The Experts below are selected from a list of 156 Experts worldwide ranked by ideXlab platform
Snehasis Mukhopadhyay - One of the best experts on this subject based on the ideXlab platform.
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adaptive control of nonlinear Multivariable Systems using neural networks
Neural Networks, 1994Co-Authors: Kumpati S Narendra, Snehasis MukhopadhyayAbstract:Abstract Most practical Systems have multiple inputs and multiple outputs, and the applicability of neural networks as practical adaptive controllers will eventually be judged by their success in Multivariable problems. The representation, identification, and control of nonlinear Multivariable Systems are rendered difficult by the coupling as well as the delays that exist between the inputs and outputs. In the first part of the paper, theoretical questions related to system representation and existence of a desired control input are discussed. The second part of the paper develops a design methodology using neural networks. It is shown that under appropriate conditions, it may be possible to design efficient neural controllers for nonlinear Multivariable Systems for which linear controllers are inadequate.
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Adaptive control of nonlinear Multivariable Systems using neural networks
Proceedings of 32nd IEEE Conference on Decision and Control, 1Co-Authors: Kumpati S Narendra, Snehasis MukhopadhyayAbstract:In this paper we examine the problem of control of Multivariable Systems using neural networks. The problem is discussed assuming different amounts of prior information concerning the plant and hence different levels of complexity. In the first stage it is assumed that the state equations describing the plant are known and that the state of the system is accessible. Following this the same problem is considered when the state equations are unknown. In the last stage the adaptive control of the Multivariable system using only input-output data is discussed in detail. The objective of the paper is to demonstrate that results from nonlinear control theory and linear adaptive control theory can be used to design practically viable controllers for unknown nonlinear Multivariable Systems using neural networks. The different assumptions that have to be made, the choice of identifier and controller architectures and the generation of adaptive laws for the adjustment of the parameters of the neural networks form the core of the paper. >
Kumpati S Narendra - One of the best experts on this subject based on the ideXlab platform.
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adaptive control of nonlinear Multivariable Systems using neural networks
Neural Networks, 1994Co-Authors: Kumpati S Narendra, Snehasis MukhopadhyayAbstract:Abstract Most practical Systems have multiple inputs and multiple outputs, and the applicability of neural networks as practical adaptive controllers will eventually be judged by their success in Multivariable problems. The representation, identification, and control of nonlinear Multivariable Systems are rendered difficult by the coupling as well as the delays that exist between the inputs and outputs. In the first part of the paper, theoretical questions related to system representation and existence of a desired control input are discussed. The second part of the paper develops a design methodology using neural networks. It is shown that under appropriate conditions, it may be possible to design efficient neural controllers for nonlinear Multivariable Systems for which linear controllers are inadequate.
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Adaptive control of nonlinear Multivariable Systems using neural networks
Proceedings of 32nd IEEE Conference on Decision and Control, 1Co-Authors: Kumpati S Narendra, Snehasis MukhopadhyayAbstract:In this paper we examine the problem of control of Multivariable Systems using neural networks. The problem is discussed assuming different amounts of prior information concerning the plant and hence different levels of complexity. In the first stage it is assumed that the state equations describing the plant are known and that the state of the system is accessible. Following this the same problem is considered when the state equations are unknown. In the last stage the adaptive control of the Multivariable system using only input-output data is discussed in detail. The objective of the paper is to demonstrate that results from nonlinear control theory and linear adaptive control theory can be used to design practically viable controllers for unknown nonlinear Multivariable Systems using neural networks. The different assumptions that have to be made, the choice of identifier and controller architectures and the generation of adaptive laws for the adjustment of the parameters of the neural networks form the core of the paper. >
Xiaowei Yu - One of the best experts on this subject based on the ideXlab platform.
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Second-order terminal sliding mode control of uncertain Multivariable Systems
International Journal of Control, 2007Co-Authors: Y Feng, Yongxing Wang, Yongqiang Wang, Xiao Han, Xiaowei YuAbstract:A second-order terminal sliding mode controller for uncertain Multivariable Systems is proposed in this paper. The controller adopts the hierarchical control structure. The paper derives the state transform matrices which are used to transform a Multivariable linear system to the block controllable form consisting of two subSystems, an input-output subsystem and a stable internal dynamic subsystem. The proposed controller utilizes a non-singular terminal sliding mode manifold for the input-output subsystem to realize fast convergence and better tracking precision. Meanwhile, a chattering-free second-order terminal sliding mode control law is presented. The stability of uncertain Multivariable Systems can be realized using the proposed controller. A derivative estimator is utilized in the paper to estimate the derivatives of the sliding mode functions for the controller. The simulation results are presented to validate the design method.
Feng Ding - One of the best experts on this subject based on the ideXlab platform.
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filtering based iterative identification for Multivariable Systems
Iet Control Theory and Applications, 2016Co-Authors: Yanjiao Wang, Feng DingAbstract:This study applies the filtering technique to system identification to study the data filtering-based parameter estimation methods for Multivariable Systems, which are corrupted by correlated noise – an autoregressive moving average process. To solve the difficulty that the identification model contains the unmeasurable variables and noise terms in the information matrix, the authors present a hierarchical gradient-based iterative (HGI) algorithