and overload failure will destroy the robustness of controllability for the whole network. Therefore, the optimization of the robustness of interdependent network controllability is of great importance in the research area of complex networks. In this paper, based on the model of interdependent networks constructed first, we determine the cascading process under different proportions of node attacks. Then, the structural controllability of interdependent networks is measured by the minimum driver nodes. Furthermore, we propose a parameter which can be obtained by the structure and minimum driver set of interdependent networks under different proportions of node attacks and analyze the robustness for interdependent network controllability. Finally, we optimize the robustness of interdependent network controllability by redundant design including node backup and redundancy edge backup and improve the redundant design by proposing different strategies according to their cost. Comparative strategies of redundant design are conducted to find the best strategy. Results shows that node backup and redundancy edge backup can indeed decrease those nodes suffering from failure and improve the robustness of controllability. Considering the cost of redundant design, we should choose BBS (betweenness-based strategy) or DBS (degree based strategy) for node backup and HDF(high degree first) for redundancy edge backup. Above all, our proposed strategies are feasible and effective at improving the robustness of interdependent network controllability. PMID:29438426

Health-related fitness profiles in adolescents with complex congenital heart disease.

PubMed

Klausen, Susanne Hwiid; Wetterslev, Jørn; Søndergaard, Lars; Andersen, Lars L; Mikkelsen, Ulla Ramer; Dideriksen, Kasper; Zoffmann, Vibeke; Moons, Philip

2015-04-01

This study investigates whether subgroups of different health-related fitness (HrF) profiles exist among girls and boys with complex congenital heart disease (ConHD) and how these are associated with lifestyle behaviors. We measured the cardiorespiratory fitness, muscle strength, and body composition of 158 adolescents aged 13-16 years with previous surgery for a complex ConHD. Data on lifestyle behaviors were collected concomitantly between October 2010 and April 2013. A cluster analysis was conducted to identify profiles with similar HrF. For comparisons between clusters, multivariate analyses of covariance were used to test the differences in lifestyle behaviors. Three distinct profiles were formed: (1) Robust (43, 27%; 20 girls and 23 boys); (2) Moderately Robust (85, 54%; 37 girls and 48 boys); and (3) Less robust (30, 19%; 9 girls and 21 boys). The participants in the Robust clusters reported leading a physically active lifestyle and participants in the Less robust cluster reported leading a sedentary lifestyle. Diagnoses were evenly distributed between clusters. The cluster analysis attributed some of the variability in cardiorespiratory fitness among adolescents with complex ConHD to lifestyle behaviors and physical activity. Profiling of HrF offers a valuable new option in the management of person-centered health promotion. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2005-01-01

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

Self-tuning multivariable pole placement control of a multizone crystal growth furnace

NASA Technical Reports Server (NTRS)

Batur, C.; Sharpless, R. B.; Duval, W. M. B.; Rosenthal, B. N.

1992-01-01

This paper presents the design and implementation of a multivariable self-tuning temperature controller for the control of lead bromide crystal growth. The crystal grows inside a multizone transparent furnace. There are eight interacting heating zones shaping the axial temperature distribution inside the furnace. A multi-input, multi-output furnace model is identified on-line by a recursive least squares estimation algorithm. A multivariable pole placement controller based on this model is derived and implemented. Comparison between single-input, single-output and multi-input, multi-output self-tuning controllers demonstrates that the zone-to-zone interactions can be minimized better by a multi-input, multi-output controller design. This directly affects the quality of crystal grown.

Direct adaptive robust tracking control for 6 DOF industrial robot with enhanced accuracy.

PubMed

Yin, Xiuxing; Pan, Li

2018-01-01

A direct adaptive robust tracking control is proposed for trajectory tracking of 6 DOF industrial robot in the presence of parametric uncertainties, external disturbances and uncertain nonlinearities. The controller is designed based on the dynamic characteristics in the working space of the end-effector of the 6 DOF robot. The controller includes robust control term and model compensation term that is developed directly based on the input reference or desired motion trajectory. A projection-type parametric adaptation law is also designed to compensate for parametric estimation errors for the adaptive robust control. The feasibility and effectiveness of the proposed direct adaptive robust control law and the associated projection-type parametric adaptation law have been comparatively evaluated based on two 6 DOF industrial robots. The test results demonstrate that the proposed control can be employed to better maintain the desired trajectory tracking even in the presence of large parametric uncertainties and external disturbances as compared with PD controller and nonlinear controller. The parametric estimates also eventually converge to the real values along with the convergence of tracking errors, which further validate the effectiveness of the proposed parametric adaption law. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Boosted Multivariate Trees for Longitudinal Data

PubMed Central

Pande, Amol; Li, Liang; Rajeswaran, Jeevanantham; Ehrlinger, John; Kogalur, Udaya B.; Blackstone, Eugene H.; Ishwaran, Hemant

2017-01-01

Machine learning methods provide a powerful approach for analyzing longitudinal data in which repeated measurements are observed for a subject over time. We boost multivariate trees to fit a novel flexible semi-nonparametric marginal model for longitudinal data. In this model, features are assumed to be nonparametric, while feature-time interactions are modeled semi-nonparametrically utilizing P-splines with estimated smoothing parameter. In order to avoid overfitting, we describe a relatively simple in sample cross-validation method which can be used to estimate the optimal boosting iteration and which has the surprising added benefit of stabilizing certain parameter estimates. Our new multivariate tree boosting method is shown to be highly flexible, robust to covariance misspecification and unbalanced designs, and resistant to overfitting in high dimensions. Feature selection can be used to identify important features and feature-time interactions. An application to longitudinal data of forced 1-second lung expiratory volume (FEV1) for lung transplant patients identifies an important feature-time interaction and illustrates the ease with which our method can find complex relationships in longitudinal data. PMID:29249866

Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.

PubMed

Wang, Longfei; Lee, Sungyoung; Gim, Jungsoo; Qiao, Dandi; Cho, Michael; Elston, Robert C; Silverman, Edwin K; Won, Sungho

2016-09-01

Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows. © 2016 WILEY PERIODICALS, INC.

Robust control algorithms for Mars aerobraking

NASA Technical Reports Server (NTRS)

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

1992-01-01

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

A frequency-domain estimator for use in adaptive control systems

NASA Technical Reports Server (NTRS)

Lamaire, Richard O.; Valavani, Lena; Athans, Michael; Stein, Gunter

1991-01-01

This paper presents a frequency-domain estimator that can identify both a parametrized nominal model of a plant as well as a frequency-domain bounding function on the modeling error associated with this nominal model. This estimator, which we call a robust estimator, can be used in conjunction with a robust control-law redesign algorithm to form a robust adaptive controller.

The use of singular value gradients and optimization techniques to design robust controllers for multiloop systems

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two-input/two-output drone flight control system.

The use of singular value gradients and optimization techniques to design robust controllers for multiloop systems

NASA Technical Reports Server (NTRS)

Newsom, J. R.; Mukhopadhyay, V.

1983-01-01

A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two output drone flight control system.

Robust stabilization of the Space Station in the presence of inertia matrix uncertainty

NASA Technical Reports Server (NTRS)

Wie, Bong; Liu, Qiang; Sunkel, John

1993-01-01

This paper presents a robust H-infinity full-state feedback control synthesis method for uncertain systems with D11 not equal to 0. The method is applied to the robust stabilization problem of the Space Station in the face of inertia matrix uncertainty. The control design objective is to find a robust controller that yields the largest stable hypercube in uncertain parameter space, while satisfying the nominal performance requirements. The significance of employing an uncertain plant model with D11 not equal 0 is demonstrated.

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.

Robust Constrained Optimization Approach to Control Design for International Space Station Centrifuge Rotor Auto Balancing Control System

NASA Technical Reports Server (NTRS)

Postma, Barry Dirk

2005-01-01

This thesis discusses application of a robust constrained optimization approach to control design to develop an Auto Balancing Controller (ABC) for a centrifuge rotor to be implemented on the International Space Station. The design goal is to minimize a performance objective of the system, while guaranteeing stability and proper performance for a range of uncertain plants. The Performance objective is to minimize the translational response of the centrifuge rotor due to a fixed worst-case rotor imbalance. The robustness constraints are posed with respect to parametric uncertainty in the plant. The proposed approach to control design allows for both of these objectives to be handled within the framework of constrained optimization. The resulting controller achieves acceptable performance and robustness characteristics.

Unemployment, public-sector health-care spending and breast cancer mortality in the European Union: 1990-2009.

PubMed

Maruthappu, Mahiben; Watkins, Johnathan A; Waqar, Mueez; Williams, Callum; Ali, Raghib; Atun, Rifat; Faiz, Omar; Zeltner, Thomas

2015-04-01

The global economic crisis has been associated with increased unemployment, reduced health-care spending and adverse health outcomes. Insights into the impact of economic variations on cancer mortality, however, remain limited. We used multivariate regression analysis to assess how changes in unemployment and public-sector expenditure on health care (PSEH) varied with female breast cancer mortality in the 27 European Union member states from 1990 to 2009. We then determined how the association with unemployment was modified by PSEH. Country-specific differences in infrastructure and demographic structure were controlled for, and 1-, 3-, 5- and 10-year lag analyses were conducted. Several robustness checks were also implemented. Unemployment was associated with an increase in breast cancer mortality [P < 0.0001, coefficient (R) = 0.1829, 95% confidence interval (CI) 0.0978-0.2680]. Lag analysis showed a continued increase in breast cancer mortality at 1, 3, 5 and 10 years after unemployment rises (P < 0.05). Controlling for PSEH removed this association (P = 0.063, R = 0.080, 95% CI -0.004 to 0.163). PSEH increases were associated with significant decreases in breast cancer mortality (P < 0.0001, R = -1.28, 95% CI -1.67 to -0.877). The association between unemployment and breast cancer mortality remained in all robustness checks. Rises in unemployment are associated with significant short- and long-term increases in breast cancer mortality, while increases in PSEH are associated with reductions in breast cancer mortality. Initiatives that bolster employment and maintain total health-care expenditure may help minimize increases in breast cancer mortality during economic crises. © The Author 2014. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Vehicle lateral motion regulation under unreliable communication links based on robust H∞ output-feedback control schema

NASA Astrophysics Data System (ADS)

Li, Cong; Jing, Hui; Wang, Rongrong; Chen, Nan

2018-05-01

This paper presents a robust control schema for vehicle lateral motion regulation under unreliable communication links via controller area network (CAN). The communication links between the system plant and the controller are assumed to be imperfect and therefore the data packet dropouts occur frequently. The paper takes the form of parallel distributed compensation and treats the dropouts as random binary numbers that form Bernoulli distribution. Both of the tire cornering stiffness uncertainty and external disturbances are considered to enhance the robustness of the controller. In addition, a robust H∞ static output-feedback control approach is proposed to realize the lateral motion control with relative low cost sensors. The stochastic stability of the closed-loop system and conservation of the guaranteed H∞ performance are investigated. Simulation results based on CarSim platform using a high-fidelity and full-car model verify the effectiveness of the proposed control approach.

Robust H∞ output-feedback control for path following of autonomous ground vehicles

NASA Astrophysics Data System (ADS)

Hu, Chuan; Jing, Hui; Wang, Rongrong; Yan, Fengjun; Chadli, Mohammed

2016-03-01

This paper presents a robust H∞ output-feedback control strategy for the path following of autonomous ground vehicles (AGVs). Considering the vehicle lateral velocity is usually hard to measure with low cost sensor, a robust H∞ static output-feedback controller based on the mixed genetic algorithms (GA)/linear matrix inequality (LMI) approach is proposed to realize the path following without the information of the lateral velocity. The proposed controller is robust to the parametric uncertainties and external disturbances, with the parameters including the tire cornering stiffness, vehicle longitudinal velocity, yaw rate and road curvature. Simulation results based on CarSim-Simulink joint platform using a high-fidelity and full-car model have verified the effectiveness of the proposed control approach.

Negative religious coping as a correlate of suicidal ideation in patients with advanced cancer

PubMed Central

Trevino, K. M.; Balboni, M.; Zollfrank, A.; Balboni, T.; Prigerson, H. G.

2016-01-01

Objective The purpose of this study is to examine the relationship between negative religious coping (NRC) and suicidal ideation in patients with advanced cancer, controlling for demographic and disease characteristics and risk and protective factors for suicidal ideation. Methods Adult patients with advanced cancer (life expectancy ≤6 months) were recruited from seven medical centers in the northeastern and southwestern USA (n = 603). Trained raters verbally administered the examined measures to patients upon study entry. Multivariable logistic regression analyses regressed suicidal ideation on NRC controlling for significant demographic, disease, risk, and protective factors. Results Negative religious coping was associated with an increased risk for suicidal ideation (OR, 2.65 [95% CI, 1.22, 5.74], p = 0.01) after controlling for demographic and disease characteristics, mental and physical health, self-efficacy, secular coping, social support, spiritual care received, global religiousness and spirituality, and positive religious coping. Conclusions Negative religious coping is a robust correlate of suicidal ideation. Assessment of NRC in patients with advanced cancer may identify patients experiencing spiritual distress and those at risk for suicidal ideation. Confirmation of these results in future studies would suggest the need for interventions targeting the reduction of NRC to reduce suicidal ideation among advanced cancer patients. PMID:24577802

A new look at the robust control of discrete-time Markov jump linear systems

NASA Astrophysics Data System (ADS)

Todorov, M. G.; Fragoso, M. D.

2016-03-01

In this paper, we make a foray in the role played by a set of four operators on the study of robust H2 and mixed H2/H∞ control problems for discrete-time Markov jump linear systems. These operators appear in the study of mean square stability for this class of systems. By means of new linear matrix inequality (LMI) characterisations of controllers, which include slack variables that, to some extent, separate the robustness and performance objectives, we introduce four alternative approaches to the design of controllers which are robustly stabilising and at the same time provide a guaranteed level of H2 performance. Since each operator provides a different degree of conservatism, the results are unified in the form of an iterative LMI technique for designing robust H2 controllers, whose convergence is attained in a finite number of steps. The method yields a new way of computing mixed H2/H∞ controllers, whose conservatism decreases with iteration. Two numerical examples illustrate the applicability of the proposed results for the control of a small unmanned aerial vehicle, and for an underactuated robotic arm.

Robust control for a biaxial servo with time delay system based on adaptive tuning technique.

PubMed

Chen, Tien-Chi; Yu, Chih-Hsien

2009-07-01

A robust control method for synchronizing a biaxial servo system motion is proposed in this paper. A new network based cross-coupled control and adaptive tuning techniques are used together to cancel out the skew error. The conventional fixed gain PID cross-coupled controller (CCC) is replaced with the adaptive cross-coupled controller (ACCC) in the proposed control scheme to maintain biaxial servo system synchronization motion. Adaptive-tuning PID (APID) position and velocity controllers provide the necessary control actions to maintain synchronization while following a variable command trajectory. A delay-time compensator (DTC) with an adaptive controller was augmented to set the time delay element, effectively moving it outside the closed loop, enhancing the stability of the robust controlled system. This scheme provides strong robustness with respect to uncertain dynamics and disturbances. The simulation and experimental results reveal that the proposed control structure adapts to a wide range of operating conditions and provides promising results under parameter variations and load changes.

Synthesis Methods for Robust Passification and Control

NASA Technical Reports Server (NTRS)

Kelkar, Atul G.; Joshi, Suresh M. (Technical Monitor)

2000-01-01

The research effort under this cooperative agreement has been essentially the continuation of the work from previous grants. The ongoing work has primarily focused on developing passivity-based control techniques for Linear Time-Invariant (LTI) systems. During this period, there has been a significant progress made in the area of passivity-based control of LTI systems and some preliminary results have also been obtained for nonlinear systems, as well. The prior work has addressed optimal control design for inherently passive as well as non- passive linear systems. For exploiting the robustness characteristics of passivity-based controllers the passification methodology was developed for LTI systems that are not inherently passive. Various methods of passification were first proposed in and further developed. The robustness of passification was addressed for multi-input multi-output (MIMO) systems for certain classes of uncertainties using frequency-domain methods. For MIMO systems, a state-space approach using Linear Matrix Inequality (LMI)-based formulation was presented, for passification of non-passive LTI systems. An LMI-based robust passification technique was presented for systems with redundant actuators and sensors. The redundancy in actuators and sensors was used effectively for robust passification using the LMI formulation. The passification was designed to be robust to an interval-type uncertainties in system parameters. The passification techniques were used to design a robust controller for Benchmark Active Control Technology wing under parametric uncertainties. The results on passive nonlinear systems, however, are very limited to date. Our recent work in this area was presented, wherein some stability results were obtained for passive nonlinear systems that are affine in control.

Metabolic Control in Mammalian Fed-Batch Cell Cultures for Reduced Lactic Acid Accumulation and Improved Process Robustness

PubMed Central

Konakovsky, Viktor; Clemens, Christoph; Müller, Markus Michael; Bechmann, Jan; Berger, Martina; Schlatter, Stefan; Herwig, Christoph

2016-01-01

Biomass and cell-specific metabolic rates usually change dynamically over time, making the “feed according to need” strategy difficult to realize in a commercial fed-batch process. We here demonstrate a novel feeding strategy which is designed to hold a particular metabolic state in a fed-batch process by adaptive feeding in real time. The feed rate is calculated with a transferable biomass model based on capacitance, which changes the nutrient flow stoichiometrically in real time. A limited glucose environment was used to confine the cell in a particular metabolic state. In order to cope with uncertainty, two strategies were tested to change the adaptive feed rate and prevent starvation while in limitation: (i) inline pH and online glucose concentration measurement or (ii) inline pH alone, which was shown to be sufficient for the problem statement. In this contribution, we achieved metabolic control within a defined target range. The direct benefit was two-fold: the lactic acid profile was improved and pH could be kept stable. Multivariate Data Analysis (MVDA) has shown that pH influenced lactic acid production or consumption in historical data sets. We demonstrate that a low pH (around 6.8) is not required for our strategy, as glucose availability is already limiting the flux. On the contrary, we boosted glycolytic flux in glucose limitation by setting the pH to 7.4. This new approach led to a yield of lactic acid/glucose (Y L/G) around zero for the whole process time and high titers in our labs. We hypothesize that a higher carbon flux, resulting from a higher pH, may lead to more cells which produce more product. The relevance of this work aims at feeding mammalian cell cultures safely in limitation with a desired metabolic flux range. This resulted in extremely stable, low glucose levels, very robust pH profiles without acid/base interventions and a metabolic state in which lactic acid was consumed instead of being produced from day 1. With this contribution, we wish to extend the basic repertoire of available process control strategies, which will open up new avenues in automation technology and radically improve process robustness in both process development and manufacturing. PMID:28952567

Vascular robustness: The missing parameter in cardiovascular risk prediction.

PubMed

Kraushaar, Lutz E; Dressel, Alexander; Maßmann, Alexander

2018-03-01

Undetected high risk for premature death of cardiovascular disease (CVD) among individuals with low-to-moderate risk factor scores is an acknowledged obstacle to CVD prevention. The vasculature's functional robustness against risk factor derailment may serve as a novel discriminator of mortality risk under similar risk factor loads. To test this assumption, we hypothesized that the expected inverse robustness-mortality association is verifiable as a significant trend along the age spectrum of risk factor-challenged cohorts. This is a retrospective cohort study of 372 adults (mean age 56.1 years, range 21-92; 45% female) with a variety of CV risk factors. An arterial model (VascAssist 2, iSYMED GmbH, Germany) was used to derive global parameters of arterial function from non-invasively acquired pulse pressure waves. Participants were stratified by health status: apparently healthy (AH; n = 221); with hypertension and/or hypercholesterolemia (CC; n = 61); with history of CV event(s) (CVE; n = 90). Multivariate linear regression was used to derive a robustness score which was calibrated against the CVD mortality hazard rate of a sub-cohort of the LURIC study (n = 1369; mean age 59.1 years, range 20-75; 37% female). Robustness correlated linearly with calendar age in CC (F(1, 59) = 10.42; p  < 0.01) and CVE (F(1, 88) = 40.34; p  < 0.0001) but not in the AH strata, supporting the hypothesis of preferential elimination of less robust individuals along the aging trajectory under risk factor challenges. Vascular robustness may serve as a biomarker of vulnerability to CVD risk factor challenges, prognosticating otherwise undetectable elevated risk for premature CVD mortality.

Research in robust control for hypersonic aircraft

NASA Technical Reports Server (NTRS)

Calise, A. J.

1993-01-01

The research during the second reporting period has focused on robust control design for hypersonic vehicles. An already existing design for the Hypersonic Winged-Cone Configuration has been enhanced. Uncertainty models for the effects of propulsion system perturbations due to angle of attack variations, structural vibrations, and uncertainty in control effectiveness were developed. Using H(sub infinity) and mu-synthesis techniques, various control designs were performed in order to investigate the impact of these effects on achievable robust performance.

Single-variant and multi-variant trend tests for genetic association with next-generation sequencing that are robust to sequencing error.

PubMed

Kim, Wonkuk; Londono, Douglas; Zhou, Lisheng; Xing, Jinchuan; Nato, Alejandro Q; Musolf, Anthony; Matise, Tara C; Finch, Stephen J; Gordon, Derek

2012-01-01

As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci. Copyright © 2013 S. Karger AG, Basel.

Single variant and multi-variant trend tests for genetic association with next generation sequencing that are robust to sequencing error

PubMed Central

Kim, Wonkuk; Londono, Douglas; Zhou, Lisheng; Xing, Jinchuan; Nato, Andrew; Musolf, Anthony; Matise, Tara C.; Finch, Stephen J.; Gordon, Derek

2013-01-01

As with any new technology, next generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model, based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to that data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p-value, no matter how many loci. PMID:23594495

Design, evaluation and test of an electronic, multivariable control for the F100 turbofan engine

NASA Technical Reports Server (NTRS)

Skira, C. A.; Dehoff, R. L.; Hall, W. E., Jr.

1980-01-01

A digital, multivariable control design procedure for the F100 turbofan engine is described. The controller is based on locally linear synthesis techniques using linear, quadratic regulator design methods. The control structure uses an explicit model reference form with proportional and integral feedback near a nominal trajectory. Modeling issues, design procedures for the control law and the estimation of poorly measured variables are presented.

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A Baseline for the Multivariate Comparison of Resting-State Networks

PubMed Central

Allen, Elena A.; Erhardt, Erik B.; Damaraju, Eswar; Gruner, William; Segall, Judith M.; Silva, Rogers F.; Havlicek, Martin; Rachakonda, Srinivas; Fries, Jill; Kalyanam, Ravi; Michael, Andrew M.; Caprihan, Arvind; Turner, Jessica A.; Eichele, Tom; Adelsheim, Steven; Bryan, Angela D.; Bustillo, Juan; Clark, Vincent P.; Feldstein Ewing, Sarah W.; Filbey, Francesca; Ford, Corey C.; Hutchison, Kent; Jung, Rex E.; Kiehl, Kent A.; Kodituwakku, Piyadasa; Komesu, Yuko M.; Mayer, Andrew R.; Pearlson, Godfrey D.; Phillips, John P.; Sadek, Joseph R.; Stevens, Michael; Teuscher, Ursina; Thoma, Robert J.; Calhoun, Vince D.

2011-01-01

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease. PMID:21442040

Measurement of pH in whole blood by near-infrared spectroscopy

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

Alam, M. Kathleen; Maynard, John D.; Robinson, M. Ries

1999-03-01

Whole blood pH has been determined {ital in vitro} by using near-infrared spectroscopy over the wavelength range of 1500 to 1785 nm with multivariate calibration modeling of the spectral data obtained from two different sample sets. In the first sample set, the pH of whole blood was varied without controlling cell size and oxygen saturation (O{sub 2} Sat) variation. The result was that the red blood cell (RBC) size and O{sub 2} Sat correlated with pH. Although the partial least-squares (PLS) multivariate calibration of these data produced a good pH prediction cross-validation standard error of prediction (CVSEP)=0.046, R{sup 2}=0.982, themore » spectral data were dominated by scattering changes due to changing RBC size that correlated with the pH changes. A second experiment was carried out where the RBC size and O{sub 2} Sat were varied orthogonally to the pH variation. A PLS calibration of the spectral data obtained from these samples produced a pH prediction with an R{sup 2} of 0.954 and a cross-validated standard error of prediction of 0.064 pH units. The robustness of the PLS calibration models was tested by predicting the data obtained from the other sets. The predicted pH values obtained from both data sets yielded R{sup 2} values greater than 0.9 once the data were corrected for differences in hemoglobin concentration. For example, with the use of the calibration produced from the second sample set, the pH values from the first sample set were predicted with an R{sup 2} of 0.92 after the predictions were corrected for bias and slope. It is shown that spectral information specific to pH-induced chemical changes in the hemoglobin molecule is contained within the PLS loading vectors developed for both the first and second data sets. It is this pH specific information that allows the spectra dominated by pH-correlated scattering changes to provide robust pH predictive ability in the uncorrelated data, and visa versa. {copyright} {ital 1999} {ital Society for Applied Spectroscopy}« less

Robust Temperature Control of a Thermoelectric Cooler via μ -Synthesis

NASA Astrophysics Data System (ADS)

Kürkçü, Burak; Kasnakoğlu, Coşku

2018-02-01

In this work robust temperature control of a thermoelectric cooler (TEC) via μ -synthesis is studied. An uncertain dynamical model for the TEC that is suitable for robust control methods is derived. The model captures variations in operating point due to current, load and temperature changes. A temperature controller is designed utilizing μ -synthesis, a powerful method guaranteeing robust stability and performance. For comparison two well-known control methods, namely proportional-integral-derivative (PID) and internal model control (IMC), are also realized to benchmark the proposed approach. It is observed that the stability and performance on the nominal model are satisfactory for all cases. On the other hand, under perturbations the responses of PID and IMC deteriorate and even become unstable. In contrast, the μ -synthesis controller succeeds in keeping system stability and achieving good performance under all perturbations within the operating range, while at the same time providing good disturbance rejection.

Robust Stabilization Control Based on Guardian Maps Theory for a Longitudinal Model of Hypersonic Vehicle

PubMed Central

Liu, Mengying; Sun, Peihua

2014-01-01

A typical model of hypersonic vehicle has the complicated dynamics such as the unstable states, the nonminimum phases, and the strong coupling input-output relations. As a result, designing a robust stabilization controller is essential to implement the anticipated tasks. This paper presents a robust stabilization controller based on the guardian maps theory for hypersonic vehicle. First, the guardian maps theories are provided to explain the constraint relations between the open subsets of complex plane and the eigenvalues of the state matrix of closed-loop control system. Then, a general control structure in relation to the guardian maps theories is proposed to achieve the respected design demands. Furthermore, the robust stabilization control law depending on the given general control structure is designed for the longitudinal model of hypersonic vehicle. Finally, a simulation example is provided to verify the effectiveness of the proposed methods. PMID:24795535

Fractional Control of An Active Four-wheel-steering Vehicle

NASA Astrophysics Data System (ADS)

Wang, Tianting; Tong, Jun; Chen, Ning; Tian, Jie

2018-03-01

A four-wheel-steering (4WS) vehicle model and reference model with a drop filter are constructed. The decoupling of 4WS vehicle model is carried out. And a fractional PIλDμ controller is introduced into the decoupling strategy to reduce the effects of the uncertainty of the vehicle parameters as well as the unmodelled dynamics on the system performance. Based on optimization techniques, the design of fractional controller are obtained to ensure the robustness of 4WS vehicle during the special range of frequencies through proper choice of the constraints. In order to compare with fractional robust controller, an optimal controller for the same vehicle is also designed. The simulations of the two control systems are carried out and it reveals that the decoupling and fractional robust controller is able to make vehicle model trace the reference model very well with better robustness.

Robust stabilization control based on guardian maps theory for a longitudinal model of hypersonic vehicle.

PubMed

Liu, Yanbin; Liu, Mengying; Sun, Peihua

2014-01-01

A typical model of hypersonic vehicle has the complicated dynamics such as the unstable states, the nonminimum phases, and the strong coupling input-output relations. As a result, designing a robust stabilization controller is essential to implement the anticipated tasks. This paper presents a robust stabilization controller based on the guardian maps theory for hypersonic vehicle. First, the guardian maps theories are provided to explain the constraint relations between the open subsets of complex plane and the eigenvalues of the state matrix of closed-loop control system. Then, a general control structure in relation to the guardian maps theories is proposed to achieve the respected design demands. Furthermore, the robust stabilization control law depending on the given general control structure is designed for the longitudinal model of hypersonic vehicle. Finally, a simulation example is provided to verify the effectiveness of the proposed methods.

Robust fast controller design via nonlinear fractional differential equations.

PubMed

Zhou, Xi; Wei, Yiheng; Liang, Shu; Wang, Yong

2017-07-01

A new method for linear system controller design is proposed whereby the closed-loop system achieves both robustness and fast response. The robustness performance considered here means the damping ratio of closed-loop system can keep its desired value under system parameter perturbation, while the fast response, represented by rise time of system output, can be improved by tuning the controller parameter. We exploit techniques from both the nonlinear systems control and the fractional order systems control to derive a novel nonlinear fractional order controller. For theoretical analysis of the closed-loop system performance, two comparison theorems are developed for a class of fractional differential equations. Moreover, the rise time of the closed-loop system can be estimated, which facilitates our controller design to satisfy the fast response performance and maintain the robustness. Finally, numerical examples are given to illustrate the effectiveness of our methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

An advanced robust method for speed control of switched reluctance motor

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

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

PubMed

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

2017-01-01

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

Application of the MNA design method to a nonlinear turbofan engine. [multivariable Nyquist array method

NASA Technical Reports Server (NTRS)

Leininger, G. G.

1981-01-01

Using nonlinear digital simulation as a representative model of the dynamic operation of the QCSEE turbofan engine, a feedback control system is designed by variable frequency design techniques. Transfer functions are generated for each of five power level settings covering the range of operation from approach power to full throttle (62.5% to 100% full power). These transfer functions are then used by an interactive control system design synthesis program to provide a closed loop feedback control using the multivariable Nyquist array and extensions to multivariable Bode diagrams and Nichols charts.

Robust control for fractional variable-order chaotic systems with non-singular kernel

NASA Astrophysics Data System (ADS)

Zuñiga-Aguilar, C. J.; Gómez-Aguilar, J. F.; Escobar-Jiménez, R. F.; Romero-Ugalde, H. M.

2018-01-01

This paper investigates the chaos control for a class of variable-order fractional chaotic systems using robust control strategy. The variable-order fractional models of the non-autonomous biological system, the King Cobra chaotic system, the Halvorsen's attractor and the Burke-Shaw system, have been derived using the fractional-order derivative with Mittag-Leffler in the Liouville-Caputo sense. The fractional differential equations and the control law were solved using the Adams-Bashforth-Moulton algorithm. To test the control stability efficiency, different statistical indicators were introduced. Finally, simulation results demonstrate the effectiveness of the proposed robust control.

Robust control synthesis for uncertain dynamical systems

NASA Technical Reports Server (NTRS)

Byun, Kuk-Whan; Wie, Bong; Sunkel, John

1989-01-01

This paper presents robust control synthesis techniques for uncertain dynamical systems subject to structured parameter perturbation. Both QFT (quantitative feedback theory) and H-infinity control synthesis techniques are investigated. Although most H-infinity-related control techniques are not concerned with the structured parameter perturbation, a new way of incorporating the parameter uncertainty in the robust H-infinity control design is presented. A generic model of uncertain dynamical systems is used to illustrate the design methodologies investigated in this paper. It is shown that, for a certain noncolocated structural control problem, use of both techniques results in nonminimum phase compensation.

ATR-FTIR membrane-based sensor for the simultaneous determination of surfactant and oil total indices in industrial degreasing baths.

PubMed

Lucena, Rafael; Cárdenas, Soledad; Gallego, Mercedes; Valcárcel, Miguel

2006-03-01

Monitoring the exhaustion of alkaline degreasing baths is one of the main aspects in metal mechanizing industrial process control. The global level of surfactant, and mainly grease, can be used as ageing indicators. In this paper, an attenuated total reflection-Fourier transform infrared (ATR-FTIR) membrane-based sensor is presented for the determination of these parameters. The system is based on a micro-liquid-liquid extraction of the analytes through a polymeric membrane from the aqueous to the organic solvent layer which is in close contact with the internal reflection element and continuously monitored. Samples are automatically processed using a simple, robust sequential injection analysis (SIA) configuration, on-line coupled to the instrument. The global signal obtained for both families of compounds are processed via a multivariate calibration technique (partial least squares, PLS). Excellent correlation was obtained for the values given by the proposed method compared to those of the gravimetric reference one with very low error values for both calibration and validation.

Rapid neural discrimination of communicative gestures.

PubMed

Redcay, Elizabeth; Carlson, Thomas A

2015-04-01

Humans are biased toward social interaction. Behaviorally, this bias is evident in the rapid effects that self-relevant communicative signals have on attention and perceptual systems. The processing of communicative cues recruits a wide network of brain regions, including mentalizing systems. Relatively less work, however, has examined the timing of the processing of self-relevant communicative cues. In the present study, we used multivariate pattern analysis (decoding) approach to the analysis of magnetoencephalography (MEG) to study the processing dynamics of social-communicative actions. Twenty-four participants viewed images of a woman performing actions that varied on a continuum of communicative factors including self-relevance (to the participant) and emotional valence, while their brain activity was recorded using MEG. Controlling for low-level visual factors, we found early discrimination of emotional valence (70 ms) and self-relevant communicative signals (100 ms). These data offer neural support for the robust and rapid effects of self-relevant communicative cues on behavior. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Association between perception of maternal bonding styles and social anxiety disorder among young women.

PubMed

Castelli, Rochele D; Quevedo, Luciana de Á; Coelho, Fábio M; Lopez, Mariane A; da Silva, Ricardo A; Böhm, Denise M; Souza, Luciano D; de Matos, Mariana B; Pinheiro, Karen A; Pinheiro, Ricardo T

2015-01-01

To evaluate the association between social anxiety disorder (SAD) and perceived maternal bonding styles among young women during pregnancy and 30 months after childbirth. A cohort of young women from the city of Pelotas, Brazil was followed up from pregnancy to 30 months postpartum. The Mini Neuropsychiatric Interview Plus was used to assess SAD and the Parental Bonding Instrument was administered to measure maternal bonding styles. Poisson regression with robust variance was used for multivariable analysis. After adjusting for potential confounding factors, SAD prevalence was 6.39 times higher among young women who perceived their mothers as neglectful (prevalence ratio [PR] 6.39; 95% confidence interval [95%CI] 1.2-32.0), and 5.57 times higher in women who perceived their mothers as affectionless controlling (PR = 5.57; 95%CI 1.5-19.7) when compared with those who received optimal care. Maternal bonding style may have an influence on the development of SAD. Therefore, support and early prevention strategies should be offered to the family.

Corruption and use of antibiotics in regions of Europe.

PubMed

Rönnerstrand, Björn; Lapuente, Victor

2017-03-01

The aim of this article is to investigate the association between corruption and antibiotic use at sub-national level. We explore the correlation between, on the one hand, two measures of corruption (prevalence of corruption in the health sector and prevalence of bribes in the society) at regional level from the European Quality of Government Index; and, on the other, the consumption of antibiotics in those European regions from a 2009 Special Euro Barometer. In a multivariate regression model, we control for potential confounders: purchasing power of standardized regional gross domestic product, inhabitants per medical doctor and age-standardized all-cause mortality rates. We find that there is a strong positive association between both measures of corruption (i.e. in the health sector, and in the society at large) and antibiotics use; and that this association is robust to the introduction of the control variables. These results support previous findings in the literature linking corruption to higher antibiotic use at cross-national level. We show that corruption does seem to account for some of the remarkable between-region variation in antibiotic consumption in Europe. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Relationship of soft drink consumption to global overweight, obesity, and diabetes: a cross-national analysis of 75 countries.

PubMed

Basu, Sanjay; McKee, Martin; Galea, Gauden; Stuckler, David

2013-11-01

We estimated the relationship between soft drink consumption and obesity and diabetes worldwide. We used multivariate linear regression to estimate the association between soft drink consumption and overweight, obesity, and diabetes prevalence in 75 countries, controlling for other foods (cereals, meats, fruits and vegetables, oils, and total calories), income, urbanization, and aging. Data were obtained from the Euromonitor Global Market Information Database, the World Health Organization, and the International Diabetes Federation. Bottled water consumption, which increased with per-capita income in parallel to soft drink consumption, served as a natural control group. Soft drink consumption increased globally from 9.5 gallons per person per year in 1997 to 11.4 gallons in 2010. A 1% rise in soft drink consumption was associated with an additional 4.8 overweight adults per 100 (adjusted B; 95% confidence interval [CI] = 3.1, 6.5), 2.3 obese adults per 100 (95% CI = 1.1, 3.5), and 0.3 adults with diabetes per 100 (95% CI = 0.1, 0.8). These findings remained robust in low- and middle-income countries. Soft drink consumption is significantly linked to overweight, obesity, and diabetes worldwide, including in low- and middle-income countries.

Design of permanent magnet synchronous motor speed loop controller based on sliding mode control algorithm

NASA Astrophysics Data System (ADS)

Qiang, Jiang; Meng-wei, Liao; Ming-jie, Luo

2018-03-01

Abstract.The control performance of Permanent Magnet Synchronous Motor will be affected by the fluctuation or changes of mechanical parameters when PMSM is applied as driving motor in actual electric vehicle,and external disturbance would influence control robustness.To improve control dynamic quality and robustness of PMSM speed control system, a new second order integral sliding mode control algorithm is introduced into PMSM vector control.The simulation results show that, compared with the traditional PID control,the modified control scheme optimized has better control precision and dynamic response ability and perform better with a stronger robustness facing external disturbance,it can effectively solve the traditional sliding mode variable structure control chattering problems as well.

Robust synchronization control scheme of a population of nonlinear stochastic synthetic genetic oscillators under intrinsic and extrinsic molecular noise via quorum sensing.

PubMed

Chen, Bor-Sen; Hsu, Chih-Yuan

2012-10-26

Collective rhythms of gene regulatory networks have been a subject of considerable interest for biologists and theoreticians, in particular the synchronization of dynamic cells mediated by intercellular communication. Synchronization of a population of synthetic genetic oscillators is an important design in practical applications, because such a population distributed over different host cells needs to exploit molecular phenomena simultaneously in order to emerge a biological phenomenon. However, this synchronization may be corrupted by intrinsic kinetic parameter fluctuations and extrinsic environmental molecular noise. Therefore, robust synchronization is an important design topic in nonlinear stochastic coupled synthetic genetic oscillators with intrinsic kinetic parameter fluctuations and extrinsic molecular noise. Initially, the condition for robust synchronization of synthetic genetic oscillators was derived based on Hamilton Jacobi inequality (HJI). We found that if the synchronization robustness can confer enough intrinsic robustness to tolerate intrinsic parameter fluctuation and extrinsic robustness to filter the environmental noise, then robust synchronization of coupled synthetic genetic oscillators is guaranteed. If the synchronization robustness of a population of nonlinear stochastic coupled synthetic genetic oscillators distributed over different host cells could not be maintained, then robust synchronization could be enhanced by external control input through quorum sensing molecules. In order to simplify the analysis and design of robust synchronization of nonlinear stochastic synthetic genetic oscillators, the fuzzy interpolation method was employed to interpolate several local linear stochastic coupled systems to approximate the nonlinear stochastic coupled system so that the HJI-based synchronization design problem could be replaced by a simple linear matrix inequality (LMI)-based design problem, which could be solved with the help of LMI toolbox in MATLAB easily. If the synchronization robustness criterion, i.e. the synchronization robustness ≥ intrinsic robustness + extrinsic robustness, then the stochastic coupled synthetic oscillators can be robustly synchronized in spite of intrinsic parameter fluctuation and extrinsic noise. If the synchronization robustness criterion is violated, external control scheme by adding inducer can be designed to improve synchronization robustness of coupled synthetic genetic oscillators. The investigated robust synchronization criteria and proposed external control method are useful for a population of coupled synthetic networks with emergent synchronization behavior, especially for multi-cellular, engineered networks.

Robust synchronization control scheme of a population of nonlinear stochastic synthetic genetic oscillators under intrinsic and extrinsic molecular noise via quorum sensing

PubMed Central

2012-01-01

Background Collective rhythms of gene regulatory networks have been a subject of considerable interest for biologists and theoreticians, in particular the synchronization of dynamic cells mediated by intercellular communication. Synchronization of a population of synthetic genetic oscillators is an important design in practical applications, because such a population distributed over different host cells needs to exploit molecular phenomena simultaneously in order to emerge a biological phenomenon. However, this synchronization may be corrupted by intrinsic kinetic parameter fluctuations and extrinsic environmental molecular noise. Therefore, robust synchronization is an important design topic in nonlinear stochastic coupled synthetic genetic oscillators with intrinsic kinetic parameter fluctuations and extrinsic molecular noise. Results Initially, the condition for robust synchronization of synthetic genetic oscillators was derived based on Hamilton Jacobi inequality (HJI). We found that if the synchronization robustness can confer enough intrinsic robustness to tolerate intrinsic parameter fluctuation and extrinsic robustness to filter the environmental noise, then robust synchronization of coupled synthetic genetic oscillators is guaranteed. If the synchronization robustness of a population of nonlinear stochastic coupled synthetic genetic oscillators distributed over different host cells could not be maintained, then robust synchronization could be enhanced by external control input through quorum sensing molecules. In order to simplify the analysis and design of robust synchronization of nonlinear stochastic synthetic genetic oscillators, the fuzzy interpolation method was employed to interpolate several local linear stochastic coupled systems to approximate the nonlinear stochastic coupled system so that the HJI-based synchronization design problem could be replaced by a simple linear matrix inequality (LMI)-based design problem, which could be solved with the help of LMI toolbox in MATLAB easily. Conclusion If the synchronization robustness criterion, i.e. the synchronization robustness ≥ intrinsic robustness + extrinsic robustness, then the stochastic coupled synthetic oscillators can be robustly synchronized in spite of intrinsic parameter fluctuation and extrinsic noise. If the synchronization robustness criterion is violated, external control scheme by adding inducer can be designed to improve synchronization robustness of coupled synthetic genetic oscillators. The investigated robust synchronization criteria and proposed external control method are useful for a population of coupled synthetic networks with emergent synchronization behavior, especially for multi-cellular, engineered networks. PMID:23101662

Robust high-performance control for robotic manipulators

NASA Technical Reports Server (NTRS)

Seraji, H.

1989-01-01

A robust control scheme to accomplish accurate trajectory tracking for an integrated system of manipulator-plus-actuators is proposed. The control scheme comprises a feedforward and a feedback controller. The feedforward controller contains any known part of the manipulator dynamics that can be used for online control. The feedback controller consists of adaptive position and velocity feedback gains and an auxiliary signal which is simply generated by a fixed-gain proportional/integral/derivative controller. The feedback controller is updated by very simple adaptation laws which contain both proportional and integral adaptation terms. By introduction of a simple sigma modification to the adaptation laws, robustness is guaranteed in the presence of unmodeled dynamics and disturbances.

Research on Robust Control Strategies for VSC-HVDC

NASA Astrophysics Data System (ADS)

Zhu, Kaicheng; Bao, Hai

2018-01-01

In the control system of VSC-HVDC, the phase locked loop provides phase signals to voltage vector control and trigger pulses to generate the required reference phase. The PLL is a typical second-order system. When the system is in unstable state, it will oscillate, make the trigger angle shift, produce harmonic, and make active power and reactive power coupled. Thus, considering the external disturbances introduced by the PLL in VSC-HVDC control system, the parameter perturbations of the controller and the model uncertainties, a H∞ robust controller of mixed sensitivity optimization problem is designed by using the Hinf function provided by the robust control toolbox. Then, compare it with the proportional integral controller through the MATLAB simulation experiment. By contrast, when the H∞ robust controller is added, active and reactive power of the converter station can track the change of reference values more accurately and quickly, and reduce overshoot. When the step change of active and reactive power occurs, mutual influence is reduced and better independent regulation is achieved.

Robust diagnosis of non-Hodgkin lymphoma phenotypes validated on gene expression data from different laboratories.

PubMed

Bhanot, Gyan; Alexe, Gabriela; Levine, Arnold J; Stolovitzky, Gustavo

2005-01-01

A major challenge in cancer diagnosis from microarray data is the need for robust, accurate, classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose such a classification scheme originally developed for phenotype identification from mass spectrometry data. The method uses a robust multivariate gene selection procedure and combines the results of several machine learning tools trained on raw and pattern data to produce an accurate meta-classifier. We illustrate and validate our method by applying it to gene expression datasets: the oligonucleotide HuGeneFL microarray dataset of Shipp et al. (www.genome.wi.mit.du/MPR/lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our pattern-based meta-classification technique achieves higher predictive accuracies than each of the individual classifiers , is robust against data perturbations and provides subsets of related predictive genes. Our techniques predict that combinations of some genes in the p53 pathway are highly predictive of phenotype. In particular, we find that in 80% of DLBCL cases the mRNA level of at least one of the three genes p53, PLK1 and CDK2 is elevated, while in 80% of FL cases, the mRNA level of at most one of them is elevated.

Robust reliable sampled-data control for switched systems with application to flight control

NASA Astrophysics Data System (ADS)

Sakthivel, R.; Joby, Maya; Shi, P.; Mathiyalagan, K.

2016-11-01

This paper addresses the robust reliable stabilisation problem for a class of uncertain switched systems with random delays and norm bounded uncertainties. The main aim of this paper is to obtain the reliable robust sampled-data control design which involves random time delay with an appropriate gain control matrix for achieving the robust exponential stabilisation for uncertain switched system against actuator failures. In particular, the involved delays are assumed to be randomly time-varying which obeys certain mutually uncorrelated Bernoulli distributed white noise sequences. By constructing an appropriate Lyapunov-Krasovskii functional (LKF) and employing an average-dwell time approach, a new set of criteria is derived for ensuring the robust exponential stability of the closed-loop switched system. More precisely, the Schur complement and Jensen's integral inequality are used in derivation of stabilisation criteria. By considering the relationship among the random time-varying delay and its lower and upper bounds, a new set of sufficient condition is established for the existence of reliable robust sampled-data control in terms of solution to linear matrix inequalities (LMIs). Finally, an illustrative example based on the F-18 aircraft model is provided to show the effectiveness of the proposed design procedures.

Robust control of dielectric elastomer diaphragm actuator for human pulse signal tracking

NASA Astrophysics Data System (ADS)

Ye, Zhihang; Chen, Zheng; Asmatulu, Ramazan; Chan, Hoyin

2017-08-01

Human pulse signal tracking is an emerging technology that is needed in traditional Chinese medicine. However, soft actuation with multi-frequency tracking capability is needed for tracking human pulse signal. Dielectric elastomer (DE) is one type of soft actuating that has great potential in human pulse signal tracking. In this paper, a DE diaphragm actuator was designed and fabricated to track human pulse pressure signal. A physics-based and control-oriented model has been developed to capture the dynamic behavior of DE diaphragm actuator. Using the physical model, an H-infinity robust control was designed for the actuator to reject high-frequency sensing noises and disturbances. The robust control was then implemented in real-time to track a multi-frequency signal, which verified the tracking capability and robustness of the control system. In the human pulse signal tracking test, a human pulse signal was measured at the City University of Hong Kong and then was tracked using DE actuator at Wichita State University in the US. Experimental results have verified that the DE actuator with its robust control is capable of tracking human pulse signal.

Designing for Damage: Robust Flight Control Design using Sliding Mode Techniques

NASA Technical Reports Server (NTRS)

Vetter, T. K.; Wells, S. R.; Hess, Ronald A.; Bacon, Barton (Technical Monitor); Davidson, John (Technical Monitor)

2002-01-01

A brief review of sliding model control is undertaken, with particular emphasis upon the effects of neglected parasitic dynamics. Sliding model control design is interpreted in the frequency domain. The inclusion of asymptotic observers and control 'hedging' is shown to reduce the effects of neglected parasitic dynamics. An investigation into the application of observer-based sliding mode control to the robust longitudinal control of a highly unstable is described. The sliding mode controller is shown to exhibit stability and performance robustness superior to that of a classical loop-shaped design when significant changes in vehicle and actuator dynamics are employed to model airframe damage.

Sliding Mode Control of the X-33 with an Engine Failure

NASA Technical Reports Server (NTRS)

Shtessel, Yuri B.; Hall, Charles E.

2000-01-01

Ascent flight control of the X-3 is performed using two XRS-2200 linear aerospike engines. in addition to aerosurfaces. The baseline control algorithms are PID with gain scheduling. Flight control using an innovative method. Sliding Mode Control. is presented for nominal and engine failed modes of flight. An easy to implement, robust controller. requiring no reconfiguration or gain scheduling is demonstrated through high fidelity flight simulations. The proposed sliding mode controller utilizes a two-loop structure and provides robust. de-coupled tracking of both orientation angle command profiles and angular rate command profiles in the presence of engine failure, bounded external disturbances (wind gusts) and uncertain matrix of inertia. Sliding mode control causes the angular rate and orientation angle tracking error dynamics to be constrained to linear, de-coupled, homogeneous, and vector valued differential equations with desired eigenvalues. Conditions that restrict engine failures to robustness domain of the sliding mode controller are derived. Overall stability of a two-loop flight control system is assessed. Simulation results show that the designed controller provides robust, accurate, de-coupled tracking of the orientation angle command profiles in the presence of external disturbances and vehicle inertia uncertainties, as well as the single engine failed case. The designed robust controller will significantly reduce the time and cost associated with flying new trajectory profiles or orbits, with new payloads, and with modified vehicles

The relationship between hypophosphataemia and outcomes during low-intensity and high-intensity continuous renal replacement therapy.

PubMed

Bellomo, Rinaldo; Cass, Alan; Cole, Louise; Finfer, Simon; Gallagher, Martin; Kim, Inbyung; Lee, Joanne; Lo, Serigne; McArthur, Colin; McGuinness, Shay; McGuiness, Shay; Norton, Robyn; Myburgh, John; Scheinkestel, Carlos

2014-03-01

To identify risk factors for development of hypophosphataemia in patients treated with two different intensities of continuous renal replacement therapy (CRRT) and to assess the independent association of hypophosphataemia with major clinical outcomes. We performed secondary analysis of data collected from 1441 patients during a large, multicentre randomised controlled trial of CRRT intensity. We allocated patients to two different intensities of CRRT (25mL/kg/hour vs 40 mL/kg/hour of effluent generation) and obtained daily measurement of serum phosphate levels. We obtained 14 115 phosphate measurements and identified 462 patients (32.1%) with hypophosphataemia, with peak incidence on Day 2 and Day 3. With lower intensity CRRT, there were 58 episodes of hypophosphataemia/1000 patient days, compared with 112 episodes/1000 patient days with higher intensity CRRT (P < 0.001). On multivariable logistic regression analysis, higher intensity CRRT, female sex, higher Acute Physiology and Chronic Health Evaluation score and hypokalaemia were independently associated with an increased odds ratio (OR) for hypophosphataemia. On multivariable models, hypophosphataemia was associated with better clinical outcomes, but when analysis was confined to patients alive at 96 hours, hypophosphataemia was not independently associated with clinical outcomes. Hypophosphataemia is common during CRRT and its incidence increases with greater CRRT intensity. Hypophosphataemia is not a robust independent predictor of mortality. Its greater incidence in the higher intensity CRRT arm of the Randomised Evaluation of Normal vs Augmented Level trial does not explain the lack of improved outcomes with such treatment.

Classification of the medicinal plants of the genus Atractylodes using high-performance liquid chromatography with diode array and tandem mass spectrometry detection combined with multivariate statistical analysis.

PubMed

Cho, Hyun-Deok; Kim, Unyong; Suh, Joon Hyuk; Eom, Han Young; Kim, Junghyun; Lee, Seul Gi; Choi, Yong Seok; Han, Sang Beom

2016-04-01

Analytical methods using high-performance liquid chromatography with diode array and tandem mass spectrometry detection were developed for the discrimination of the rhizomes of four Atractylodes medicinal plants: A. japonica, A. macrocephala, A. chinensis, and A. lancea. A quantitative study was performed, selecting five bioactive components, including atractylenolide I, II, III, eudesma-4(14),7(11)-dien-8-one and atractylodin, on twenty-six Atractylodes samples of various origins. Sample extraction was optimized to sonication with 80% methanol for 40 min at room temperature. High-performance liquid chromatography with diode array detection was established using a C18 column with a water/acetonitrile gradient system at a flow rate of 1.0 mL/min, and the detection wavelength was set at 236 nm. Liquid chromatography with tandem mass spectrometry was applied to certify the reliability of the quantitative results. The developed methods were validated by ensuring specificity, linearity, limit of quantification, accuracy, precision, recovery, robustness, and stability. Results showed that cangzhu contained higher amounts of atractylenolide I and atractylodin than baizhu, and especially atractylodin contents showed the greatest variation between baizhu and cangzhu. Multivariate statistical analysis, such as principal component analysis and hierarchical cluster analysis, were also employed for further classification of the Atractylodes plants. The established method was suitable for quality control of the Atractylodes plants. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Discrimination between glycosylation patterns of therapeutic antibodies using a microfluidic platform, MALDI-MS and multivariate statistics.

PubMed

Thuy, Tran Thi; Tengstrand, Erik; Aberg, Magnus; Thorsén, Gunnar

2012-11-01

Optimal glycosylation with respect to the efficacy, serum half-life time, and immunogenic properties is essential in the generation of therapeutic antibodies. The glycosylation pattern can be affected by several different parameters during the manufacture of antibodies and may change significantly over cultivation time. Fast and robust methods for determination of the glycosylation patterns of therapeutic antibodies are therefore needed. We have recently presented an efficient method for the determination of glycans on therapeutic antibodies using a microfluidic CD platform for sample preparation prior to matrix-assisted laser-desorption mass spectrometry analysis. In the present work, this method is applied to analyse the glycosylation patterns of three commercially available therapeutic antibodies and one intended for therapeutic use. Two of the antibodies produced in mouse myeloma cell line (SP2/0) and one produced in Chinese hamster ovary (CHO) cells exhibited similar glycosylation patterns but could still be readily differentiated from each other using multivariate statistical methods. The two antibodies with most similar glycosylation patterns were also studied in an assessment of the method's applicability for quality control of therapeutic antibodies. The method presented in this paper is highly automated and rapid. It can therefore efficiently generate data that helps to keep a production process within the desired design space or assess that an identical product is being produced after changes to the process. Copyright © 2012 Elsevier B.V. All rights reserved.

Active Fault Tolerant Control for Ultrasonic Piezoelectric Motor

NASA Astrophysics Data System (ADS)

Boukhnifer, Moussa

2012-07-01

Ultrasonic piezoelectric motor technology is an important system component in integrated mechatronics devices working on extreme operating conditions. Due to these constraints, robustness and performance of the control interfaces should be taken into account in the motor design. In this paper, we apply a new architecture for a fault tolerant control using Youla parameterization for an ultrasonic piezoelectric motor. The distinguished feature of proposed controller architecture is that it shows structurally how the controller design for performance and robustness may be done separately which has the potential to overcome the conflict between performance and robustness in the traditional feedback framework. A fault tolerant control architecture includes two parts: one part for performance and the other part for robustness. The controller design works in such a way that the feedback control system will be solely controlled by the proportional plus double-integral PI2 performance controller for a nominal model without disturbances and H∞ robustification controller will only be activated in the presence of the uncertainties or an external disturbances. The simulation results demonstrate the effectiveness of the proposed fault tolerant control architecture.

Robust Control Design for Uncertain Nonlinear Dynamic Systems

NASA Technical Reports Server (NTRS)

Kenny, Sean P.; Crespo, Luis G.; Andrews, Lindsey; Giesy, Daniel P.

2012-01-01

Robustness to parametric uncertainty is fundamental to successful control system design and as such it has been at the core of many design methods developed over the decades. Despite its prominence, most of the work on robust control design has focused on linear models and uncertainties that are non-probabilistic in nature. Recently, researchers have acknowledged this disparity and have been developing theory to address a broader class of uncertainties. This paper presents an experimental application of robust control design for a hybrid class of probabilistic and non-probabilistic parametric uncertainties. The experimental apparatus is based upon the classic inverted pendulum on a cart. The physical uncertainty is realized by a known additional lumped mass at an unknown location on the pendulum. This unknown location has the effect of substantially altering the nominal frequency and controllability of the nonlinear system, and in the limit has the capability to make the system neutrally stable and uncontrollable. Another uncertainty to be considered is a direct current motor parameter. The control design objective is to design a controller that satisfies stability, tracking error, control power, and transient behavior requirements for the largest range of parametric uncertainties. This paper presents an overview of the theory behind the robust control design methodology and the experimental results.

Robust control of multi-jointed arm with a decentralized autonomous control mechanism

NASA Technical Reports Server (NTRS)

Kimura, Shinichi; Miyazaki, Ken; Suzuki, Yoshiaki

1994-01-01

A decentralized autonomous control mechanism applied to the control of three dimensional manipulators and its robustness to partial damage was assessed by computer simulation. Decentralized control structures are believed to be quite robust to time delay between the operator and the target system. A 10-jointed manipulator based on our control mechanism was able to continue its positioning task in three-dimensional space without revision of the control program, even after some of its joints were damaged. These results suggest that this control mechanism can be effectively applied to space telerobots, which are associated with serious time delay between the operator and the target system, and which cannot be easily repaired after being partially damaged.

Power oscillation suppression by robust SMES in power system with large wind power penetration

NASA Astrophysics Data System (ADS)

Ngamroo, Issarachai; Cuk Supriyadi, A. N.; Dechanupaprittha, Sanchai; Mitani, Yasunori

2009-01-01

The large penetration of wind farm into interconnected power systems may cause the severe problem of tie-line power oscillations. To suppress power oscillations, the superconducting magnetic energy storage (SMES) which is able to control active and reactive powers simultaneously, can be applied. On the other hand, several generating and loading conditions, variation of system parameters, etc., cause uncertainties in the system. The SMES controller designed without considering system uncertainties may fail to suppress power oscillations. To enhance the robustness of SMES controller against system uncertainties, this paper proposes a robust control design of SMES by taking system uncertainties into account. The inverse additive perturbation is applied to represent the unstructured system uncertainties and included in power system modeling. The configuration of active and reactive power controllers is the first-order lead-lag compensator with single input feedback. To tune the controller parameters, the optimization problem is formulated based on the enhancement of robust stability margin. The particle swarm optimization is used to solve the problem and achieve the controller parameters. Simulation studies in the six-area interconnected power system with wind farms confirm the robustness of the proposed SMES under various operating conditions.

Robustness of Controllability for Networks Based on Edge-Attack

PubMed Central

Nie, Sen; Wang, Xuwen; Zhang, Haifeng; Li, Qilang; Wang, Binghong

2014-01-01

We study the controllability of networks in the process of cascading failures under two different attacking strategies, random and intentional attack, respectively. For the highest-load edge attack, it is found that the controllability of Erdős-Rényi network, that with moderate average degree, is less robust, whereas the Scale-free network with moderate power-law exponent shows strong robustness of controllability under the same attack strategy. The vulnerability of controllability under random and intentional attacks behave differently with the increasing of removal fraction, especially, we find that the robustness of control has important role in cascades for large removal fraction. The simulation results show that for Scale-free networks with various power-law exponents, the network has larger scale of cascades do not mean that there will be more increments of driver nodes. Meanwhile, the number of driver nodes in cascading failures is also related to the edges amount in strongly connected components. PMID:24586507

Robustness of controllability for networks based on edge-attack.

PubMed

Nie, Sen; Wang, Xuwen; Zhang, Haifeng; Li, Qilang; Wang, Binghong

2014-01-01

We study the controllability of networks in the process of cascading failures under two different attacking strategies, random and intentional attack, respectively. For the highest-load edge attack, it is found that the controllability of Erdős-Rényi network, that with moderate average degree, is less robust, whereas the Scale-free network with moderate power-law exponent shows strong robustness of controllability under the same attack strategy. The vulnerability of controllability under random and intentional attacks behave differently with the increasing of removal fraction, especially, we find that the robustness of control has important role in cascades for large removal fraction. The simulation results show that for Scale-free networks with various power-law exponents, the network has larger scale of cascades do not mean that there will be more increments of driver nodes. Meanwhile, the number of driver nodes in cascading failures is also related to the edges amount in strongly connected components.

Robust, nonlinear, high angle-of-attack control design for a supermaneuverable vehicle

NASA Technical Reports Server (NTRS)

Adams, Richard J.

1993-01-01

High angle-of-attack flight control laws are developed for a supermaneuverable fighter aircraft. The methods of dynamic inversion and structured singular value synthesis are combined into an approach which addresses both the nonlinearity and robustness problems of flight at extreme operating conditions. The primary purpose of the dynamic inversion control elements is to linearize the vehicle response across the flight envelope. Structured singular value synthesis is used to design a dynamic controller which provides robust tracking to pilot commands. The resulting control system achieves desired flying qualities and guarantees a large margin of robustness to uncertainties for high angle-of-attack flight conditions. The results of linear simulation and structured singular value stability analysis are presented to demonstrate satisfaction of the design criteria. High fidelity nonlinear simulation results show that the combined dynamics inversion/structured singular value synthesis control law achieves a high level of performance in a realistic environment.

A Computational Framework to Control Verification and Robustness Analysis

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2010-01-01

This paper presents a methodology for evaluating the robustness of a controller based on its ability to satisfy the design requirements. The framework proposed is generic since it allows for high-fidelity models, arbitrary control structures and arbitrary functional dependencies between the requirements and the uncertain parameters. The cornerstone of this contribution is the ability to bound the region of the uncertain parameter space where the degradation in closed-loop performance remains acceptable. The size of this bounding set, whose geometry can be prescribed according to deterministic or probabilistic uncertainty models, is a measure of robustness. The robustness metrics proposed herein are the parametric safety margin, the reliability index, the failure probability and upper bounds to this probability. The performance observed at the control verification setting, where the assumptions and approximations used for control design may no longer hold, will fully determine the proposed control assessment.

Control of a small working robot on a large flexible manipulator for suppressing vibrations: Development of a robust control law for flexible robot and it's stability analysis

NASA Technical Reports Server (NTRS)

Soo, Han Lee

1991-01-01

Researchers developed a robust control law for slow motions for the accurate trajectory control of a flexible robot. The control law does not need larger velocity gains than position gains, which some researchers need to ensure the stability of a rigid robot. Initial experimentation for the Small Articulated Manipulator (SAM) shows that control laws that use smaller velocity gains are more robust to signal noise than the control laws that use larger velocity gains. Researchers analyzed the stability of the composite control law, the robust control for the slow motion, and the strain rate feedback for the fast control. The stability analysis was done by using a quadratic Liapunov function. Researchers found that the flexible motion of links could be controlled by relating the input force to the flexible signals which are sensed at the near tip of each link. The signals are contaminated by the time delayed input force. However, the effect of the time delayed input force can be reduced by giving a certain configuration to the SAM.

Manufacturing Execution Systems: Examples of Performance Indicator and Operational Robustness Tools.

PubMed

Gendre, Yannick; Waridel, Gérard; Guyon, Myrtille; Demuth, Jean-François; Guelpa, Hervé; Humbert, Thierry

Manufacturing Execution Systems (MES) are computerized systems used to measure production performance in terms of productivity, yield, and quality. In the first part, performance indicator and overall equipment effectiveness (OEE), process robustness tools and statistical process control are described. The second part details some tools to help process robustness and control by operators by preventing deviations from target control charts. MES was developed by Syngenta together with CIMO for automation.

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

NASA Technical Reports Server (NTRS)

Burken, John J.

2005-01-01

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

A robust control scheme for flexible arms with friction in the joints

NASA Technical Reports Server (NTRS)

Rattan, Kuldip S.; Feliu, Vicente; Brown, H. Benjamin, Jr.

1988-01-01

A general control scheme to control flexible arms with friction in the joints is proposed in this paper. This scheme presents the advantage of being robust in the sense that it minimizes the effects of the Coulomb friction existing in the motor and the effects of changes in the dynamic friction coefficient. A justification of the robustness properties of the scheme is given in terms of the sensitivity analysis.

Optimal and robust control of quantum state transfer by shaping the spectral phase of ultrafast laser pulses.

PubMed

Guo, Yu; Dong, Daoyi; Shu, Chuan-Cun

2018-04-04

Achieving fast and efficient quantum state transfer is a fundamental task in physics, chemistry and quantum information science. However, the successful implementation of the perfect quantum state transfer also requires robustness under practically inevitable perturbative defects. Here, we demonstrate how an optimal and robust quantum state transfer can be achieved by shaping the spectral phase of an ultrafast laser pulse in the framework of frequency domain quantum optimal control theory. Our numerical simulations of the single dibenzoterrylene molecule as well as in atomic rubidium show that optimal and robust quantum state transfer via spectral phase modulated laser pulses can be achieved by incorporating a filtering function of the frequency into the optimization algorithm, which in turn has potential applications for ultrafast robust control of photochemical reactions.

Robust linear quadratic designs with respect to parameter uncertainty

NASA Technical Reports Server (NTRS)

Douglas, Joel; Athans, Michael

1992-01-01

The authors derive a linear quadratic regulator (LQR) which is robust to parametric uncertainty by using the overbounding method of I. R. Petersen and C. V. Hollot (1986). The resulting controller is determined from the solution of a single modified Riccati equation. It is shown that, when applied to a structural system, the controller gains add robustness by minimizing the potential energy of uncertain stiffness elements, and minimizing the rate of dissipation of energy through uncertain damping elements. A worst-case disturbance in the direction of the uncertainty is also considered. It is proved that performance robustness has been increased with the robust LQR when compared to a mismatched LQR design where the controller is designed on the nominal system, but applied to the actual uncertain system.

Multivariable control altitude demonstration on the F100 turbofan engine

NASA Technical Reports Server (NTRS)

Lehtinen, B.; Dehoff, R. L.; Hackney, R. D.

1979-01-01

The F100 Multivariable control synthesis (MVCS) program, was aimed at demonstrating the benefits of LGR synthesis theory in the design of a multivariable engine control system for operation throughout the flight envelope. The advantages of such procedures include: (1) enhanced performance from cross-coupled controls, (2) maximum use of engine variable geometry, and (3) a systematic design procedure that can be applied efficiently to new engine systems. The control system designed, under the MVCS program, for the Pratt & Whitney F100 turbofan engine is described. Basic components of the control include: (1) a reference value generator for deriving a desired equilibrium state and an approximate control vector, (2) a transition model to produce compatible reference point trajectories during gross transients, (3) gain schedules for producing feedback terms appropriate to the flight condition, and (4) integral switching logic to produce acceptable steady-state performance without engine operating limit exceedance.

A homotopy algorithm for synthesizing robust controllers for flexible structures via the maximum entropy design equations

NASA Technical Reports Server (NTRS)

Collins, Emmanuel G., Jr.; Richter, Stephen

1990-01-01

One well known deficiency of LQG compensators is that they do not guarantee any measure of robustness. This deficiency is especially highlighted when considering control design for complex systems such as flexible structures. There has thus been a need to generalize LQG theory to incorporate robustness constraints. Here we describe the maximum entropy approach to robust control design for flexible structures, a generalization of LQG theory, pioneered by Hyland, which has proved useful in practice. The design equations consist of a set of coupled Riccati and Lyapunov equations. A homotopy algorithm that is used to solve these design equations is presented.

Robust adaptive tracking control for nonholonomic mobile manipulator with uncertainties.

PubMed

Peng, Jinzhu; Yu, Jie; Wang, Jie

2014-07-01

In this paper, mobile manipulator is divided into two subsystems, that is, nonholonomic mobile platform subsystem and holonomic manipulator subsystem. First, the kinematic controller of the mobile platform is derived to obtain a desired velocity. Second, regarding the coupling between the two subsystems as disturbances, Lyapunov functions of the two subsystems are designed respectively. Third, a robust adaptive tracking controller is proposed to deal with the unknown upper bounds of parameter uncertainties and disturbances. According to the Lyapunov stability theory, the derived robust adaptive controller guarantees global stability of the closed-loop system, and the tracking errors and adaptive coefficient errors are all bounded. Finally, simulation results show that the proposed robust adaptive tracking controller for nonholonomic mobile manipulator is effective and has good tracking capacity. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Some Modified Integrated Squared Error Procedures for Multivariate Normal Data.

DTIC Science & Technology

1982-06-01

p-dimensional Gaussian. There are a number of measures of qualitative robustness but the most important is the influence function . Most of the other...measures are derived from the influence function . The influence function is simply proportional to the score function (Huber, 1981, p. 45 ). The... influence function at the p-variate Gaussian distribution Np (UV) is as -1P IC(x; ,N) = IE&) ;-") sD=XV = (I+c) (p+2)(x-p) exp(- ! (x-p) TV-.1-)) (3.6

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

PubMed

Zhang, Qichao; Zhao, Dongbin; Wang, Ding

2018-01-01

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

Robust Cytokine and Chemokine Response in Nasopharyngeal Secretions: Association With Decreased Severity in Children With Physician Diagnosed Bronchiolitis

PubMed Central

Nicholson, Erin G.; Schlegel, Chelsea; Garofalo, Roberto P.; Mehta, Reena; Scheffler, Margaret; Mei, Minghua; Piedra, Pedro A.

2016-01-01

Background. Bronchiolitis causes substantial disease in young children. Previous findings had indicated that a robust innate immune response was not associated with a poor clinical outcome in bronchiolitis. This study tested the hypothesis that increased concentrations of cytokines and chemokines in nasal wash specimens were associated with decreased severity in bronchiolitis. Methods. Children <24 months old who presented to the emergency department with signs and symptoms of bronchiolitis were eligible for enrollment. Nasal wash specimens were analyzed for viral pathogens and cytokine/chemokine concentrations. These results were evaluated with regard to disposition. Results. One hundred eleven children with bronchiolitis were enrolled. A viral pathogen was identified in 91.9% of patients (respiratory syncytial virus in 51.4%, human rhinovirus in 11.7%). Higher levels of cytokines and chemokines (interferon [IFN] γ; interleukin [IL] 4, 15, and 17; CXCL10; and eotaxin) were significantly associated with a decreased risk of hospitalization. IL-17, IL-4, IFN-γ, and IFN-γ–inducible protein 10 (CXCL10 or IP-10) remained statistically significant in the multivariate analyses. Conclusions. The cytokines and chemokines significantly associated with decreased bronchiolitis severity are classified in a wide range of functional groups (T-helper 1 and 2, regulatory, and chemoattractant). The involvement of these functional groups suggest that a broadly overlapping cytokine/chemokine response is required for control of virus-mediated respiratory disease in young children. PMID:27190183

Transthoracic echocardiography and mortality in sepsis: analysis of the MIMIC-III database.

PubMed

Feng, Mengling; McSparron, Jakob I; Kien, Dang Trung; Stone, David J; Roberts, David H; Schwartzstein, Richard M; Vieillard-Baron, Antoine; Celi, Leo Anthony

2018-06-01

While the use of transthoracic echocardiography (TTE) in the ICU is rapidly expanding, the contribution of TTE to altering patient outcomes among ICU patients with sepsis has not been examined. This study was designed to examine the association of TTE with 28-day mortality specifically in that population. The MIMIC-III database was employed to identify patients with sepsis who had and had not received TTE. The statistical approaches utilized included multivariate regression, propensity score analysis, doubly robust estimation, the gradient boosted model, and an inverse probability-weighting model to ensure the robustness of our findings. Significant benefit in terms of 28-day mortality was observed among the TTE patients compared to the control (no TTE) group (odds ratio = 0.78, 95% CI 0.68-0.90, p < 0.001). The amount of fluid administered (2.5 vs. 2.1 L on day 1, p < 0.001), use of dobutamine (2% vs. 1%, p = 0.007), and the maximum dose of norepinephrine (1.4 vs. 1 mg/min, p = 0.001) were significantly higher for the TTE patients. Importantly, the TTE patients were weaned off vasopressors more quickly than those in the no TTE group (vasopressor-free days on day 28 of 21 vs. 19, p = 0.004). In a general population of critically ill patients with sepsis, use of TTE is associated with an improvement in 28-day mortality.

Generating level-dependent models of cervical and thoracic spinal cord injury: Exploring the interplay of neuroanatomy, physiology, and function.

PubMed

Wilcox, Jared T; Satkunendrarajah, Kajana; Nasirzadeh, Yasmin; Laliberte, Alex M; Lip, Alyssa; Cadotte, David W; Foltz, Warren D; Fehlings, Michael G

2017-09-01

The majority of spinal cord injuries (SCI) occur at the cervical level, which results in significant impairment. Neurologic level and severity of injury are primary endpoints in clinical trials; however, how level-specific damages relate to behavioural performance in cervical injury is incompletely understood. We hypothesized that ascending level of injury leads to worsening forelimb performance, and correlates with loss of neural tissue and muscle-specific neuron pools. A direct comparison of multiple models was made with injury realized at the C5, C6, C7 and T7 vertebral levels using clip compression with sham-operated controls. Animals were assessed for 10weeks post-injury with numerous (40) outcome measures, including: classic behavioural tests, CatWalk, non-invasive MRI, electrophysiology, histologic lesion morphometry, neuron counts, and motor compartment quantification, and multivariate statistics on the total dataset. Histologic staining and T1-weighted MR imaging revealed similar structural changes and distinct tissue loss with cystic cavitation across all injuries. Forelimb tests, including grip strength, F-WARP motor scale, Inclined Plane, and forelimb ladder walk, exhibited stratification between all groups and marked impairment with C5 and C6 injuries. Classic hindlimb tests including BBB, hindlimb ladder walk, bladder recovery, and mortality were not different between cervical and thoracic injuries. CatWalk multivariate gait analysis showed reciprocal and progressive changes forelimb and hindlimb function with ascending level of injury. Electrophysiology revealed poor forelimb axonal conduction in cervical C5 and C6 groups alone. The cervical enlargement (C5-T2) showed progressive ventral horn atrophy and loss of specific motor neuron populations with ascending injury. Multivariate statistics revealed a robust dataset, rank-order contribution of outcomes, and allowed prediction of injury level with single-level discrimination using forelimb performance and neuron counts. Level-dependent models were generated using clip-compression SCI, with marked and reliable differences in forelimb performance and specific neuron pool loss. Copyright © 2017 Elsevier Inc. All rights reserved.

imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel.

PubMed

Grapov, Dmitry; Newman, John W

2012-09-01

Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010).

Feedback system design with an uncertain plant

NASA Technical Reports Server (NTRS)

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

1986-01-01

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

A Robust Inner and Outer Loop Control Method for Trajectory Tracking of a Quadrotor

PubMed Central

Xia, Dunzhu; Cheng, Limei; Yao, Yanhong

2017-01-01

In order to achieve the complicated trajectory tracking of quadrotor, a geometric inner and outer loop control scheme is presented. The outer loop generates the desired rotation matrix for the inner loop. To improve the response speed and robustness, a geometric SMC controller is designed for the inner loop. The outer loop is also designed via sliding mode control (SMC). By Lyapunov theory and cascade theory, the closed-loop system stability is guaranteed. Next, the tracking performance is validated by tracking three representative trajectories. Then, the robustness of the proposed control method is illustrated by trajectory tracking in presence of model uncertainty and disturbances. Subsequently, experiments are carried out to verify the method. In the experiment, ultra wideband (UWB) is used for indoor positioning. Extended Kalman Filter (EKF) is used for fusing inertial measurement unit (IMU) and UWB measurements. The experimental results show the feasibility of the designed controller in practice. The comparative experiments with PD and PD loop demonstrate the robustness of the proposed control method. PMID:28925984

Robust Frequency-Domain Constrained Feedback Design via a Two-Stage Heuristic Approach.

PubMed

Li, Xianwei; Gao, Huijun

2015-10-01

Based on a two-stage heuristic method, this paper is concerned with the design of robust feedback controllers with restricted frequency-domain specifications (RFDSs) for uncertain linear discrete-time systems. Polytopic uncertainties are assumed to enter all the system matrices, while RFDSs are motivated by the fact that practical design specifications are often described in restricted finite frequency ranges. Dilated multipliers are first introduced to relax the generalized Kalman-Yakubovich-Popov lemma for output feedback controller synthesis and robust performance analysis. Then a two-stage approach to output feedback controller synthesis is proposed: at the first stage, a robust full-information (FI) controller is designed, which is used to construct a required output feedback controller at the second stage. To improve the solvability of the synthesis method, heuristic iterative algorithms are further formulated for exploring the feedback gain and optimizing the initial FI controller at the individual stage. The effectiveness of the proposed design method is finally demonstrated by the application to active control of suspension systems.

Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI

PubMed Central

Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Treviño, Victor; Tamez-Peña, José G.

2015-01-01

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. PMID:26504490

Design of a robust control law for the Vega launcher ballistic phase

NASA Astrophysics Data System (ADS)

Valli, Monica; Lavagna, Michèle R.; Panozzo, Thomas

2012-02-01

This work presents the design of a robust control law, and the related control system architecture, for the Vega launcher ballistic phase, taking into account the complete six degrees of freedom dynamics. To gain robustness a non-linear control approach has been preferred: more specifically the Lyapunov's second stability theorem has been exploited, being a very powerful tool to guarantee asymptotic stability of the controlled dynamics. The dynamics of Vega's actuators has also been taken into account. The system performance has been checked and analyzed by numerical simulations run on real mission data for different operational and configuration scenarios, and the effectiveness of the synthesized control highlighted: in particular scenarios including a wide range of composite's inertial configurations performing various typologies of maneuvers have been run. The robustness of the controlled dynamics has been validated by 100 cases Monte Carlo analysis campaign: the containment of the dispersion for the controlled variables - say the composite roll, yaw and pitch angles - confirmed the wide validity and generality of the proposed control law. This paper will show the theoretical approach and discuss the obtained results.

Structural analysis and design of multivariable control systems: An algebraic approach

NASA Technical Reports Server (NTRS)

Tsay, Yih Tsong; Shieh, Leang-San; Barnett, Stephen

1988-01-01

The application of algebraic system theory to the design of controllers for multivariable (MV) systems is explored analytically using an approach based on state-space representations and matrix-fraction descriptions. Chapters are devoted to characteristic lambda matrices and canonical descriptions of MIMO systems; spectral analysis, divisors, and spectral factors of nonsingular lambda matrices; feedback control of MV systems; and structural decomposition theories and their application to MV control systems.

Aperiodic Robust Model Predictive Control for Constrained Continuous-Time Nonlinear Systems: An Event-Triggered Approach.

PubMed

Liu, Changxin; Gao, Jian; Li, Huiping; Xu, Demin

2018-05-01

The event-triggered control is a promising solution to cyber-physical systems, such as networked control systems, multiagent systems, and large-scale intelligent systems. In this paper, we propose an event-triggered model predictive control (MPC) scheme for constrained continuous-time nonlinear systems with bounded disturbances. First, a time-varying tightened state constraint is computed to achieve robust constraint satisfaction, and an event-triggered scheduling strategy is designed in the framework of dual-mode MPC. Second, the sufficient conditions for ensuring feasibility and closed-loop robust stability are developed, respectively. We show that robust stability can be ensured and communication load can be reduced with the proposed MPC algorithm. Finally, numerical simulations and comparison studies are performed to verify the theoretical results.

Robust control of systems with real parameter uncertainty and unmodelled dynamics

NASA Technical Reports Server (NTRS)

Chang, Bor-Chin; Fischl, Robert

1991-01-01

During this research period we have made significant progress in the four proposed areas: (1) design of robust controllers via H infinity optimization; (2) design of robust controllers via mixed H2/H infinity optimization; (3) M-delta structure and robust stability analysis for structured uncertainties; and (4) a study on controllability and observability of perturbed plant. It is well known now that the two-Riccati-equation solution to the H infinity control problem can be used to characterize all possible stabilizing optimal or suboptimal H infinity controllers if the optimal H infinity norm or gamma, an upper bound of a suboptimal H infinity norm, is given. In this research, we discovered some useful properties of these H infinity Riccati solutions. Among them, the most prominent one is that the spectral radius of the product of these two Riccati solutions is a continuous, nonincreasing, convex function of gamma in the domain of interest. Based on these properties, quadratically convergent algorithms are developed to compute the optimal H infinity norm. We also set up a detailed procedure for applying the H infinity theory to robust control systems design. The desire to design controllers with H infinity robustness but H(exp 2) performance has recently resulted in mixed H(exp 2) and H infinity control problem formulation. The mixed H(exp 2)/H infinity problem have drawn the attention of many investigators. However, solution is only available for special cases of this problem. We formulated a relatively realistic control problem with H(exp 2) performance index and H infinity robustness constraint into a more general mixed H(exp 2)/H infinity problem. No optimal solution yet is available for this more general mixed H(exp 2)/H infinity problem. Although the optimal solution for this mixed H(exp 2)/H infinity control has not yet been found, we proposed a design approach which can be used through proper choice of the available design parameters to influence both robustness and performance. For a large class of linear time-invariant systems with real parametric perturbations, the coefficient vector of the characteristic polynomial is a multilinear function of the real parameter vector. Based on this multilinear mapping relationship together with the recent developments for polytopic polynomials and parameter domain partition technique, we proposed an iterative algorithm for coupling the real structured singular value.

Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line

PubMed Central

Li, Peng; Zhao, Na; Zhou, Donghua; Cao, Min; Li, Jingjie; Shi, Xinling

2014-01-01

The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model's prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters. PMID:25136653

Vectored Thrust Digital Flight Control for Crew Escape. Volume 2.

DTIC Science & Technology

1985-12-01

no. 24. Lecrique, J., A. Rault, M. Tessier and J.L. Testud (1978), - "Multivariable Regulation of a Thermal Power Plant Steam Generator," presented...and Extended Kalman Observers," presented at the Conf. Decision and Control, San Diego, CA. Testud , J.L. (1977), Commande Numerique Multivariable du

Application of advanced control techniques to aircraft propulsion systems

NASA Technical Reports Server (NTRS)

Lehtinen, B.

1984-01-01

Two programs are described which involve the application of advanced control techniques to the design of engine control algorithms. Multivariable control theory is used in the F100 MVCS (multivariable control synthesis) program to design controls which coordinate the control inputs for improved engine performance. A systematic method for handling a complex control design task is given. Methods of analytical redundancy are aimed at increasing the control system reliability. The F100 DIA (detection, isolation, and accommodation) program, which investigates the uses of software to replace or augment hardware redundancy for certain critical engine sensor, is described.

Practical Methods for the Compensation and Control of Multivariable Systems.

DTIC Science & Technology

1982-04-01

a constant gain element gji . To be more specific, let us consider a linear multivariable system whose dynamical behavior is specified by a (pxm...controllable via uk if Yi is fed back to uj via an arbitrary gain gji , as depicted in the figure below? It might be noted that only the outputs and inputs...modes controllable via uk(s) before feedback will remain -19- controllable via uk(s) irrespective of gji (although certain of these uk controllable

Power and sample size for multivariate logistic modeling of unmatched case-control studies.

PubMed

Gail, Mitchell H; Haneuse, Sebastien

2017-01-01

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

Robust fuzzy control subject to state variance and passivity constraints for perturbed nonlinear systems with multiplicative noises.

PubMed

Chang, Wen-Jer; Huang, Bo-Jyun

2014-11-01

The multi-constrained robust fuzzy control problem is investigated in this paper for perturbed continuous-time nonlinear stochastic systems. The nonlinear system considered in this paper is represented by a Takagi-Sugeno fuzzy model with perturbations and state multiplicative noises. The multiple performance constraints considered in this paper include stability, passivity and individual state variance constraints. The Lyapunov stability theory is employed to derive sufficient conditions to achieve the above performance constraints. By solving these sufficient conditions, the contribution of this paper is to develop a parallel distributed compensation based robust fuzzy control approach to satisfy multiple performance constraints for perturbed nonlinear systems with multiplicative noises. At last, a numerical example for the control of perturbed inverted pendulum system is provided to illustrate the applicability and effectiveness of the proposed multi-constrained robust fuzzy control method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Analysis techniques for multivariate root loci. [a tool in linear control systems

NASA Technical Reports Server (NTRS)

Thompson, P. M.; Stein, G.; Laub, A. J.

1980-01-01

Analysis and techniques are developed for the multivariable root locus and the multivariable optimal root locus. The generalized eigenvalue problem is used to compute angles and sensitivities for both types of loci, and an algorithm is presented that determines the asymptotic properties of the optimal root locus.

Robust control of seismically excited cable stayed bridges with MR dampers

NASA Astrophysics Data System (ADS)

YeganehFallah, Arash; Khajeh Ahamd Attari, Nader

2017-03-01

In recent decades active and semi-active structural control are becoming attractive alternatives for enhancing performance of civil infrastructures subjected to seismic and winds loads. However, in order to have reliable active and semi-active control, there is a need to include information of uncertainties in design of the controller. In real world for civil structures, parameters such as loading places, stiffness, mass and damping are time variant and uncertain. These uncertainties in many cases model as parametric uncertainties. The motivation of this research is to design a robust controller for attenuating the vibrational responses of civil infrastructures, regarding their dynamical uncertainties. Uncertainties in structural dynamic’s parameters are modeled as affine uncertainties in state space modeling. These uncertainties are decoupled from the system through Linear Fractional Transformation (LFT) and are assumed to be unknown input to the system but norm bounded. The robust H ∞ controller is designed for the decoupled system to regulate the evaluation outputs and it is robust to effects of uncertainties, disturbance and sensors noise. The cable stayed bridge benchmark which is equipped with MR damper is considered for the numerical simulation. The simulated results show that the proposed robust controller can effectively mitigate undesired uncertainties effects on systems’ responds under seismic loading.

A Low-Power Wireless Image Sensor Node with Noise-Robust Moving Object Detection and a Region-of-Interest Based Rate Controller

DTIC Science & Technology

2017-03-01

A Low- Power Wireless Image Sensor Node with Noise-Robust Moving Object Detection and a Region-of-Interest Based Rate Controller Jong Hwan Ko...Atlanta, GA 30332 USA Contact Author Email: jonghwan.ko@gatech.edu Abstract: This paper presents a low- power wireless image sensor node for...present a low- power wireless image sensor node with a noise-robust moving object detection and region-of-interest based rate controller [Fig. 1]. The

Robust H ∞ Control for Spacecraft Rendezvous with a Noncooperative Target

PubMed Central

Wu, Shu-Nan; Zhou, Wen-Ya; Tan, Shu-Jun; Wu, Guo-Qiang

2013-01-01

The robust H ∞ control for spacecraft rendezvous with a noncooperative target is addressed in this paper. The relative motion of chaser and noncooperative target is firstly modeled as the uncertain system, which contains uncertain orbit parameter and mass. Then the H ∞ performance and finite time performance are proposed, and a robust H ∞ controller is developed to drive the chaser to rendezvous with the non-cooperative target in the presence of control input saturation, measurement error, and thrust error. The linear matrix inequality technology is used to derive the sufficient condition of the proposed controller. An illustrative example is finally provided to demonstrate the effectiveness of the controller. PMID:24027446

Multivariate Associations Among Behavioral, Clinical, and Multimodal Imaging Phenotypes in Patients With Psychosis.

PubMed

Moser, Dominik A; Doucet, Gaelle E; Lee, Won Hee; Rasgon, Alexander; Krinsky, Hannah; Leibu, Evan; Ing, Alex; Schumann, Gunter; Rasgon, Natalie; Frangou, Sophia

2018-04-01

Alterations in multiple neuroimaging phenotypes have been reported in psychotic disorders. However, neuroimaging measures can be influenced by factors that are not directly related to psychosis and may confound the interpretation of case-control differences. Therefore, a detailed characterization of the contribution of these factors to neuroimaging phenotypes in psychosis is warranted. To quantify the association between neuroimaging measures and behavioral, health, and demographic variables in psychosis using an integrated multivariate approach. This imaging study was conducted at a university research hospital from June 26, 2014, to March 9, 2017. High-resolution multimodal magnetic resonance imaging data were obtained from 100 patients with schizophrenia, 40 patients with bipolar disorder, and 50 healthy volunteers; computed were cortical thickness, subcortical volumes, white matter fractional anisotropy, task-related brain activation (during working memory and emotional recognition), and resting-state functional connectivity. Ascertained in all participants were nonimaging measures pertaining to clinical features, cognition, substance use, psychological trauma, physical activity, and body mass index. The association between imaging and nonimaging measures was modeled using sparse canonical correlation analysis with robust reliability testing. Multivariate patterns of the association between nonimaging and neuroimaging measures in patients with psychosis and healthy volunteers. The analyses were performed in 92 patients with schizophrenia (23 female [25.0%]; mean [SD] age, 27.0 [7.6] years), 37 patients with bipolar disorder (12 female [32.4%]; mean [SD] age, 27.5 [8.1] years), and 48 healthy volunteers (20 female [41.7%]; mean [SD] age, 29.8 [8.5] years). The imaging and nonimaging data sets showed significant covariation (r = 0.63, P < .001), which was independent of diagnosis. Among the nonimaging variables examined, age (r = -0.53), IQ (r = 0.36), and body mass index (r = -0.25) were associated with multiple imaging phenotypes; cannabis use (r = 0.23) and other substance use (r = 0.33) were associated with subcortical volumes, and alcohol use was associated with white matter integrity (r = -0.15). Within the multivariate models, positive symptoms retained associations with the global neuroimaging (r = -0.13), the cortical thickness (r = -0.22), and the task-related activation variates (r = -0.18); negative symptoms were mostly associated with measures of subcortical volume (r = 0.23), and depression/anxiety was associated with measures of white matter integrity (r = 0.12). Multivariate analyses provide a more accurate characterization of the association between brain alterations and psychosis because they enable the modeling of other key factors that influence neuroimaging phenotypes.

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

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

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less

Robust Control of Uncertain Systems via Dissipative LQG-Type Controllers

NASA Technical Reports Server (NTRS)

Joshi, Suresh M.

2000-01-01

Optimal controller design is addressed for a class of linear, time-invariant systems which are dissipative with respect to a quadratic power function. The system matrices are assumed to be affine functions of uncertain parameters confined to a convex polytopic region in the parameter space. For such systems, a method is developed for designing a controller which is dissipative with respect to a given power function, and is simultaneously optimal in the linear-quadratic-Gaussian (LQG) sense. The resulting controller provides robust stability as well as optimal performance. Three important special cases, namely, passive, norm-bounded, and sector-bounded controllers, which are also LQG-optimal, are presented. The results give new methods for robust controller design in the presence of parametric uncertainties.

An Improved Method to Control the Critical Parameters of a Multivariable Control System

NASA Astrophysics Data System (ADS)

Subha Hency Jims, P.; Dharmalingam, S.; Wessley, G. Jims John

2017-10-01

The role of control systems is to cope with the process deficiencies and the undesirable effect of the external disturbances. Most of the multivariable processes are highly iterative and complex in nature. Aircraft systems, Modern Power Plants, Refineries, Robotic systems are few such complex systems that involve numerous critical parameters that need to be monitored and controlled. Control of these important parameters is not only tedious and cumbersome but also is crucial from environmental, safety and quality perspective. In this paper, one such multivariable system, namely, a utility boiler has been considered. A modern power plant is a complex arrangement of pipework and machineries with numerous interacting control loops and support systems. In this paper, the calculation of controller parameters based on classical tuning concepts has been presented. The controller parameters thus obtained and employed has controlled the critical parameters of a boiler during fuel switching disturbances. The proposed method can be applied to control the critical parameters like elevator, aileron, rudder, elevator trim rudder and aileron trim, flap control systems of aircraft systems.

Advanced Control Synthesis for Reverse Osmosis Water Desalination Processes.

PubMed

Phuc, Bui Duc Hong; You, Sam-Sang; Choi, Hyeung-Six; Jeong, Seok-Kwon

2017-11-01

  In this study, robust control synthesis has been applied to a reverse osmosis desalination plant whose product water flow and salinity are chosen as two controlled variables. The reverse osmosis process has been selected to study since it typically uses less energy than thermal distillation. The aim of the robust design is to overcome the limitation of classical controllers in dealing with large parametric uncertainties, external disturbances, sensor noises, and unmodeled process dynamics. The analyzed desalination process is modeled as a multi-input multi-output (MIMO) system with varying parameters. The control system is decoupled using a feed forward decoupling method to reduce the interactions between control channels. Both nominal and perturbed reverse osmosis systems have been analyzed using structured singular values for their stabilities and performances. Simulation results show that the system responses meet all the control requirements against various uncertainties. Finally the reduced order controller provides excellent robust performance, with achieving decoupling, disturbance attenuation, and noise rejection. It can help to reduce the membrane cleanings, increase the robustness against uncertainties, and lower the energy consumption for process monitoring.

Robust iterative learning control for multi-phase batch processes: an average dwell-time method with 2D convergence indexes

NASA Astrophysics Data System (ADS)

Wang, Limin; Shen, Yiteng; Yu, Jingxian; Li, Ping; Zhang, Ridong; Gao, Furong

2018-01-01

In order to cope with system disturbances in multi-phase batch processes with different dimensions, a hybrid robust control scheme of iterative learning control combined with feedback control is proposed in this paper. First, with a hybrid iterative learning control law designed by introducing the state error, the tracking error and the extended information, the multi-phase batch process is converted into a two-dimensional Fornasini-Marchesini (2D-FM) switched system with different dimensions. Second, a switching signal is designed using the average dwell-time method integrated with the related switching conditions to give sufficient conditions ensuring stable running for the system. Finally, the minimum running time of the subsystems and the control law gains are calculated by solving the linear matrix inequalities. Meanwhile, a compound 2D controller with robust performance is obtained, which includes a robust extended feedback control for ensuring the steady-state tracking error to converge rapidly. The application on an injection molding process displays the effectiveness and superiority of the proposed strategy.

Robust control of a parallel hybrid drivetrain with a CVT

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

Mayer, T.; Schroeder, D.

1996-09-01

In this paper the design of a robust control system for a parallel hybrid drivetrain is presented. The drivetrain is based on a continuously variable transmission (CVT) and is therefore a highly nonlinear multiple-input-multiple-output system (MIMO-System). Input-Output-Linearization offers the possibility of linearizing and of decoupling the system. Since for example the vehicle mass varies with the load and the efficiency of the gearbox depends strongly on the actual working point, an exact linearization of the plant will mostly fail. Therefore a robust control algorithm based on sliding mode is used to control the drivetrain.

Baseline estimation in flame's spectra by using neural networks and robust statistics

NASA Astrophysics Data System (ADS)

Garces, Hugo; Arias, Luis; Rojas, Alejandro

2014-09-01

This work presents a baseline estimation method in flame spectra based on artificial intelligence structure as a neural network, combining robust statistics with multivariate analysis to automatically discriminate measured wavelengths belonging to continuous feature for model adaptation, surpassing restriction of measuring target baseline for training. The main contributions of this paper are: to analyze a flame spectra database computing Jolliffe statistics from Principal Components Analysis detecting wavelengths not correlated with most of the measured data corresponding to baseline; to systematically determine the optimal number of neurons in hidden layers based on Akaike's Final Prediction Error; to estimate baseline in full wavelength range sampling measured spectra; and to train an artificial intelligence structure as a Neural Network which allows to generalize the relation between measured and baseline spectra. The main application of our research is to compute total radiation with baseline information, allowing to diagnose combustion process state for optimization in early stages.

Gain-scheduling multivariable LPV control of an irrigation canal system.

PubMed

Bolea, Yolanda; Puig, Vicenç

2016-07-01

The purpose of this paper is to present a multivariable linear parameter varying (LPV) controller with a gain scheduling Smith Predictor (SP) scheme applicable to open-flow canal systems. This LPV controller based on SP is designed taking into account the uncertainty in the estimation of delay and the variation of plant parameters according to the operating point. This new methodology can be applied to a class of delay systems that can be represented by a set of models that can be factorized into a rational multivariable model in series with left/right diagonal (multiple) delays, such as, the case of irrigation canals. A multiple pool canal system is used to test and validate the proposed control approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Robust consensus algorithm for multi-agent systems with exogenous disturbances under convergence conditions

NASA Astrophysics Data System (ADS)

Jiang, Yulian; Liu, Jianchang; Tan, Shubin; Ming, Pingsong

2014-09-01

In this paper, a robust consensus algorithm is developed and sufficient conditions for convergence to consensus are proposed for a multi-agent system (MAS) with exogenous disturbances subject to partial information. By utilizing H∞ robust control, differential game theory and a design-based approach, the consensus problem of the MAS with exogenous bounded interference is resolved and the disturbances are restrained, simultaneously. Attention is focused on designing an H∞ robust controller (the robust consensus algorithm) based on minimisation of our proposed rational and individual cost functions according to goals of the MAS. Furthermore, sufficient conditions for convergence of the robust consensus algorithm are given. An example is employed to demonstrate that our results are effective and more capable to restrain exogenous disturbances than the existing literature.

Adaptive integral robust control and application to electromechanical servo systems.

PubMed

Deng, Wenxiang; Yao, Jianyong

2017-03-01

This paper proposes a continuous adaptive integral robust control with robust integral of the sign of the error (RISE) feedback for a class of uncertain nonlinear systems, in which the RISE feedback gain is adapted online to ensure the robustness against disturbances without the prior bound knowledge of the additive disturbances. In addition, an adaptive compensation integrated with the proposed adaptive RISE feedback term is also constructed to further reduce design conservatism when the system also exists parametric uncertainties. Lyapunov analysis reveals the proposed controllers could guarantee the tracking errors are asymptotically converging to zero with continuous control efforts. To illustrate the high performance nature of the developed controllers, numerical simulations are provided. At the end, an application case of an actual electromechanical servo system driven by motor is also studied, with some specific design consideration, and comparative experimental results are obtained to verify the effectiveness of the proposed controllers. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Stochastic Control Synthesis of Systems with Structured Uncertainty

NASA Technical Reports Server (NTRS)

Padula, Sharon L. (Technical Monitor); Crespo, Luis G.

2003-01-01

This paper presents a study on the design of robust controllers by using random variables to model structured uncertainty for both SISO and MIMO feedback systems. Once the parameter uncertainty is prescribed with probability density functions, its effects are propagated through the analysis leading to stochastic metrics for the system's output. Control designs that aim for satisfactory performances while guaranteeing robust closed loop stability are attained by solving constrained non-linear optimization problems in the frequency domain. This approach permits not only to quantify the probability of having unstable and unfavorable responses for a particular control design but also to search for controls while favoring the values of the parameters with higher chance of occurrence. In this manner, robust optimality is achieved while the characteristic conservatism of conventional robust control methods is eliminated. Examples that admit closed form expressions for the probabilistic metrics of the output are used to elucidate the nature of the problem at hand and validate the proposed formulations.

Enhanced Attitude Control Experiment for SSTI Lewis Spacecraft

NASA Technical Reports Server (NTRS)

Maghami, Peoman G.

1997-01-01

The enhanced attitude control system experiment is a technology demonstration experiment on the NASA's small spacecraft technology initiative program's Lewis spacecraft to evaluate advanced attitude control strategies. The purpose of the enhanced attitude control system experiment is to evaluate the feasibility of designing and implementing robust multi-input/multi-output attitude control strategies for enhanced pointing performance of spacecraft to improve the quality of the measurements of the science instruments. Different control design strategies based on modern and robust control theories are being considered for the enhanced attitude control system experiment. This paper describes the experiment as well as the design and synthesis of a mixed H(sub 2)/H(sub infinity) controller for attitude control. The control synthesis uses a nonlinear programming technique to tune the controller parameters and impose robustness and performance constraints. Simulations are carried out to demonstrate the feasibility of the proposed attitude control design strategy. Introduction

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

Neeway, James J.; Rieke, Peter C.; Parruzot, Benjamin P.

In far-from-equilibrium conditions, the dissolution of borosilicate glasses used to immobilize nuclear waste is known to be a function of both temperature and pH. The aim of this paper is to study effects of these variables on three model waste glasses (SON68, ISG, AFCI). To do this, experiments were conducted at temperatures of 23, 40, 70, and 90 °C and pH(RT) values of 9, 10, 11, and 12 with the single-pass flow-through (SPFT) test method. The results from these tests were then used to parameterize a kinetic rate model based on transition state theory. Both the absolute dissolution rates andmore » the rate model parameters are compared with previous results. Discrepancies in the absolute dissolution rates as compared to those obtained using other test methods are discussed. Rate model parameters for the three glasses studied here are nearly equivalent within error and in relative agreement with previous studies. The results were analyzed with a linear multivariate regression (LMR) and a nonlinear multivariate regression performed with the use of the Glass Corrosion Modeling Tool (GCMT), which is capable of providing a robust uncertainty analysis. This robust analysis highlights the high degree of correlation of various parameters in the kinetic rate model. As more data are obtained on borosilicate glasses with varying compositions, the effect of glass composition on the rate parameter values could possibly be obtained. This would allow for the possibility of predicting the forward dissolution rate of glass based solely on composition« less

Using multiobjective tradeoff sets and Multivariate Regression Trees to identify critical and robust decisions for long term water utility planning

NASA Astrophysics Data System (ADS)

Smith, R.; Kasprzyk, J. R.; Balaji, R.

2017-12-01

In light of deeply uncertain factors like future climate change and population shifts, responsible resource management will require new types of information and strategies. For water utilities, this entails potential expansion and efficient management of water supply infrastructure systems for changes in overall supply; changes in frequency and severity of climate extremes such as droughts and floods; and variable demands, all while accounting for conflicting long and short term performance objectives. Multiobjective Evolutionary Algorithms (MOEAs) are emerging decision support tools that have been used by researchers and, more recently, water utilities to efficiently generate and evaluate thousands of planning portfolios. The tradeoffs between conflicting objectives are explored in an automated way to produce (often large) suites of portfolios that strike different balances of performance. Once generated, the sets of optimized portfolios are used to support relatively subjective assertions of priorities and human reasoning, leading to adoption of a plan. These large tradeoff sets contain information about complex relationships between decisions and between groups of decisions and performance that, until now, has not been quantitatively described. We present a novel use of Multivariate Regression Trees (MRTs) to analyze tradeoff sets to reveal these relationships and critical decisions. Additionally, when MRTs are applied to tradeoff sets developed for different realizations of an uncertain future, they can identify decisions that are robust across a wide range of conditions and produce fundamental insights about the system being optimized.

Single neural adaptive controller and neural network identifier based on PSO algorithm for spherical actuators with 3D magnet array

NASA Astrophysics Data System (ADS)

Yan, Liang; Zhang, Lu; Zhu, Bo; Zhang, Jingying; Jiao, Zongxia

2017-10-01

Permanent magnet spherical actuator (PMSA) is a multi-variable featured and inter-axis coupled nonlinear system, which unavoidably compromises its motion control implementation. Uncertainties such as external load and friction torque of ball bearing and manufacturing errors also influence motion performance significantly. Therefore, the objective of this paper is to propose a controller based on a single neural adaptive (SNA) algorithm and a neural network (NN) identifier optimized with a particle swarm optimization (PSO) algorithm to improve the motion stability of PMSA with three-dimensional magnet arrays. The dynamic model and computed torque model are formulated for the spherical actuator, and a dynamic decoupling control algorithm is developed. By utilizing the global-optimization property of the PSO algorithm, the NN identifier is trained to avoid locally optimal solution and achieve high-precision compensations to uncertainties. The employment of the SNA controller helps to reduce the effect of compensation errors and convert the system to a stable one, even if there is difference between the compensations and uncertainties due to external disturbances. A simulation model is established, and experiments are conducted on the research prototype to validate the proposed control algorithm. The amplitude of the parameter perturbation is set to 5%, 10%, and 15%, respectively. The strong robustness of the proposed hybrid algorithm is validated by the abundant simulation data. It shows that the proposed algorithm can effectively compensate the influence of uncertainties and eliminate the effect of inter-axis couplings of the spherical actuator.

Robust integrated flight/propulsion control design for a STOVL aircraft using H-infinity control design techniques

NASA Technical Reports Server (NTRS)

Garg, Sanjay

1993-01-01

Results are presented from an application of H-infinity control design methodology to a centralized integrated flight/propulsion control (IFPC) system design for a supersonic STOVL fighter aircraft in transition flight. The emphasis is on formulating the H-infinity optimal control synthesis problem such that the critical requirements for the flight and propulsion systems are adequately reflected within the linear, centralized control problem formulation and the resulting controller provides robustness to modeling uncertainties and model parameter variations with flight condition. Detailed evaluation results are presented for a reduced order controller obtained from the improved H-infinity control design showing that the control design meets the specified nominal performance objective as well as provides stability robustness for variations in plant system dynamics with changes in aircraft trim speed within the transition flight envelope.

Robust time and frequency domain estimation methods in adaptive control

NASA Technical Reports Server (NTRS)

Lamaire, Richard Orville

1987-01-01

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

Robustness analysis of non-ordinary Petri nets for flexible assembly systems

NASA Astrophysics Data System (ADS)

Hsieh, Fu-Shiung

2010-05-01

Non-ordinary controlled Petri nets (NCPNs) have the advantages to model flexible assembly systems in which multiple identical resources may be required to perform an operation. However, existing studies on NCPNs are still limited. For example, the robustness properties of NCPNs have not been studied. This motivates us to develop an analysis method for NCPNs. Robustness analysis concerns the ability for a system to maintain operation in the presence of uncertainties. It provides an alternative way to analyse a perturbed system without reanalysis. In our previous research, we have analysed the robustness properties of several subclasses of ordinary controlled Petri nets. To study the robustness properties of NCPNs, we augment NCPNs with an uncertainty model, which specifies an upper bound on the uncertainties for each reachable marking. The resulting PN models are called non-ordinary controlled Petri nets with uncertainties (NCPNU). Based on NCPNU, the problem is to characterise the maximal tolerable uncertainties for each reachable marking. The computational complexities to characterise maximal tolerable uncertainties for each reachable marking grow exponentially with the size of the nets. Instead of considering general NCPNU, we limit our scope to a subclass of PN models called non-ordinary controlled flexible assembly Petri net with uncertainties (NCFAPNU) for assembly systems and study its robustness. We will extend the robustness analysis to NCFAPNU. We identify two types of uncertainties under which the liveness of NCFAPNU can be maintained.

Robust control of the DC-DC boost converter based on the uncertainty and disturbance estimator

NASA Astrophysics Data System (ADS)

Oucheriah, Said

2017-11-01

In this paper, a robust non-linear controller based on the uncertainty and disturbance estimator (UDE) scheme is successfully developed and implemented for the output voltage regulation of the DC-DC boost converter. System uncertainties, external disturbances and unknown non-linear dynamics are lumped as a signal that is accurately estimated using a low-pass filter and their effects are cancelled by the controller. This methodology forms the basis of the UDE-based controller. A simple procedure is also developed that systematically determines the parameters of the controller to meet certain specifications. Using simulation, the effectiveness of the proposed controller is compared against the sliding-mode control (SMC). Experimental tests also show that the proposed controller is robust to system uncertainties, large input and load perturbations.

Robust Optimal Adaptive Control Method with Large Adaptive Gain

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2009-01-01

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient stability robustness. Simulations were conducted for a damaged generic transport aircraft with both standard adaptive control and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model while maintaining a sufficient time delay margin.

Controlled Multivariate Evaluation of Open Education: Application of a Critical Model.

ERIC Educational Resources Information Center

Sewell, Alan F.; And Others

This paper continues previous reports of a controlled multivariate evaluation of a junior high school open-education program. A new method of estimating program objectives and implementation is presented, together with the nature and degree of obtained student outcomes. Open-program students were found to approve more highly of their learning…

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.

Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs.

PubMed

Yan, Binjun; Fang, Zhonghua; Shen, Lijuan; Qu, Haibin

2015-01-01

The batch-to-batch quality consistency of herbal drugs has always been an important issue. To propose a methodology for batch-to-batch quality control based on HPLC-MS fingerprints and process knowledgebase. The extraction process of Compound E-jiao Oral Liquid was taken as a case study. After establishing the HPLC-MS fingerprint analysis method, the fingerprints of the extract solutions produced under normal and abnormal operation conditions were obtained. Multivariate statistical models were built for fault detection and a discriminant analysis model was built using the probabilistic discriminant partial-least-squares method for fault diagnosis. Based on multivariate statistical analysis, process knowledge was acquired and the cause-effect relationship between process deviations and quality defects was revealed. The quality defects were detected successfully by multivariate statistical control charts and the type of process deviations were diagnosed correctly by discriminant analysis. This work has demonstrated the benefits of combining HPLC-MS fingerprints, process knowledge and multivariate analysis for the quality control of herbal drugs. Copyright © 2015 John Wiley & Sons, Ltd.

Multivariable model predictive control design of reactive distillation column for Dimethyl Ether production

NASA Astrophysics Data System (ADS)

Wahid, A.; Putra, I. G. E. P.

2018-03-01

Dimethyl ether (DME) as an alternative clean energy has attracted a growing attention in the recent years. DME production via reactive distillation has potential for capital cost and energy requirement savings. However, combination of reaction and distillation on a single column makes reactive distillation process a very complex multivariable system with high non-linearity of process and strong interaction between process variables. This study investigates a multivariable model predictive control (MPC) based on two-point temperature control strategy for the DME reactive distillation column to maintain the purities of both product streams. The process model is estimated by a first order plus dead time model. The DME and water purity is maintained by controlling a stage temperature in rectifying and stripping section, respectively. The result shows that the model predictive controller performed faster responses compared to conventional PI controller that are showed by the smaller ISE values. In addition, the MPC controller is able to handle the loop interactions well.

Evaluation of the Bitterness of Traditional Chinese Medicines using an E-Tongue Coupled with a Robust Partial Least Squares Regression Method.

PubMed

Lin, Zhaozhou; Zhang, Qiao; Liu, Ruixin; Gao, Xiaojie; Zhang, Lu; Kang, Bingya; Shi, Junhan; Wu, Zidan; Gui, Xinjing; Li, Xuelin

2016-01-25

To accurately, safely, and efficiently evaluate the bitterness of Traditional Chinese Medicines (TCMs), a robust predictor was developed using robust partial least squares (RPLS) regression method based on data obtained from an electronic tongue (e-tongue) system. The data quality was verified by the Grubb's test. Moreover, potential outliers were detected based on both the standardized residual and score distance calculated for each sample. The performance of RPLS on the dataset before and after outlier detection was compared to other state-of-the-art methods including multivariate linear regression, least squares support vector machine, and the plain partial least squares regression. Both R² and root-mean-squares error (RMSE) of cross-validation (CV) were recorded for each model. With four latent variables, a robust RMSECV value of 0.3916 with bitterness values ranging from 0.63 to 4.78 were obtained for the RPLS model that was constructed based on the dataset including outliers. Meanwhile, the RMSECV, which was calculated using the models constructed by other methods, was larger than that of the RPLS model. After six outliers were excluded, the performance of all benchmark methods markedly improved, but the difference between the RPLS model constructed before and after outlier exclusion was negligible. In conclusion, the bitterness of TCM decoctions can be accurately evaluated with the RPLS model constructed using e-tongue data.

Use of the Generating Options for Active Risk Control (GO-ARC) Technique can lead to more robust risk control options.

PubMed

Card, Alan J; Simsekler, Mecit Can Emre; Clark, Michael; Ward, James R; Clarkson, P John

2014-01-01

Risk assessment is widely used to improve patient safety, but healthcare workers are not trained to design robust solutions to the risks they uncover. This leads to an overreliance on the weakest category of risk control recommendations: administrative controls. Increasing the proportion of non-administrative risk control options (NARCOs) generated would enable (though not ensure) the adoption of more robust solutions. Experimentally assess a method for generating stronger risk controls: The Generating Options for Active Risk Control (GO-ARC) Technique. Participants generated risk control options in response to two patient safety scenarios. Scenario 1 (baseline): All participants used current practice (unstructured brainstorming). Scenario 2: Control group used current practice; intervention group used the GO-ARC Technique. To control for individual differences between participants, analysis focused on the change in the proportion of NARCOs for each group. Proportion of NARCOs decreased from 0.18 at baseline to 0.12. Intervention group: Proportion increased from 0.10 at baseline to 0.29 using the GO-ARC Technique. Results were statistically significant. There was no decrease in the number of administrative controls generated by the intervention group. The Generating Options for Active Risk Control (GO-ARC) Technique appears to lead to more robust risk control options.

Evaluation of an F100 multivariable control using a real-time engine simulation

NASA Technical Reports Server (NTRS)

Szuch, J. R.; Soeder, J. F.; Skira, C.

1977-01-01

The control evaluated has been designed for the F100-PW-100 turbofan engine. The F100 engine represents the current state-of-the-art in aircraft gas turbine technology. The control makes use of a multivariable, linear quadratic regulator. The evaluation procedure employed utilized a real-time hybrid computer simulation of the F100 engine and an implementation of the control logic on the NASA LeRC digital computer/controller. The results of the evaluation indicated that the control logic and its implementation will be capable of controlling the engine throughout its operating range.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Robust attitude control design for spacecraft under assigned velocity and control constraints.

PubMed

Hu, Qinglei; Li, Bo; Zhang, Youmin

2013-07-01

A novel robust nonlinear control design under the constraints of assigned velocity and actuator torque is investigated for attitude stabilization of a rigid spacecraft. More specifically, a nonlinear feedback control is firstly developed by explicitly taking into account the constraints on individual angular velocity components as well as external disturbances. Considering further the actuator misalignments and magnitude deviation, a modified robust least-squares based control allocator is employed to deal with the problem of distributing the previously designed three-axis moments over the available actuators, in which the focus of this control allocation is to find the optimal control vector of actuators by minimizing the worst-case residual error using programming algorithms. The attitude control performance using the controller structure is evaluated through a numerical example. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Sliding mode control method having terminal convergence in finite time

NASA Technical Reports Server (NTRS)

Venkataraman, Subramanian T. (Inventor); Gulati, Sandeep (Inventor)

1994-01-01

An object of this invention is to provide robust nonlinear controllers for robotic operations in unstructured environments based upon a new class of closed loop sliding control methods, sometimes denoted terminal sliders, where the new class will enforce closed-loop control convergence to equilibrium in finite time. Improved performance results from the elimination of high frequency control switching previously employed for robustness to parametric uncertainties. Improved performance also results from the dependence of terminal slider stability upon the rate of change of uncertainties over the sliding surface rather than the magnitude of the uncertainty itself for robust control. Terminal sliding mode control also yields improved convergence where convergence time is finite and is to be controlled. A further object is to apply terminal sliders to robot manipulator control and benchmark performance with the traditional computed torque control method and provide for design of control parameters.

Intelligent, Robust Control of Deteriorated Turbofan Engines via Linear Parameter Varying Quadratic Lyapunov Function Design

NASA Technical Reports Server (NTRS)

Turso, James A.; Litt, Jonathan S.

2004-01-01

A method for accommodating engine deterioration via a scheduled Linear Parameter Varying Quadratic Lyapunov Function (LPVQLF)-Based controller is presented. The LPVQLF design methodology provides a means for developing unconditionally stable, robust control of Linear Parameter Varying (LPV) systems. The controller is scheduled on the Engine Deterioration Index, a function of estimated parameters that relate to engine health, and is computed using a multilayer feedforward neural network. Acceptable thrust response and tight control of exhaust gas temperature (EGT) is accomplished by adjusting the performance weights on these parameters for different levels of engine degradation. Nonlinear simulations demonstrate that the controller achieves specified performance objectives while being robust to engine deterioration as well as engine-to-engine variations.

LMI-Based Generation of Feedback Laws for a Robust Model Predictive Control Algorithm

NASA Technical Reports Server (NTRS)

Acikmese, Behcet; Carson, John M., III

2007-01-01

This technical note provides a mathematical proof of Corollary 1 from the paper 'A Nonlinear Model Predictive Control Algorithm with Proven Robustness and Resolvability' that appeared in the 2006 Proceedings of the American Control Conference. The proof was omitted for brevity in the publication. The paper was based on algorithms developed for the FY2005 R&TD (Research and Technology Development) project for Small-body Guidance, Navigation, and Control [2].The framework established by the Corollary is for a robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems that guarantees the resolvability of the associated nite-horizon optimal control problem in a receding-horizon implementation. Additional details of the framework are available in the publication.

Robust multi-model control of an autonomous wind power system

NASA Astrophysics Data System (ADS)

Cutululis, Nicolas Antonio; Ceanga, Emil; Hansen, Anca Daniela; Sørensen, Poul

2006-09-01

This article presents a robust multi-model control structure for a wind power system that uses a variable speed wind turbine (VSWT) driving a permanent magnet synchronous generator (PMSG) connected to a local grid. The control problem consists in maximizing the energy captured from the wind for varying wind speeds. The VSWT-PMSG linearized model analysis reveals the resonant nature of its dynamic at points on the optimal regimes characteristic (ORC). The natural frequency of the system and the damping factor are strongly dependent on the operating point on the ORC. Under these circumstances a robust multi-model control structure is designed. The simulation results prove the viability of the proposed control structure. Copyright

A High-Availability, Distributed Hardware Control System Using Java

NASA Technical Reports Server (NTRS)

Niessner, Albert F.

2011-01-01

Two independent coronagraph experiments that require 24/7 availability with different optical layouts and different motion control requirements are commanded and controlled with the same Java software system executing on many geographically scattered computer systems interconnected via TCP/IP. High availability of a distributed system requires that the computers have a robust communication messaging system making the mix of TCP/IP (a robust transport), and XML (a robust message) a natural choice. XML also adds the configuration flexibility. Java then adds object-oriented paradigms, exception handling, heavily tested libraries, and many third party tools for implementation robustness. The result is a software system that provides users 24/7 access to two diverse experiments with XML files defining the differences

Hypersonic vehicle model and control law development using H(infinity) and micron synthesis

NASA Astrophysics Data System (ADS)

Gregory, Irene M.; Chowdhry, Rajiv S.; McMinn, John D.; Shaughnessy, John D.

1994-10-01

The control system design for a Single Stage To Orbit (SSTO) air breathing vehicle will be central to a successful mission because a precise ascent trajectory will preserve narrow payload margins. The air breathing propulsion system requires the vehicle to fly roughly halfway around the Earth through atmospheric turbulence. The turbulence, the high sensitivity of the propulsion system to inlet flow conditions, the relatively large uncertainty of the parameters characterizing the vehicle, and continuous acceleration make the problem especially challenging. Adequate stability margins must be provided without sacrificing payload mass since payload margins are critical. Therefore, a multivariable control theory capable of explicitly including both uncertainty and performance is needed. The H(infinity) controller in general provides good robustness but can result in conservative solutions for practical problems involving structured uncertainty. Structured singular value mu framework for analysis and synthesis is potentially much less conservative and hence more appropriate for problems with tight margins. An SSTO control system requires: highly accurate tracking of velocity and altitude commands while limiting angle-of-attack oscillations, minimized control power usage, and a stabilized vehicle when atmospheric turbulence and system uncertainty are present. The controller designs using H(infinity) and mu-synthesis procedures were compared. An integrated flight/propulsion dynamic mathematical model of a conical accelerator vehicle was linearized as the vehicle accelerated through Mach 8. Vehicle acceleration through the selected flight condition gives rise to parametric variation that was modeled as a structured uncertainty. The mu-analysis approach was used in the frequency domain to conduct controller analysis and was confirmed by time history plots. Results demonstrate the inherent advantages of the mu framework for this class of problems.

Hypersonic vehicle model and control law development using H(infinity) and micron synthesis

NASA Technical Reports Server (NTRS)

Gregory, Irene M.; Chowdhry, Rajiv S.; Mcminn, John D.; Shaughnessy, John D.

1994-01-01

The control system design for a Single Stage To Orbit (SSTO) air breathing vehicle will be central to a successful mission because a precise ascent trajectory will preserve narrow payload margins. The air breathing propulsion system requires the vehicle to fly roughly halfway around the Earth through atmospheric turbulence. The turbulence, the high sensitivity of the propulsion system to inlet flow conditions, the relatively large uncertainty of the parameters characterizing the vehicle, and continuous acceleration make the problem especially challenging. Adequate stability margins must be provided without sacrificing payload mass since payload margins are critical. Therefore, a multivariable control theory capable of explicitly including both uncertainty and performance is needed. The H(infinity) controller in general provides good robustness but can result in conservative solutions for practical problems involving structured uncertainty. Structured singular value mu framework for analysis and synthesis is potentially much less conservative and hence more appropriate for problems with tight margins. An SSTO control system requires: highly accurate tracking of velocity and altitude commands while limiting angle-of-attack oscillations, minimized control power usage, and a stabilized vehicle when atmospheric turbulence and system uncertainty are present. The controller designs using H(infinity) and mu-synthesis procedures were compared. An integrated flight/propulsion dynamic mathematical model of a conical accelerator vehicle was linearized as the vehicle accelerated through Mach 8. Vehicle acceleration through the selected flight condition gives rise to parametric variation that was modeled as a structured uncertainty. The mu-analysis approach was used in the frequency domain to conduct controller analysis and was confirmed by time history plots. Results demonstrate the inherent advantages of the mu framework for this class of problems.

Reexamining Sample Size Requirements for Multivariate, Abundance-Based Community Research: When Resources are Limited, the Research Does Not Have to Be.

PubMed

Forcino, Frank L; Leighton, Lindsey R; Twerdy, Pamela; Cahill, James F

2015-01-01

Community ecologists commonly perform multivariate techniques (e.g., ordination, cluster analysis) to assess patterns and gradients of taxonomic variation. A critical requirement for a meaningful statistical analysis is accurate information on the taxa found within an ecological sample. However, oversampling (too many individuals counted per sample) also comes at a cost, particularly for ecological systems in which identification and quantification is substantially more resource consuming than the field expedition itself. In such systems, an increasingly larger sample size will eventually result in diminishing returns in improving any pattern or gradient revealed by the data, but will also lead to continually increasing costs. Here, we examine 396 datasets: 44 previously published and 352 created datasets. Using meta-analytic and simulation-based approaches, the research within the present paper seeks (1) to determine minimal sample sizes required to produce robust multivariate statistical results when conducting abundance-based, community ecology research. Furthermore, we seek (2) to determine the dataset parameters (i.e., evenness, number of taxa, number of samples) that require larger sample sizes, regardless of resource availability. We found that in the 44 previously published and the 220 created datasets with randomly chosen abundances, a conservative estimate of a sample size of 58 produced the same multivariate results as all larger sample sizes. However, this minimal number varies as a function of evenness, where increased evenness resulted in increased minimal sample sizes. Sample sizes as small as 58 individuals are sufficient for a broad range of multivariate abundance-based research. In cases when resource availability is the limiting factor for conducting a project (e.g., small university, time to conduct the research project), statistically viable results can still be obtained with less of an investment.

A Study on the Requirements for Fast Active Turbine Tip Clearance Control Systems

NASA Technical Reports Server (NTRS)

DeCastro, Jonathan A.; Melcher, Kevin J.

2004-01-01

This paper addresses the requirements of a control system for active turbine tip clearance control in a generic commercial turbofan engine through design and analysis. The control objective is to articulate the shroud in the high pressure turbine section in order to maintain a certain clearance set point given several possible engine transient events. The system must also exhibit reasonable robustness to modeling uncertainties and reasonable noise rejection properties. Two actuators were chosen to fulfill such a requirement, both of which possess different levels of technological readiness: electrohydraulic servovalves and piezoelectric stacks. Identification of design constraints, desired actuator parameters, and actuator limitations are addressed in depth; all of which are intimately tied with the hardware and controller design process. Analytical demonstrations of the performance and robustness characteristics of the two axisymmetric LQG clearance control systems are presented. Takeoff simulation results show that both actuators are capable of maintaining the clearance within acceptable bounds and demonstrate robustness to parameter uncertainty. The present model-based control strategy was employed to demonstrate the tradeoff between performance, control effort, and robustness and to implement optimal state estimation in a noisy engine environment with intent to eliminate ad hoc methods for designing reliable control systems.

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

PubMed

Hashemi, Mahnaz; Shahgholian, Ghazanfar

2018-03-01

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

Statistical Control Paradigm for Aerospace Structures Under Impulsive Disturbances

DTIC Science & Technology

2006-08-03

attitude control system with an innovative and robust statistical controller design shows significant promise for use in attitude hold mode operation...indicate that the existing attitude control system with an innovative and robust statistical controller design shows significant promise for use in...and three thrusters are for use in controlling the attitude of the satellite. Then the angular momentum of the satellite with three thrusters and a

Optimized pulses for the control of uncertain qubits

DOE PAGES

Grace, Matthew D.; Dominy, Jason M.; Witzel, Wayne M.; ...

2012-05-18

The construction of high-fidelity control fields that are robust to control, system, and/or surrounding environment uncertainties is a crucial objective for quantum information processing. Using the two-state Landau-Zener model for illustrative simulations of a controlled qubit, we generate optimal controls for π/2 and π pulses and investigate their inherent robustness to uncertainty in the magnitude of the drift Hamiltonian. Next, we construct a quantum-control protocol to improve system-drift robustness by combining environment-decoupling pulse criteria and optimal control theory for unitary operations. By perturbatively expanding the unitary time-evolution operator for an open quantum system, previous analysis of environment-decoupling control pulses hasmore » calculated explicit control-field criteria to suppress environment-induced errors up to (but not including) third order from π/2 and π pulses. We systematically integrate this criteria with optimal control theory, incorporating an estimate of the uncertain parameter to produce improvements in gate fidelity and robustness, demonstrated via a numerical example based on double quantum dot qubits. For the qubit model used in this work, postfacto analysis of the resulting controls suggests that realistic control-field fluctuations and noise may contribute just as significantly to gate errors as system and environment fluctuations.« less

Finite-time robust stabilization of uncertain delayed neural networks with discontinuous activations via delayed feedback control.

PubMed

Wang, Leimin; Shen, Yi; Sheng, Yin

2016-04-01

This paper is concerned with the finite-time robust stabilization of delayed neural networks (DNNs) in the presence of discontinuous activations and parameter uncertainties. By using the nonsmooth analysis and control theory, a delayed controller is designed to realize the finite-time robust stabilization of DNNs with discontinuous activations and parameter uncertainties, and the upper bound of the settling time functional for stabilization is estimated. Finally, two examples are provided to demonstrate the effectiveness of the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems.

PubMed

Tulsyan, Aditya; Garvin, Christopher; Ündey, Cenk

2018-04-06

Biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. The state-of-the-art real-time multivariate statistical batch process monitoring (BPM) platforms have been in use in recent years to ensure comprehensive monitoring is in place as a complementary tool for continued process verification to detect weak signals. This article addresses a longstanding, industry-wide problem in BPM, referred to as the "Low-N" problem, wherein a product has a limited production history. The current best industrial practice to address the Low-N problem is to switch from a multivariate to a univariate BPM, until sufficient product history is available to build and deploy a multivariate BPM platform. Every batch run without a robust multivariate BPM platform poses risk of not detecting potential weak signals developing in the process that might have an impact on process and product performance. In this article, we propose an approach to solve the Low-N problem by generating an arbitrarily large number of in silico batches through a combination of hardware exploitation and machine-learning methods. To the best of authors' knowledge, this is the first article to provide a solution to the Low-N problem in biopharmaceutical manufacturing using machine-learning methods. Several industrial case studies from bulk drug substance manufacturing are presented to demonstrate the efficacy of the proposed approach for BPM under various Low-N scenarios. © 2018 Wiley Periodicals, Inc.

Coping with matrix effects in headspace solid phase microextraction gas chromatography using multivariate calibration strategies.

PubMed

Ferreira, Vicente; Herrero, Paula; Zapata, Julián; Escudero, Ana

2015-08-14

SPME is extremely sensitive to experimental parameters affecting liquid-gas and gas-solid distribution coefficients. Our aims were to measure the weights of these factors and to design a multivariate strategy based on the addition of a pool of internal standards, to minimize matrix effects. Synthetic but real-like wines containing selected analytes and variable amounts of ethanol, non-volatile constituents and major volatile compounds were prepared following a factorial design. The ANOVA study revealed that even using a strong matrix dilution, matrix effects are important and additive with non-significant interaction effects and that it is the presence of major volatile constituents the most dominant factor. A single internal standard provided a robust calibration for 15 out of 47 analytes. Then, two different multivariate calibration strategies based on Partial Least Square Regression were run in order to build calibration functions based on 13 different internal standards able to cope with matrix effects. The first one is based in the calculation of Multivariate Internal Standards (MIS), linear combinations of the normalized signals of the 13 internal standards, which provide the expected area of a given unit of analyte present in each sample. The second strategy is a direct calibration relating concentration to the 13 relative areas measured in each sample for each analyte. Overall, 47 different compounds can be reliably quantified in a single fully automated method with overall uncertainties better than 15%. Copyright © 2015 Elsevier B.V. All rights reserved.

Estimating correlation between multivariate longitudinal data in the presence of heterogeneity.

PubMed

Gao, Feng; Philip Miller, J; Xiong, Chengjie; Luo, Jingqin; Beiser, Julia A; Chen, Ling; Gordon, Mae O

2017-08-17

Estimating correlation coefficients among outcomes is one of the most important analytical tasks in epidemiological and clinical research. Availability of multivariate longitudinal data presents a unique opportunity to assess joint evolution of outcomes over time. Bivariate linear mixed model (BLMM) provides a versatile tool with regard to assessing correlation. However, BLMMs often assume that all individuals are drawn from a single homogenous population where the individual trajectories are distributed smoothly around population average. Using longitudinal mean deviation (MD) and visual acuity (VA) from the Ocular Hypertension Treatment Study (OHTS), we demonstrated strategies to better understand the correlation between multivariate longitudinal data in the presence of potential heterogeneity. Conditional correlation (i.e., marginal correlation given random effects) was calculated to describe how the association between longitudinal outcomes evolved over time within specific subpopulation. The impact of heterogeneity on correlation was also assessed by simulated data. There was a significant positive correlation in both random intercepts (ρ = 0.278, 95% CI: 0.121-0.420) and random slopes (ρ = 0.579, 95% CI: 0.349-0.810) between longitudinal MD and VA, and the strength of correlation constantly increased over time. However, conditional correlation and simulation studies revealed that the correlation was induced primarily by participants with rapid deteriorating MD who only accounted for a small fraction of total samples. Conditional correlation given random effects provides a robust estimate to describe the correlation between multivariate longitudinal data in the presence of unobserved heterogeneity (NCT00000125).

Comparative evaluation of spectroscopic models using different multivariate statistical tools in a multicancer scenario

NASA Astrophysics Data System (ADS)

Ghanate, A. D.; Kothiwale, S.; Singh, S. P.; Bertrand, Dominique; Krishna, C. Murali

2011-02-01

Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.

Robust hopping based on virtual pendulum posture control.

PubMed

Sharbafi, Maziar A; Maufroy, Christophe; Ahmadabadi, Majid Nili; Yazdanpanah, Mohammad J; Seyfarth, Andre

2013-09-01

A new control approach to achieve robust hopping against perturbations in the sagittal plane is presented in this paper. In perturbed hopping, vertical body alignment has a significant role for stability. Our approach is based on the virtual pendulum concept, recently proposed, based on experimental findings in human and animal locomotion. In this concept, the ground reaction forces are pointed to a virtual support point, named virtual pivot point (VPP), during motion. This concept is employed in designing the controller to balance the trunk during the stance phase. New strategies for leg angle and length adjustment besides the virtual pendulum posture control are proposed as a unified controller. This method is investigated by applying it on an extension of the spring loaded inverted pendulum (SLIP) model. Trunk, leg mass and damping are added to the SLIP model in order to make the model more realistic. The stability is analyzed by Poincaré map analysis. With fixed VPP position, stability, disturbance rejection and moderate robustness are achieved, but with a low convergence speed. To improve the performance and attain higher robustness, an event-based control of the VPP position is introduced, using feedback of the system states at apexes. Discrete linear quartic regulator is used to design the feedback controller. Considerable enhancements with respect to stability, convergence speed and robustness against perturbations and parameter changes are achieved.

Small Body GN&C Research Report: A Robust Model Predictive Control Algorithm with Guaranteed Resolvability

NASA Technical Reports Server (NTRS)

Acikmese, Behcet A.; Carson, John M., III

2005-01-01

A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees the resolvability of the associated finite-horizon optimal control problem in a receding-horizon implementation. The control consists of two components; (i) feedforward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives, and derivatives in polytopes. An illustrative numerical example is also provided.

A robust model predictive control algorithm for uncertain nonlinear systems that guarantees resolvability

NASA Technical Reports Server (NTRS)

Acikmese, Ahmet Behcet; Carson, John M., III

2006-01-01

A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees resolvability. With resolvability, initial feasibility of the finite-horizon optimal control problem implies future feasibility in a receding-horizon framework. The control consists of two components; (i) feed-forward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives and derivatives in polytopes. An illustrative numerical example is also provided.

Multivariate Prediction Equations for HbA1c Lowering, Weight Change, and Hypoglycemic Events Associated with Insulin Rescue Medication in Type 2 Diabetes Mellitus: Informing Economic Modeling.

PubMed

Willis, Michael; Asseburg, Christian; Nilsson, Andreas; Johnsson, Kristina; Kartman, Bernt

2017-03-01

Type 2 diabetes mellitus (T2DM) is chronic and progressive and the cost-effectiveness of new treatment interventions must be established over long time horizons. Given the limited durability of drugs, assumptions regarding downstream rescue medication can drive results. Especially for insulin, for which treatment effects and adverse events are known to depend on patient characteristics, this can be problematic for health economic evaluation involving modeling. To estimate parsimonious multivariate equations of treatment effects and hypoglycemic event risks for use in parameterizing insulin rescue therapy in model-based cost-effectiveness analysis. Clinical evidence for insulin use in T2DM was identified in PubMed and from published reviews and meta-analyses. Study and patient characteristics and treatment effects and adverse event rates were extracted and the data used to estimate parsimonious treatment effect and hypoglycemic event risk equations using multivariate regression analysis. Data from 91 studies featuring 171 usable study arms were identified, mostly for premix and basal insulin types. Multivariate prediction equations for glycated hemoglobin A 1c lowering and weight change were estimated separately for insulin-naive and insulin-experienced patients. Goodness of fit (R 2 ) for both outcomes were generally good, ranging from 0.44 to 0.84. Multivariate prediction equations for symptomatic, nocturnal, and severe hypoglycemic events were also estimated, though considerable heterogeneity in definitions limits their usefulness. Parsimonious and robust multivariate prediction equations were estimated for glycated hemoglobin A 1c and weight change, separately for insulin-naive and insulin-experienced patients. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

PubMed

Ma, Yan; Mazumdar, Madhu

2011-10-30

Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

Robust decentralized power system controller design: Integrated approach

NASA Astrophysics Data System (ADS)

Veselý, Vojtech

2017-09-01

A unique approach to the design of gain scheduled controller (GSC) is presented. The proposed design procedure is based on the Bellman-Lyapunov equation, guaranteed cost and robust stability conditions using the parameter dependent quadratic stability approach. The obtained feasible design procedures for robust GSC design are in the form of BMI with guaranteed convex stability conditions. The obtained design results and their properties are illustrated in the simultaneously design of controllers for simple model (6-order) turbogenerator. The results of the obtained design procedure are a PI automatic voltage regulator (AVR) for synchronous generator, a PI governor controller and a power system stabilizer for excitation system.

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

NASA Astrophysics Data System (ADS)

Hu, Qing; Hu, Yuwei

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

Remote Multivariable Control Design Using a Competition Game

ERIC Educational Resources Information Center

Atanasijevic-Kunc, M.; Logar, V.; Karba, R.; Papic, M.; Kos, A.

2011-01-01

In this paper, some approaches to teaching multivariable control design are discussed, with special attention being devoted to a step-by-step transition to e-learning. The approach put into practice and presented here is developed through design projects, from which one is chosen as a competition game and is realized using the E-CHO system,…

Decentralized Feedback Controllers for Robust Stabilization of Periodic Orbits of Hybrid Systems: Application to Bipedal Walking.

PubMed

Hamed, Kaveh Akbari; Gregg, Robert D

2017-07-01

This paper presents a systematic algorithm to design time-invariant decentralized feedback controllers to exponentially and robustly stabilize periodic orbits for hybrid dynamical systems against possible uncertainties in discrete-time phases. The algorithm assumes a family of parameterized and decentralized nonlinear controllers to coordinate interconnected hybrid subsystems based on a common phasing variable. The exponential and [Formula: see text] robust stabilization problems of periodic orbits are translated into an iterative sequence of optimization problems involving bilinear and linear matrix inequalities. By investigating the properties of the Poincaré map, some sufficient conditions for the convergence of the iterative algorithm are presented. The power of the algorithm is finally demonstrated through designing a set of robust stabilizing local nonlinear controllers for walking of an underactuated 3D autonomous bipedal robot with 9 degrees of freedom, impact model uncertainties, and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg.

Decentralized Feedback Controllers for Robust Stabilization of Periodic Orbits of Hybrid Systems: Application to Bipedal Walking

PubMed Central

Hamed, Kaveh Akbari; Gregg, Robert D.

2016-01-01

This paper presents a systematic algorithm to design time-invariant decentralized feedback controllers to exponentially and robustly stabilize periodic orbits for hybrid dynamical systems against possible uncertainties in discrete-time phases. The algorithm assumes a family of parameterized and decentralized nonlinear controllers to coordinate interconnected hybrid subsystems based on a common phasing variable. The exponential and H2 robust stabilization problems of periodic orbits are translated into an iterative sequence of optimization problems involving bilinear and linear matrix inequalities. By investigating the properties of the Poincaré map, some sufficient conditions for the convergence of the iterative algorithm are presented. The power of the algorithm is finally demonstrated through designing a set of robust stabilizing local nonlinear controllers for walking of an underactuated 3D autonomous bipedal robot with 9 degrees of freedom, impact model uncertainties, and a decentralization scheme motivated by amputee locomotion with a transpelvic prosthetic leg. PMID:28959117

New multivariable capabilities of the INCA program

NASA Technical Reports Server (NTRS)

Bauer, Frank H.; Downing, John P.; Thorpe, Christopher J.

1989-01-01

The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.

A Robust Control of Two-Wheeled Mobile Manipulator with Underactuated Joint by Nonlinear Backstepping Method

NASA Astrophysics Data System (ADS)

Acar, Cihan; Murakami, Toshiyuki

In this paper, a robust control of two-wheeled mobile manipulator with underactuated joint is considered. Two-wheeled mobile manipulators are dynamically balanced two-wheeled driven systems that do not have any caster or extra wheels to stabilize their body. Two-wheeled mobile manipulators mainly have an important feature that makes them more flexible and agile than the statically stable mobile manipulators. However, two-wheeled mobile manipulator is an underactuated system due to its two-wheeled structure. Therefore, it is required to stabilize the underactuated passive body and, at the same time, control the position of the center of gravity (CoG) of the manipulator in this system. To realize this, nonlinear backstepping based control method with virtual double inverted pendulum model is proposed in this paper. Backstepping is used with sliding mode to increase the robustness of the system against modeling errors and other perturbations. Then robust acceleration control is also achieved by utilizing disturbance observer. Performance of the proposed method is evaluated by several experiments.

Optimal strategy analysis based on robust predictive control for inventory system with random demand

NASA Astrophysics Data System (ADS)

Saputra, Aditya; Widowati, Sutrisno

2017-12-01

In this paper, the optimal strategy for a single product single supplier inventory system with random demand is analyzed by using robust predictive control with additive random parameter. We formulate the dynamical system of this system as a linear state space with additive random parameter. To determine and analyze the optimal strategy for the given inventory system, we use robust predictive control approach which gives the optimal strategy i.e. the optimal product volume that should be purchased from the supplier for each time period so that the expected cost is minimal. A numerical simulation is performed with some generated random inventory data. We simulate in MATLAB software where the inventory level must be controlled as close as possible to a set point decided by us. From the results, robust predictive control model provides the optimal strategy i.e. the optimal product volume that should be purchased and the inventory level was followed the given set point.

Relationship of Soft Drink Consumption to Global Overweight, Obesity, and Diabetes: A Cross-National Analysis of 75 Countries

PubMed Central

McKee, Martin; Galea, Gauden; Stuckler, David

2013-01-01

Objectives. We estimated the relationship between soft drink consumption and obesity and diabetes worldwide. Methods. We used multivariate linear regression to estimate the association between soft drink consumption and overweight, obesity, and diabetes prevalence in 75 countries, controlling for other foods (cereals, meats, fruits and vegetables, oils, and total calories), income, urbanization, and aging. Data were obtained from the Euromonitor Global Market Information Database, the World Health Organization, and the International Diabetes Federation. Bottled water consumption, which increased with per-capita income in parallel to soft drink consumption, served as a natural control group. Results. Soft drink consumption increased globally from 9.5 gallons per person per year in 1997 to 11.4 gallons in 2010. A 1% rise in soft drink consumption was associated with an additional 4.8 overweight adults per 100 (adjusted B; 95% confidence interval [CI] = 3.1, 6.5), 2.3 obese adults per 100 (95% CI = 1.1, 3.5), and 0.3 adults with diabetes per 100 (95% CI = 0.1, 0.8). These findings remained robust in low- and middle-income countries. Conclusions. Soft drink consumption is significantly linked to overweight, obesity, and diabetes worldwide, including in low- and middle-income countries. PMID:23488503

Nuclear Magnetic Resonance metabolomics reveals an excretory metabolic signature of renal cell carcinoma.

PubMed

Monteiro, Márcia S; Barros, António S; Pinto, Joana; Carvalho, Márcia; Pires-Luís, Ana S; Henrique, Rui; Jerónimo, Carmen; Bastos, Maria de Lourdes; Gil, Ana M; Guedes de Pinho, Paula

2016-11-18

RCC usually develops and progresses asymptomatically and, when detected, it is frequently at advanced stages and metastatic, entailing a dismal prognosis. Therefore, there is an obvious demand for new strategies enabling an earlier diagnosis. The importance of metabolic rearrangements for carcinogenesis unlocked a new approach for cancer research, catalyzing the increased use of metabolomics. The present study aimed the NMR metabolic profiling of RCC in urine samples from a cohort of RCC patients (n = 42) and controls (n = 49). The methodology entailed variable selection of the spectra in tandem with multivariate analysis and validation procedures. The retrieval of a disease signature was preceded by a systematic evaluation of the impacts of subject age, gender, BMI, and smoking habits. The impact of confounders on the urine metabolomics profile of this population is residual compared to that of RCC. A 32-metabolite/resonance signature descriptive of RCC was unveiled, successfully distinguishing RCC patients from controls in principal component analysis. This work demonstrates the value of a systematic metabolomics workflow for the identification of robust urinary metabolic biomarkers of RCC. Future studies should entail the validation of the 32-metabolite/resonance signature found for RCC in independent cohorts, as well as biological validation of the putative hypotheses advanced.

Access to Primary Care in US Counties Is Associated with Lower Obesity Rates.

PubMed

Gaglioti, Anne H; Petterson, Stephen; Bazemore, Andrew; Phillips, Robert

2016-01-01

Obesity causes substantial morbidity and mortality in the United States. Evidence shows that primary care physician (PCP) supply correlates positively with improved health, but its association with obesity in the United States as not been adequately characterized. Our purpose was to characterize the association between PCP supply in US counties and adult obesity. We performed a multivariate logistic regression analysis to examine the relationship between county-level PCP supply and individual obesity status. We controlled for individual variables, including sex, race, marital status, income, and insurance status, and county-level variables, including rurality and poverty. Higher county-level PCP supply was associated with lower adult obesity after controlling for common confounders. Individuals living in counties with the most robust PCP supply were about 20% less likely to be obese (P ≤ .01) than those living in counties with the lowest PCP supply. While the observed association between the supply of PCPs and lower rates of obesity may not be causal, the association warrants further investigation. This may have important implications for restructuring the physician workforce in the context of the current PCP shortage and implementation of the Affordable Care Act and the patient-centered medical home. © Copyright 2016 by the American Board of Family Medicine.

Influence of the National Trauma Data Bank on the study of trauma outcomes: is it time to set research best practices to further enhance its impact?

PubMed

Haider, Adil H; Saleem, Taimur; Leow, Jeffrey J; Villegas, Cassandra V; Kisat, Mehreen; Schneider, Eric B; Haut, Elliott R; Stevens, Kent A; Cornwell, Edward E; MacKenzie, Ellen J; Efron, David T

2012-05-01

Risk-adjusted analyses are critical in evaluating trauma outcomes. The National Trauma Data Bank (NTDB) is a statistically robust registry that allows such analyses; however, analytical techniques are not yet standardized. In this study, we examined peer-reviewed manuscripts published using NTDB data, with particular attention to characteristics strongly associated with trauma outcomes. Our objective was to determine if there are substantial variations in the methodology and quality of risk-adjusted analyses and therefore, whether development of best practices for risk-adjusted analyses is warranted. A database of all studies using NTDB data published through December 2010 was created by searching PubMed and Embase. Studies with multivariate risk-adjusted analyses were examined for their central question, main outcomes measures, analytical techniques, covariates in adjusted analyses, and handling of missing data. Of 286 NTDB publications, 122 performed a multivariable adjusted analysis. These studies focused on clinical outcomes (51 studies), public health policy or injury prevention (30), quality (16), disparities (15), trauma center designation (6), or scoring systems (4). Mortality was the main outcome in 98 of these studies. There were considerable differences in the covariates used for case adjustment. The 3 covariates most frequently controlled for were age (95%), Injury Severity Score (85%), and sex (78%). Up to 43% of studies did not control for the 5 basic covariates necessary to conduct a risk-adjusted analysis of trauma mortality. Less than 10% of studies used clustering to adjust for facility differences or imputation to handle missing data. There is significant variability in how risk-adjusted analyses using data from the NTDB are performed. Best practices are needed to further improve the quality of research from the NTDB. Copyright © 2012 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2017-11-01

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

Robust design of feedback feed-forward iterative learning control based on 2D system theory for linear uncertain systems

NASA Astrophysics Data System (ADS)

Li, Zhifu; Hu, Yueming; Li, Di

2016-08-01

For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes.

Weight misperception and disordered weight control behaviors among U.S. high school students with overweight and obesity: Associations and trends, 1999-2013.

PubMed

Hazzard, Vivienne M; Hahn, Samantha L; Sonneville, Kendrin R

2017-08-01

To examine prevalence of weight misperception (incongruence between one's perceived weight status and one's actual weight status) and disordered weight control behaviors (DWCBs; unhealthy behaviors aiming to control or modify weight), associations between weight misperception and DWCBs, and temporal trends in prevalence and associations among adolescents with overweight and obesity from 1999 to 2013. Self-reported data from eight biennial cycles (1999-2013) of the cross-sectional national Youth Risk Behavior Survey were used in analyses restricted to respondents with overweight/obesity. Data on weight status perception, use of fasting, purging, and diet pills to control weight, sex, race/ethnicity, and grade in school were used in multivariate logistic regression models. Among U.S. high school students with overweight and obesity, no linear temporal trends were detected for prevalence of weight misperception, fasting, or purging between 1999 and 2013, while a significant linear decrease was observed for prevalence of diet pill use between 1999 and 2013 (b=-0.81, p<0.01). Using data pooled across 1999-2013, weight misperception predicted lower use of all DWCBs examined in this study among females and lower use of fasting to control weight among males. No significant changes over time in associations of weight misperception with fasting or purging were observed, though the association between weight misperception and diet pill use weakened somewhat across 1999-2013. In the context of increasing prevalence of overweight and obesity, weight misperception appears to be a robust protective factor for DWCBs. Copyright © 2017. Published by Elsevier Ltd.

Control algorithms for aerobraking in the Martian atmosphere

NASA Technical Reports Server (NTRS)

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

1991-01-01

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

Robust inertia-free attitude takeover control of postcapture combined spacecraft with guaranteed prescribed performance.

PubMed

Luo, Jianjun; Wei, Caisheng; Dai, Honghua; Yin, Zeyang; Wei, Xing; Yuan, Jianping

2018-03-01

In this paper, a robust inertia-free attitude takeover control scheme with guaranteed prescribed performance is investigated for postcapture combined spacecraft with consideration of unmeasurable states, unknown inertial property and external disturbance torque. Firstly, to estimate the unavailable angular velocity of combination accurately, a novel finite-time-convergent tracking differentiator is developed with a quite computationally achievable structure free from the unknown nonlinear dynamics of combined spacecraft. Then, a robust inertia-free prescribed performance control scheme is proposed, wherein, the transient and steady-state performance of combined spacecraft is first quantitatively studied by stabilizing the filtered attitude tracking errors. Compared with the existing works, the prominent advantage is that no parameter identifications and no neural or fuzzy nonlinear approximations are needed, which decreases the complexity of robust controller design dramatically. Moreover, the prescribed performance of combined spacecraft is guaranteed a priori without resorting to repeated regulations of the controller parameters. Finally, four illustrative examples are employed to validate the effectiveness of the proposed control scheme and tracking differentiator. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Sliding Mode Approaches for Robust Control, State Estimation, Secure Communication, and Fault Diagnosis in Nuclear Systems

NASA Astrophysics Data System (ADS)

Ablay, Gunyaz

Using traditional control methods for controller design, parameter estimation and fault diagnosis may lead to poor results with nuclear systems in practice because of approximations and uncertainties in the system models used, possibly resulting in unexpected plant unavailability. This experience has led to an interest in development of robust control, estimation and fault diagnosis methods. One particularly robust approach is the sliding mode control methodology. Sliding mode approaches have been of great interest and importance in industry and engineering in the recent decades due to their potential for producing economic, safe and reliable designs. In order to utilize these advantages, sliding mode approaches are implemented for robust control, state estimation, secure communication and fault diagnosis in nuclear plant systems. In addition, a sliding mode output observer is developed for fault diagnosis in dynamical systems. To validate the effectiveness of the methodologies, several nuclear plant system models are considered for applications, including point reactor kinetics, xenon concentration dynamics, an uncertain pressurizer model, a U-tube steam generator model and a coupled nonlinear nuclear reactor model.

An integrated guidance and control approach in three-dimensional space for hypersonic missile constrained by impact angles.

PubMed

Liu, Xiaodong; Huang, Wanwei; Du, Lifu

2017-01-01

A new robust three-dimensional integrated guidance and control (3D-IGC) approach is investigated for sliding-to-turn (STT) hypersonic missile, which encounters high uncertainties and strict impact angle constraints. First, a nonlinear state-space model with more generality is established facing to the design of 3D-IGC law. With regard to the as-built nonlinear system, a robust dynamic inversion control (RDIC) approach is proposed to overcome the robustness deficiency of traditional DIC, and then it is applied to construct the basic 3D-IGC law combining with backstepping method. In order to avoid the problems of "explosion of terms" and high-frequency chattering, an improved 3D-IGC law is further proposed by introducing dynamic surface control and continuous approximation approaches. From the computer simulation on a hypersonic missile, the proposed 3D-IGC law not only guarantees the stable flight, but also presents the precise control on terminal locations and impact angles. Moreover, it possesses smooth control output and strong robustness. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties.

PubMed

Wu, Sheng; Jin, Qibing; Zhang, Ridong; Zhang, Junfeng; Gao, Furong

2017-07-01

In this paper, an improved constrained tracking control design is proposed for batch processes under uncertainties. A new process model that facilitates process state and tracking error augmentation with further additional tuning is first proposed. Then a subsequent controller design is formulated using robust stable constrained MPC optimization. Unlike conventional robust model predictive control (MPC), the proposed method enables the controller design to bear more degrees of tuning so that improved tracking control can be acquired, which is very important since uncertainties exist inevitably in practice and cause model/plant mismatches. An injection molding process is introduced to illustrate the effectiveness of the proposed MPC approach in comparison with conventional robust MPC. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

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.

A Fundamental Mathematical Model of a Microbial Predenitrification System

NASA Technical Reports Server (NTRS)

Hoo, Karlene A.

2005-01-01

Space flight beyond Low Earth Orbit requires sophisticated systems to support all aspects of the mission (life support, real-time communications, etc.). A common concern that cuts across all these systems is the selection of information technology (IT) methodology, software and hardware architectures to provide robust monitoring, diagnosis, and control support. Another dimension of the problem space is that different systems must be integrated seamlessly so that communication speed and data handling appear as a continuum (un-interrupted). One such team investigating this problem is the Advanced Integration Matrix (AIM) team whose role is to define the critical requirements expected of software and hardware to support an integrated approach to the command and control of Advanced Life Support (ALS) for future long-duration human space missions, including permanent human presence on the Moon and Mars. A goal of the AIM team is to set the foundation for testing criteria that will assist in specifying tasks, control schemes and test scenarios to validate and verify systems capabilities. This project is to contribute to the goals of the AIM team by assisting with controls planning for ALS. Control for ALS is an enormous problem it involves air revitalization, water recovery, food production, solids processing and crew. In more general terms, these systems can be characterized as involving both continuous and discrete processes, dynamic interactions among the sub-systems, nonlinear behavior due to the complex operations, and a large number of multivariable interactions due to the dimension of the state space. It is imperative that a baseline approach from which to measure performance is established especially when the expectation for the control system is complete autonomous control.

Robust dynamics in minimal hybrid models of genetic networks

PubMed Central

Perkins, Theodore J.; Wilds, Roy; Glass, Leon

2010-01-01

Many gene-regulatory networks necessarily display robust dynamics that are insensitive to noise and stable under evolution. We propose that a class of hybrid systems can be used to relate the structure of these networks to their dynamics and provide insight into the origin of robustness. In these systems, the genes are represented by logical functions, and the controlling transcription factor protein molecules are real variables, which are produced and destroyed. As the transcription factor concentrations cross thresholds, they control the production of other transcription factors. We discuss mathematical analysis of these systems and show how the concepts of robustness and minimality can be used to generate putative logical organizations based on observed symbolic sequences. We apply the methods to control of the cell cycle in yeast. PMID:20921006

Robust dynamics in minimal hybrid models of genetic networks.

PubMed

Perkins, Theodore J; Wilds, Roy; Glass, Leon

2010-11-13

Many gene-regulatory networks necessarily display robust dynamics that are insensitive to noise and stable under evolution. We propose that a class of hybrid systems can be used to relate the structure of these networks to their dynamics and provide insight into the origin of robustness. In these systems, the genes are represented by logical functions, and the controlling transcription factor protein molecules are real variables, which are produced and destroyed. As the transcription factor concentrations cross thresholds, they control the production of other transcription factors. We discuss mathematical analysis of these systems and show how the concepts of robustness and minimality can be used to generate putative logical organizations based on observed symbolic sequences. We apply the methods to control of the cell cycle in yeast.

Robust SMES controller design for stabilization of inter-area oscillation considering coil size and system uncertainties

NASA Astrophysics Data System (ADS)

Ngamroo, Issarachai

2010-12-01

It is well known that the superconducting magnetic energy storage (SMES) is able to quickly exchange active and reactive power with the power system. The SMES is expected to be the smart storage device for power system stabilization. Although the stabilizing effect of SMES is significant, the SMES is quite costly. Particularly, the superconducting magnetic coil size which is the essence of the SMES, must be carefully selected. On the other hand, various generation and load changes, unpredictable network structure, etc., cause system uncertainties. The power controller of SMES which is designed without considering such uncertainties, may not tolerate and loses stabilizing effect. To overcome these problems, this paper proposes the new design of robust SMES controller taking coil size and system uncertainties into account. The structure of the active and reactive power controllers is the 1st-order lead-lag compensator. No need for the exact mathematical representation, system uncertainties are modeled by the inverse input multiplicative perturbation. Without the difficulty of the trade-off of damping performance and robustness, the optimization problem of control parameters is formulated. The particle swarm optimization is used for solving the optimal parameters at each coil size automatically. Based on the normalized integral square error index and the consideration of coil current constraint, the robust SMES with the smallest coil size which still provides the satisfactory stabilizing effect, can be achieved. Simulation studies in the two-area four-machine interconnected power system show the superior robustness of the proposed robust SMES with the smallest coil size under various operating conditions over the non-robust SMES with large coil size.

Decentralized control of large-scale systems: Fixed modes, sensitivity and parametric robustness. Ph.D. Thesis - Universite Paul Sabatier, 1985

NASA Technical Reports Server (NTRS)

Tarras, A.

1987-01-01

The problem of stabilization/pole placement under structural constraints of large scale linear systems is discussed. The existence of a solution to this problem is expressed in terms of fixed modes. The aim is to provide a bibliographic survey of the available results concerning the fixed modes (characterization, elimination, control structure selection to avoid them, control design in their absence) and to present the author's contribution to this problem which can be summarized by the use of the mode sensitivity concept to detect or to avoid them, the use of vibrational control to stabilize them, and the addition of parametric robustness considerations to design an optimal decentralized robust control.

Computational methods of robust controller design for aerodynamic flutter suppression

NASA Technical Reports Server (NTRS)

Anderson, L. R.

1981-01-01

The development of Riccati iteration, a tool for the design and analysis of linear control systems is examined. First, Riccati iteration is applied to the problem of pole placement and order reduction in two-time scale control systems. Order reduction, yielding a good approximation to the original system, is demonstrated using a 16th order linear model of a turbofan engine. Next, a numerical method for solving the Riccati equation is presented and demonstrated for a set of eighth order random examples. A literature review of robust controller design methods follows which includes a number of methods for reducing the trajectory and performance index sensitivity in linear regulators. Lastly, robust controller design for large parameter variations is discussed.

Robust blood-glucose control using Mathematica.

PubMed

Kovács, Levente; Paláncz, Béla; Benyó, Balázs; Török, László; Benyó, Zoltán

2006-01-01

A robust control design on frequency domain using Mathematica is presented for regularization of glucose level in type I diabetes persons under intensive care. The method originally proposed under Mathematica by Helton and Merino, --now with an improved disturbance rejection constraint inequality--is employed, using a three-state minimal patient model. The robustness of the resulted high-order linear controller is demonstrated by nonlinear closed loop simulation in state-space, in case of standard meal disturbances and is compared with H infinity design implemented with the mu-toolbox of Matlab. The controller designed with model parameters represented the most favorable plant dynamics from the point of view of control purposes, can operate properly even in case of parameter values of the worst-case scenario.

Robust Gain-Scheduled Fault Tolerant Control for a Transport Aircraft

NASA Technical Reports Server (NTRS)

Shin, Jong-Yeob; Gregory, Irene

2007-01-01

This paper presents an application of robust gain-scheduled control concepts using a linear parameter-varying (LPV) control synthesis method to design fault tolerant controllers for a civil transport aircraft. To apply the robust LPV control synthesis method, the nonlinear dynamics must be represented by an LPV model, which is developed using the function substitution method over the entire flight envelope. The developed LPV model associated with the aerodynamic coefficient uncertainties represents nonlinear dynamics including those outside the equilibrium manifold. Passive and active fault tolerant controllers (FTC) are designed for the longitudinal dynamics of the Boeing 747-100/200 aircraft in the presence of elevator failure. Both FTC laws are evaluated in the full nonlinear aircraft simulation in the presence of the elevator fault and the results are compared to show pros and cons of each control law.

Quantifying China's regional economic complexity

NASA Astrophysics Data System (ADS)

Gao, Jian; Zhou, Tao

2018-02-01

China has experienced an outstanding economic expansion during the past decades, however, literature on non-monetary metrics that reveal the status of China's regional economic development are still lacking. In this paper, we fill this gap by quantifying the economic complexity of China's provinces through analyzing 25 years' firm data. First, we estimate the regional economic complexity index (ECI), and show that the overall time evolution of provinces' ECI is relatively stable and slow. Then, after linking ECI to the economic development and the income inequality, we find that the explanatory power of ECI is positive for the former but negative for the latter. Next, we compare different measures of economic diversity and explore their relationships with monetary macroeconomic indicators. Results show that the ECI index and the non-linear iteration based Fitness index are comparative, and they both have stronger explanatory power than other benchmark measures. Further multivariate regressions suggest the robustness of our results after controlling other socioeconomic factors. Our work moves forward a step towards better understanding China's regional economic development and non-monetary macroeconomic indicators.

Analyzing whether countries are equally efficient at improving longevity for men and women.

PubMed

Barthold, Douglas; Nandi, Arijit; Mendoza Rodríguez, José M; Heymann, Jody

2014-11-01

We examined the efficiency of country-specific health care spending in improving life expectancies for men and women. We estimated efficiencies of health care spending for 27 Organisation for Economic Co-operation and Development (OECD) countries during the period 1991 to 2007 using multivariable regression models, including country fixed-effects and controlling for time-varying levels of national social expenditures, economic development, and health behaviors. Findings indicated robust differences in health-spending efficiency. A 1% annual increase in health expenditures was associated with percent changes in life expectancy ranging from 0.020 in the United States (95% confidence interval [CI] = 0.008, 0.032) to 0.121 in Germany (95% CI = 0.099, 0.143). Health-spending increases were associated with greater life expectancy improvements for men than for women in nearly every OECD country. This is the first study to our knowledge to estimate the effect of country-specific health expenditures on life expectancies of men and women. Future work understanding the determinants of these differences has the potential to improve the overall efficiency and equity of national health systems.

The effect of economic sanctions on the mortality of Iraqi children prior to the 1991 Persian Gulf War.

PubMed Central

Daponte, B O; Garfield, R

2000-01-01

OBJECTIVES: This study examined the effect of sanctions on mortality among Iraqi children. METHODS: The effects of economic sanctions on health are not well known. Past studies on the effect of economic sanctions on mortality have suffered from unreliable data sources and the collinearity of sanctions with other negative economic events. We overcame these weaknesses by using individual child records from a retrospective survey of mothers conducted after the 1991 Persian Gulf War to examine the effect of sanctions on mortality among Iraqi children. Multivariate proportional hazards analysis was used to assess the effect of economic sanctions prior to war (from August through December 1990). RESULTS: We found that after controlling for child and maternal characteristics, when economic sanctions were entered into the proportional hazards equation, the risk of dying increased dramatically. This increase was highly significant statistically. CONCLUSIONS: Innovative application of robust epidemiologic research tools can contribute to assessments of health and well-being even under the methodological and practical constraints of comprehensive economic sanctions, but more research is needed. PMID:10754968

Effect of preoperative immunonutrition on postoperative short-term outcomes of patients with head and neck squamous cell carcinoma.

PubMed

Aeberhard, Carla; Mayer, Catherine; Meyer, Simone; Mueller, Simon Andreas; Schuetz, Philipp; Stanga, Zeno; Giger, Roland

2018-05-01

Patients with head and neck squamous cell carcinoma (HNSCC) often acquire an impaired nutritional status resulting in compromised outcomes. Perioperative immunonutrition may have a positive effect on outcomes after elective surgery. Short-term outcomes before and after implementation of preoperative immunonutrition were retrospectively assessed. Regression models adjusted for outcome predictors were used to compare the length of stay (LOS) in the hospital, local infections, and general complications. Four hundred eleven patients were included (control group = 209 and the intervention group = 202). With immunonutrition, hospital LOS was significantly lower (median 6 vs 8 days; adjusted mean difference of -5.65 days; P < .001) and local infections were significantly reduced (7.4% vs 15.3%; adjusted odds ratio [OR] 0.30; P = .006). Subgroup analysis showed more pronounced effects in patients with previous radiotherapy and extensive surgery. Patients receiving preoperative immunonutrition had a shorter hospital LOS and a lower rate for wound infections and local complications. These effects remained robust after a multivariate adjustment. © 2018 Wiley Periodicals, Inc.

Parent psychopathology and offspring mental disorders: results from the WHO World Mental Health Surveys

PubMed Central

McLaughlin, Katie A.; Gadermann, Anne M.; Hwang, Irving; Sampson, Nancy A.; Al-Hamzawi, Ali; Andrade, Laura Helena; Angermeyer, Matthias C.; Benjet, Corina; Bromet, Evelyn J.; Bruffaerts, Ronny; Caldas-de-Almeida, José Miguel; de Girolamo, Giovanni; de Graaf, Ron; Florescu, Silvia; Gureje, Oye; Haro, Josep Maria; Hinkov, Hristo Ruskov; Horiguchi, Itsuko; Hu, Chiyi; Karam, Aimee Nasser; Kovess-Masfety, Viviane; Lee, Sing; Murphy, Samuel D.; Nizamie, S. Haque; Posada-Villa, José; Williams, David R.; Kessler, Ronald C.

2012-01-01

Background Associations between specific parent and offspring mental disorders are likely to have been overestimated in studies that have failed to control for parent comorbidity. Aims To examine the associations of parent with respondent disorders. Method Data come from the World Health Organization (WHO) World Mental Health Surveys (n = 51 507). Respondent disorders were assessed with the Composite International Diagnostic Interview and parent disorders with informant-based Family History Research Diagnostic Criteria interviews. Results Although virtually all parent disorders examined (major depressive, generalised anxiety, panic, substance and antisocial behaviour disorders and suicidality) were significantly associated with offspring disorders in multivariate analyses, little specificity was found. Comorbid parent disorders had significant sub-additive associations with offspring disorders. Population-attributable risk proportions for parent disorders were 12.4% across all offspring disorders, generally higher in high- and upper-middle- than low-/lower-middle-income countries, and consistently higher for behaviour (11.0-19.9%) than other (7.1-14.0%) disorders. Conclusions Parent psychopathology is a robust non-specific predictor associated with a substantial proportion of offspring disorders. PMID:22403085

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

NASA Astrophysics Data System (ADS)

Ison, Mark; Artemiadis, Panagiotis

2014-10-01

Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.

Analysis and Design of Launch Vehicle Flight Control Systems

NASA Technical Reports Server (NTRS)

Wie, Bong; Du, Wei; Whorton, Mark

2008-01-01

This paper describes the fundamental principles of launch vehicle flight control analysis and design. In particular, the classical concept of "drift-minimum" and "load-minimum" control principles is re-examined and its performance and stability robustness with respect to modeling uncertainties and a gimbal angle constraint is discussed. It is shown that an additional feedback of angle-of-attack or lateral acceleration can significantly improve the overall performance and robustness, especially in the presence of unexpected large wind disturbance. Non-minimum-phase structural filtering of "unstably interacting" bending modes of large flexible launch vehicles is also shown to be effective and robust.

Robust Cytokine and Chemokine Response in Nasopharyngeal Secretions: Association With Decreased Severity in Children With Physician Diagnosed Bronchiolitis.

PubMed

Nicholson, Erin G; Schlegel, Chelsea; Garofalo, Roberto P; Mehta, Reena; Scheffler, Margaret; Mei, Minghua; Piedra, Pedro A

2016-08-15

Bronchiolitis causes substantial disease in young children. Previous findings had indicated that a robust innate immune response was not associated with a poor clinical outcome in bronchiolitis. This study tested the hypothesis that increased concentrations of cytokines and chemokines in nasal wash specimens were associated with decreased severity in bronchiolitis. Children <24 months old who presented to the emergency department with signs and symptoms of bronchiolitis were eligible for enrollment. Nasal wash specimens were analyzed for viral pathogens and cytokine/chemokine concentrations. These results were evaluated with regard to disposition. One hundred eleven children with bronchiolitis were enrolled. A viral pathogen was identified in 91.9% of patients (respiratory syncytial virus in 51.4%, human rhinovirus in 11.7%). Higher levels of cytokines and chemokines (interferon [IFN] γ; interleukin [IL] 4, 15, and 17; CXCL10; and eotaxin) were significantly associated with a decreased risk of hospitalization. IL-17, IL-4, IFN-γ, and IFN-γ-inducible protein 10 (CXCL10 or IP-10) remained statistically significant in the multivariate analyses. The cytokines and chemokines significantly associated with decreased bronchiolitis severity are classified in a wide range of functional groups (T-helper 1 and 2, regulatory, and chemoattractant). The involvement of these functional groups suggest that a broadly overlapping cytokine/chemokine response is required for control of virus-mediated respiratory disease in young children. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Development of An Intelligent Flight Propulsion Control System

NASA Technical Reports Server (NTRS)

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

1999-01-01

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

Decentralized adaptive control of robot manipulators with robust stabilization design

NASA Technical Reports Server (NTRS)

Yuan, Bau-San; Book, Wayne J.

1988-01-01

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

Robust levitation control for maglev systems with guaranteed bounded airgap.

PubMed

Xu, Jinquan; Chen, Ye-Hwa; Guo, Hong

2015-11-01

The robust control design problem for the levitation control of a nonlinear uncertain maglev system is considered. The uncertainty is (possibly) fast time-varying. The system has magnitude limitation on the airgap between the suspended chassis and the guideway in order to prevent undesirable contact. Furthermore, the (global) matching condition is not satisfied. After a three-step state transformation, a robust control scheme for the maglev vehicle is proposed, which is able to guarantee the uniform boundedness and uniform ultimate boundedness of the system, regardless of the uncertainty. The magnitude limitation of the airgap is guaranteed, regardless of the uncertainty. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Robust, Decoupled, Flight Control Design with Rate Saturating Actuators

NASA Technical Reports Server (NTRS)

Snell, S. A.; Hess, R. A.

1997-01-01

Techniques for the design of control systems for manually controlled, high-performance aircraft must provide the following: (1) multi-input, multi-output (MIMO) solutions, (2) acceptable handling qualities including no tendencies for pilot-induced oscillations, (3) a tractable approach for compensator design, (4) performance and stability robustness in the presence of significant plant uncertainty, and (5) performance and stability robustness in the presence actuator saturation (particularly rate saturation). A design technique built upon Quantitative Feedback Theory is offered as a candidate methodology which can provide flight control systems meeting these requirements, and do so over a considerable part of the flight envelope. An example utilizing a simplified model of a supermaneuverable fighter aircraft demonstrates the proposed design methodology.

Robustness of reduced-order observer-based controllers in transitional 2D Blasius boundary layers

NASA Astrophysics Data System (ADS)

Belson, Brandt; Semeraro, Onofrio; Rowley, Clarence; Pralits, Jan; Henningson, Dan

2011-11-01

In this work, we seek to delay transition in the Blasius boundary layer. We trip the flow with an upstream disturbance and dampen the growth of the resulting structures downstream. The observer-based controllers use a single sensor and a single localized body force near the wall. To formulate the controllers, we first find a reduced-order model of the system via the Eigensystem Realization Algorithm (ERA), then find the H2 optimal controller for this reduced-order system. We find the resulting controllers are effective only when the sensor is upstream of the actuator (in a feedforward configuration), but as is expected, are sensitive to model uncertainty. When the sensor is downstream of the actuator (in a feedback configuration), the reduced-order observer-based controllers are not robust and ineffective on the full system. In order to investigate the robustness properties of the system, an iterative technique called the adjoint of the direct adjoint (ADA) is employed to find a full-dimensional H2 optimal controller. This avoids the reduced-order modelling step and serves as a reference point. ADA is promising for investigating the lack of robustness previously mentioned.

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

PubMed

Benamor, Anouar; Messaoud, Hassani

2018-05-02

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

Influence assessment in censored mixed-effects models using the multivariate Student’s-t distribution

PubMed Central

Matos, Larissa A.; Bandyopadhyay, Dipankar; Castro, Luis M.; Lachos, Victor H.

2015-01-01

In biomedical studies on HIV RNA dynamics, viral loads generate repeated measures that are often subjected to upper and lower detection limits, and hence these responses are either left- or right-censored. Linear and non-linear mixed-effects censored (LMEC/NLMEC) models are routinely used to analyse these longitudinal data, with normality assumptions for the random effects and residual errors. However, the derived inference may not be robust when these underlying normality assumptions are questionable, especially the presence of outliers and thick-tails. Motivated by this, Matos et al. (2013b) recently proposed an exact EM-type algorithm for LMEC/NLMEC models using a multivariate Student’s-t distribution, with closed-form expressions at the E-step. In this paper, we develop influence diagnostics for LMEC/NLMEC models using the multivariate Student’s-t density, based on the conditional expectation of the complete data log-likelihood. This partially eliminates the complexity associated with the approach of Cook (1977, 1986) for censored mixed-effects models. The new methodology is illustrated via an application to a longitudinal HIV dataset. In addition, a simulation study explores the accuracy of the proposed measures in detecting possible influential observations for heavy-tailed censored data under different perturbation and censoring schemes. PMID:26190871

Model based control of dynamic atomic force microscope.

PubMed

Lee, Chibum; Salapaka, Srinivasa M

2015-04-01

A model-based robust control approach is proposed that significantly improves imaging bandwidth for the dynamic mode atomic force microscopy. A model for cantilever oscillation amplitude and phase dynamics is derived and used for the control design. In particular, the control design is based on a linearized model and robust H(∞) control theory. This design yields a significant improvement when compared to the conventional proportional-integral designs and verified by experiments.

A large-scale and robust dynamic MRM study of colorectal cancer biomarkers.

PubMed

You, Jia; Kao, Athit; Dillon, Roslyn; Croner, Lisa J; Benz, Ryan; Blume, John E; Wilcox, Bruce

2018-06-25

Over the past 20 years, mass spectrometry (MS) has emerged as a dynamic tool for proteomics biomarker discovery. However, published MS biomarker candidates often do not translate to the clinic, failing during attempts at independent replication. The cause can be shortcomings in study design, sample quality, assay quantitation, and/or quality/process control. To address these shortcomings, we developed an MS workflow in accordance with Tier 2 measurement requirements for targeted peptides, defined by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) "fit-for-purpose" approach, using dynamic multiple reaction monitoring (dMRM) which measures specific peptide transitions during predefined retention time (RT) windows. We describe the development of a robust multipex dMRM assay measuring 641 proteotypic peptides from 392 colorectal cancer (CRC) related proteins, and the procedures to track and handle sample processing and instrument variation over a four-month study, during which the assay measured blood samples from 1045 patients with CRC symptoms. After data collection, transitions were filtered by signal quality metrics before entering receiver operating characteristic (ROC) analysis. The results demonstrated CRC signal carried by 127 proteins in the symptomatic population. The workflow might be further developed to build Tier 1 assays for clinical tests identifying symptomatic individuals at elevated risk of CRC. We developed a dMRM MS method with the rigor of a Tier 2 assay as defined by the CPTAC 'fit for purpose approach' [1]. Using quality and process control procedures, the assay was used to quantify 641 proteotypic peptides representing 392 CRC-related proteins in plasma from 1045 CRC-symptomatic patients. To our knowledge, this is the largest MRM method applied to the largest study to date. The results showed that 127 of the proteins carried univariate CRC signal in the symptomatic population. This large number of single biomarkers bodes well for future development of multivariate classifiers to distinguish CRC in the symptomatic population. Copyright © 2018. Published by Elsevier B.V.

Passive Double-Sensory Evoked Coherence Correlates with Long-Term Memory Capacity.

PubMed

Horwitz, Anna; Mortensen, Erik L; Osler, Merete; Fagerlund, Birgitte; Lauritzen, Martin; Benedek, Krisztina

2017-01-01

HIGHLIGHTS Memory correlates with the difference between single and double-sensory evoked steady-state coherence in the gamma range (Δ C ).The correlation is most pronounced for the anterior brain region (Δ C A ).The correlation is not driven by birth size, education, speed of processing, or intelligence.The sensitivity of Δ C A for detecting low memory capacity is 90%. Cerebral rhythmic activity and oscillations are important pathways of communication between cortical cell assemblies and may be key factors in memory. We asked whether memory performance is related to gamma coherence in a non-task sensory steady-state stimulation. We investigated 40 healthy males born in 1953 who were part of a Danish birth cohort study. Coherence was measured in the gamma range in response to a single-sensory visual stimulation (36 Hz) and a double-sensory combined audiovisual stimulation (auditive: 40 Hz; visual: 36 Hz). The individual difference in coherence (Δ C ) between the bimodal and monomodal stimulation was calculated for each subject and used as the main explanatory variable. Δ C in total brain were significantly negatively correlated with long-term verbal recall. This correlation was pronounced for the anterior region. In addition, the correlation between Δ C and long-term memory was robust when controlling for working memory, as well as a wide range of potentially confounding factors, including intelligence, length of education, speed of processing, visual attention and executive function. Moreover, we found that the difference in anterior coherence (Δ C A ) is a better predictor of memory than power in multivariate models. The sensitivity of Δ C A for detecting low memory capacity is 92%. Finally, Δ C A was also associated with other types of memory: verbal learning, visual recognition, and spatial memory, and these additional correlations were also robust enough to control for a range of potentially confounding factors. Thus, the Δ C is a predictor of memory performance may be useful in cognitive neuropsychological testing.

Process design and control of a twin screw hot melt extrusion for continuous pharmaceutical tamper-resistant tablet production.

PubMed

Baronsky-Probst, J; Möltgen, C-V; Kessler, W; Kessler, R W

2016-05-25

Hot melt extrusion (HME) is a well-known process within the plastic and food industries that has been utilized for the past several decades and is increasingly accepted by the pharmaceutical industry for continuous manufacturing. For tamper-resistant formulations of e.g. opioids, HME is the most efficient production technique. The focus of this study is thus to evaluate the manufacturability of the HME process for tamper-resistant formulations. Parameters such as the specific mechanical energy (SME), as well as the melt pressure and its standard deviation, are important and will be discussed in this study. In the first step, the existing process data are analyzed by means of multivariate data analysis. Key critical process parameters such as feed rate, screw speed, and the concentration of the API in the polymers are identified, and critical quality parameters of the tablet are defined. In the second step, a relationship between the critical material, product and process quality attributes are established by means of Design of Experiments (DoEs). The resulting SME and the temperature at the die are essential data points needed to indirectly qualify the degradation of the API, which should be minimal. NIR-spectroscopy is used to monitor the material during the extrusion process. In contrast to most applications in which the probe is directly integrated into the die, the optical sensor is integrated into the cooling line of the strands. This saves costs in the probe design and maintenance and increases the robustness of the chemometric models. Finally, a process measurement system is installed to monitor and control all of the critical attributes in real-time by means of first principles, DoE models, soft sensor models, and spectroscopic information. Overall, the process is very robust as long as the screw speed is kept low. Copyright © 2015 Elsevier B.V. All rights reserved.

Passive Double-Sensory Evoked Coherence Correlates with Long-Term Memory Capacity

PubMed Central

Horwitz, Anna; Mortensen, Erik L.; Osler, Merete; Fagerlund, Birgitte; Lauritzen, Martin; Benedek, Krisztina

2017-01-01

HIGHLIGHTS Memory correlates with the difference between single and double-sensory evoked steady-state coherence in the gamma range (ΔC).The correlation is most pronounced for the anterior brain region (ΔCA).The correlation is not driven by birth size, education, speed of processing, or intelligence.The sensitivity of ΔCA for detecting low memory capacity is 90%. Cerebral rhythmic activity and oscillations are important pathways of communication between cortical cell assemblies and may be key factors in memory. We asked whether memory performance is related to gamma coherence in a non-task sensory steady-state stimulation. We investigated 40 healthy males born in 1953 who were part of a Danish birth cohort study. Coherence was measured in the gamma range in response to a single-sensory visual stimulation (36 Hz) and a double-sensory combined audiovisual stimulation (auditive: 40 Hz; visual: 36 Hz). The individual difference in coherence (ΔC) between the bimodal and monomodal stimulation was calculated for each subject and used as the main explanatory variable. ΔC in total brain were significantly negatively correlated with long-term verbal recall. This correlation was pronounced for the anterior region. In addition, the correlation between ΔC and long-term memory was robust when controlling for working memory, as well as a wide range of potentially confounding factors, including intelligence, length of education, speed of processing, visual attention and executive function. Moreover, we found that the difference in anterior coherence (ΔCA) is a better predictor of memory than power in multivariate models. The sensitivity of ΔCA for detecting low memory capacity is 92%. Finally, ΔCA was also associated with other types of memory: verbal learning, visual recognition, and spatial memory, and these additional correlations were also robust enough to control for a range of potentially confounding factors. Thus, the ΔC is a predictor of memory performance may be useful in cognitive neuropsychological testing. PMID:29311868

Robust fuel- and time-optimal control of uncertain flexible space structures

NASA Technical Reports Server (NTRS)

Wie, Bong; Sinha, Ravi; Sunkel, John; Cox, Ken

1993-01-01

The problem of computing open-loop, fuel- and time-optimal control inputs for flexible space structures in the face of modeling uncertainty is investigated. Robustified, fuel- and time-optimal pulse sequences are obtained by solving a constrained optimization problem subject to robustness constraints. It is shown that 'bang-off-bang' pulse sequences with a finite number of switchings provide a practical tradeoff among the maneuvering time, fuel consumption, and performance robustness of uncertain flexible space structures.

Input-output oriented computation algorithms for the control of large flexible structures

NASA Technical Reports Server (NTRS)

Minto, K. D.

1989-01-01

An overview is given of work in progress aimed at developing computational algorithms addressing two important aspects in the control of large flexible space structures; namely, the selection and placement of sensors and actuators, and the resulting multivariable control law design problem. The issue of sensor/actuator set selection is particularly crucial to obtaining a satisfactory control design, as clearly a poor choice will inherently limit the degree to which good control can be achieved. With regard to control law design, the researchers are driven by concerns stemming from the practical issues associated with eventual implementation of multivariable control laws, such as reliability, limit protection, multimode operation, sampling rate selection, processor throughput, etc. Naturally, the burden imposed by dealing with these aspects of the problem can be reduced by ensuring that the complexity of the compensator is minimized. Our approach to these problems is based on extensions to input/output oriented techniques that have proven useful in the design of multivariable control systems for aircraft engines. In particular, researchers are exploring the use of relative gain analysis and the condition number as a means of quantifying the process of sensor/actuator selection and placement for shape control of a large space platform.

Advanced multivariable control of a turboexpander plant

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

Altena, D.; Howard, M.; Bullin, K.

1998-12-31

This paper describes an application of advanced multivariable control on a natural gas plant and compares its performance to the previous conventional feed-back control. This control algorithm utilizes simple models from existing plant data and/or plant tests to hold the process at the desired operating point in the presence of disturbances and changes in operating conditions. The control software is able to accomplish this due to effective handling of process variable interaction, constraint avoidance and feed-forward of measured disturbances. The economic benefit of improved control lies in operating closer to the process constraints while avoiding significant violations. The South Texasmore » facility where this controller was implemented experienced reduced variability in process conditions which increased liquids recovery because the plant was able to operate much closer to the customer specified impurity constraint. An additional benefit of this implementation of multivariable control is the ability to set performance criteria beyond simple setpoints, including process variable constraints, relative variable merit and optimizing use of manipulated variables. The paper also details the control scheme applied to the complex turboexpander process and some of the safety features included to improve reliability.« less

Robust synergetic control design under inputs and states constraints

NASA Astrophysics Data System (ADS)

Rastegar, Saeid; Araújo, Rui; Sadati, Jalil

2018-03-01

In this paper, a novel robust-constrained control methodology for discrete-time linear parameter-varying (DT-LPV) systems is proposed based on a synergetic control theory (SCT) approach. It is shown that in DT-LPV systems without uncertainty, and for any unmeasured bounded additive disturbance, the proposed controller accomplishes the goal of stabilising the system by asymptotically driving the error of the controlled variable to a bounded set containing the origin and then maintaining it there. Moreover, given an uncertain DT-LPV system jointly subject to unmeasured and constrained additive disturbances, and constraints in states, input commands and reference signals (set points), then invariant set theory is used to find an appropriate polyhedral robust invariant region in which the proposed control framework is guaranteed to robustly stabilise the closed-loop system. Furthermore, this is achieved even for the case of varying non-zero control set points in such uncertain DT-LPV systems. The controller is characterised to have a simple structure leading to an easy implementation, and a non-complex design process. The effectiveness of the proposed method and the implications of the controller design on feasibility and closed-loop performance are demonstrated through application examples on the temperature control on a continuous-stirred tank reactor plant, on the control of a real-coupled DC motor plant, and on an open-loop unstable system example.

Robust lateral blended-wing-body aircraft feedback control design using a parameterized LFR model and DGK-iteration

NASA Astrophysics Data System (ADS)

Schirrer, A.; Westermayer, C.; Hemedi, M.; Kozek, M.

2013-12-01

This paper shows control design results, performance, and limitations of robust lateral control law designs based on the DGK-iteration mixed-μ-synthesis procedure for a large, flexible blended wing body (BWB) passenger aircraft. The aircraft dynamics is preshaped by a low-complexity inner loop control law providing stabilization, basic response shaping, and flexible mode damping. The μ controllers are designed to further improve vibration damping of the main flexible modes by exploiting the structure of the arising significant parameter-dependent plant variations. This is achieved by utilizing parameterized Linear Fractional Representations (LFR) of the aircraft rigid and flexible dynamics. Designs with various levels of LFR complexity are carried out and discussed, showing the achieved performance improvement over the initial controller and their robustness and complexity properties.

Novel health monitoring method using an RGB camera.

PubMed

Hassan, M A; Malik, A S; Fofi, D; Saad, N; Meriaudeau, F

2017-11-01

In this paper we present a novel health monitoring method by estimating the heart rate and respiratory rate using an RGB camera. The heart rate and the respiratory rate are estimated from the photoplethysmography (PPG) and the respiratory motion. The method mainly operates by using the green spectrum of the RGB camera to generate a multivariate PPG signal to perform multivariate de-noising on the video signal to extract the resultant PPG signal. A periodicity based voting scheme (PVS) was used to measure the heart rate and respiratory rate from the estimated PPG signal. We evaluated our proposed method with a state of the art heart rate measuring method for two scenarios using the MAHNOB-HCI database and a self collected naturalistic environment database. The methods were furthermore evaluated for various scenarios at naturalistic environments such as a motion variance session and a skin tone variance session. Our proposed method operated robustly during the experiments and outperformed the state of the art heart rate measuring methods by compensating the effects of the naturalistic environment.

imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel

PubMed Central

Grapov, Dmitry; Newman, John W.

2012-01-01

Summary: Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Availability and implementation: Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010). Contact: John.Newman@ars.usda.gov Supplementary Information: Installation instructions, tutorials and users manual are available at http://sourceforge.net/projects/imdev/. PMID:22815358

Testing for Granger Causality in the Frequency Domain: A Phase Resampling Method.

PubMed

Liu, Siwei; Molenaar, Peter

2016-01-01

This article introduces phase resampling, an existing but rarely used surrogate data method for making statistical inferences of Granger causality in frequency domain time series analysis. Granger causality testing is essential for establishing causal relations among variables in multivariate dynamic processes. However, testing for Granger causality in the frequency domain is challenging due to the nonlinear relation between frequency domain measures (e.g., partial directed coherence, generalized partial directed coherence) and time domain data. Through a simulation study, we demonstrate that phase resampling is a general and robust method for making statistical inferences even with short time series. With Gaussian data, phase resampling yields satisfactory type I and type II error rates in all but one condition we examine: when a small effect size is combined with an insufficient number of data points. Violations of normality lead to slightly higher error rates but are mostly within acceptable ranges. We illustrate the utility of phase resampling with two empirical examples involving multivariate electroencephalography (EEG) and skin conductance data.

Boosting Higgs pair production in the [Formula: see text] final state with multivariate techniques.

PubMed

Behr, J Katharina; Bortoletto, Daniela; Frost, James A; Hartland, Nathan P; Issever, Cigdem; Rojo, Juan

2016-01-01

The measurement of Higgs pair production will be a cornerstone of the LHC program in the coming years. Double Higgs production provides a crucial window upon the mechanism of electroweak symmetry breaking and has a unique sensitivity to the Higgs trilinear coupling. We study the feasibility of a measurement of Higgs pair production in the [Formula: see text] final state at the LHC. Our analysis is based on a combination of traditional cut-based methods with state-of-the-art multivariate techniques. We account for all relevant backgrounds, including the contributions from light and charm jet mis-identification, which are ultimately comparable in size to the irreducible 4 b QCD background. We demonstrate the robustness of our analysis strategy in a high pileup environment. For an integrated luminosity of [Formula: see text] ab[Formula: see text], a signal significance of [Formula: see text] is obtained, indicating that the [Formula: see text] final state alone could allow for the observation of double Higgs production at the High Luminosity LHC.

Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains.

PubMed

Allefeld, Carsten; Bialonski, Stephan

2007-12-01

Synchronization cluster analysis is an approach to the detection of underlying structures in data sets of multivariate time series, starting from a matrix R of bivariate synchronization indices. A previous method utilized the eigenvectors of R for cluster identification, analogous to several recent attempts at group identification using eigenvectors of the correlation matrix. All of these approaches assumed a one-to-one correspondence of dominant eigenvectors and clusters, which has however been shown to be wrong in important cases. We clarify the usefulness of eigenvalue decomposition for synchronization cluster analysis by translating the problem into the language of stochastic processes, and derive an enhanced clustering method harnessing recent insights from the coarse-graining of finite-state Markov processes. We illustrate the operation of our method using a simulated system of coupled Lorenz oscillators, and we demonstrate its superior performance over the previous approach. Finally we investigate the question of robustness of the algorithm against small sample size, which is important with regard to field applications.

Study of archaeological coins of different dynasties using libs coupled with multivariate analysis

NASA Astrophysics Data System (ADS)

Awasthi, Shikha; Kumar, Rohit; Rai, G. K.; Rai, A. K.

2016-04-01

Laser Induced Breakdown Spectroscopy (LIBS) is an atomic emission spectroscopic technique having unique capability of an in-situ monitoring tool for detection and quantification of elements present in different artifacts. Archaeological coins collected form G.R. Sharma Memorial Museum; University of Allahabad, India has been analyzed using LIBS technique. These coins were obtained from excavation of Kausambi, Uttar Pradesh, India. LIBS system assembled in the laboratory (laser Nd:YAG 532 nm, 4 ns pulse width FWHM with Ocean Optics LIBS 2000+ spectrometer) is employed for spectral acquisition. The spectral lines of Ag, Cu, Ca, Sn, Si, Fe and Mg are identified in the LIBS spectra of different coins. LIBS along with Multivariate Analysis play an effective role for classification and contribution of spectral lines in different coins. The discrimination between five coins with Archaeological interest has been carried out using Principal Component Analysis (PCA). The results show the potential relevancy of the methodology used in the elemental identification and classification of artifacts with high accuracy and robustness.

Forensic Discrimination of Latent Fingerprints Using Laser-Induced Breakdown Spectroscopy (LIBS) and Chemometric Approaches.

PubMed

Yang, Jun-Ho; Yoh, Jack J

2018-01-01

A novel technique is reported for separating overlapping latent fingerprints using chemometric approaches that combine laser-induced breakdown spectroscopy (LIBS) and multivariate analysis. The LIBS technique provides the capability of real time analysis and high frequency scanning as well as the data regarding the chemical composition of overlapping latent fingerprints. These spectra offer valuable information for the classification and reconstruction of overlapping latent fingerprints by implementing appropriate statistical multivariate analysis. The current study employs principal component analysis and partial least square methods for the classification of latent fingerprints from the LIBS spectra. This technique was successfully demonstrated through a classification study of four distinct latent fingerprints using classification methods such as soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). The novel method yielded an accuracy of more than 85% and was proven to be sufficiently robust. Furthermore, through laser scanning analysis at a spatial interval of 125 µm, the overlapping fingerprints were reconstructed as separate two-dimensional forms.

Decoding the content of visual short-term memory under distraction in occipital and parietal areas.

PubMed

Bettencourt, Katherine C; Xu, Yaoda

2016-01-01

Recent studies have provided conflicting accounts regarding where in the human brain visual short-term memory (VSTM) content is stored, with strong univariate fMRI responses being reported in superior intraparietal sulcus (IPS), but robust multivariate decoding being reported in occipital cortex. Given the continuous influx of information in everyday vision, VSTM storage under distraction is often required. We found that neither distractor presence nor predictability during the memory delay affected behavioral performance. Similarly, superior IPS exhibited consistent decoding of VSTM content across all distractor manipulations and had multivariate responses that closely tracked behavioral VSTM performance. However, occipital decoding of VSTM content was substantially modulated by distractor presence and predictability. Furthermore, we found no effect of target-distractor similarity on VSTM behavioral performance, further challenging the role of sensory regions in VSTM storage. Overall, consistent with previous univariate findings, our results indicate that superior IPS, but not occipital cortex, has a central role in VSTM storage.

Robust distributed control of spacecraft formation flying with adaptive network topology

NASA Astrophysics Data System (ADS)

Shasti, Behrouz; Alasty, Aria; Assadian, Nima

2017-07-01

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

Design of control laws for flutter suppression based on the aerodynamic energy concept and comparisons with other design methods

NASA Technical Reports Server (NTRS)

Nissim, E.

1989-01-01

The aerodynamic energy method is used in this paper to synthesize control laws for NASA's Drone for Aerodynamic and Structural Testing-Aerodynamic Research Wing 1 (DAST-ARW1) mathematical model. The performance of these control laws in terms of closed-loop flutter dynamic pressure, control surface activity, and robustness is compared against other control laws that appear in the literature and relate to the same model. A control law synthesis technique that makes use of the return difference singular values is developed in this paper. it is based on the aerodynamic energy approach and is shown to yield results superior to those given in the literature and based on optimal control theory. Nyquist plots are presented together with a short discussion regarding the relative merits of the minimum singular value as a measure of robustness, compared with the more traditional measure of robustness involving phase and gain margins.

Sexual dimorphism in relation to big-game hunting and economy in modern human populations.

PubMed

Collier, S

1993-08-01

Postcranial skeletal data from two recent Eskimo populations are used to test David Frayer's model of sexual dimorphism reduction in Europe between the Upper Paleolithic and Mesolithic. Frayer argued that a change from big-game hunting and adoption of new technology in the Mesolithic reduced selection for large body size in males and led to a reduction in skeletal sexual dimorphism. Though aspects of Frayer's work have been criticized in the literature, the association of big-game hunting and high sexual dimorphism is untested. This study employs univariate and multivariate analysis to test that association by examining sexual dimorphism of cranial and postcranial bones of two recent Alaskan Eskimo populations, one being big-game (whale and other large marine mammal) hunting people, and the second being salmon fishing, riverine people. While big-game hunting influences skeletal robusticity, it cannot be said to lead to greater sexual dimorphism generally. The two populations had different relative sexual dimorphism levels for different parts of the body. Notably, the big-game hunting (whaling) Eskimos had the lower multivariate dimorphism in the humerus, which could be expected to be the structure under greatest exertion by such hunting in males. While the exertions of the whale hunting economic activities led to high skeletal robusticity, as predicted by Frayer's model, this was true of the females as well as the males, resulting in low sexual dimorphism in some features. Females are half the sexual dimorphism equation, and they cannot be seen as constants in any model of economic behavior.

Exploring the relationship between entheseal changes and physical activity: a multivariate study.

PubMed

Milella, Marco; Cardoso, Francisca Alves; Assis, Sandra; Lopreno, Geneviève Perréard; Speith, Nivien

2015-02-01

Analyses of entheseal changes (EC) in identified skeletal samples employ a common research strategy based on the comparison between occupations grouped on the basis of shared biomechanical and/or social characteristics. Results from this approach are often ambiguous, with some studies that point to differences in EC between occupational samples and others failing to provide evidence of behavioral effects on EC. Here we investigate patterns of EC among documented occupations by means of a multivariate analysis of robusticity scores in nine postcranial entheses from a large (N = 372) contemporary skeletal sample including specimens from one Italian and two Portuguese identified collections. Data on entheseal robusticity, analyzed by pooled sides as well by separated sides and levels of asymmetry, are converted in binary scores and then analyzed through nonlinear principal component analysis and hierarchical cluster analysis. Results of these analyses are then used for the classification of occupations. Differences between occupational classes are tested by MANOVA and pairwise Hotelling's test. Results evidence three classes which separate occupations related to farming, physically demanding but generalized occupation, and physically undemanding occupations, with the more consistent differences between the first and the last classes. Our results are consistent with differences in biomechanical behavior between the occupations included in each class, and point to the physical and social specificity of farming activities. On the other hand, our study exemplifies the usefulness of alternative analytical protocols for the investigation of EC, and the value of research designs devoid of a priori assumptions for the test of biocultural hypotheses. Copyright © 2014 Wiley Periodicals, Inc.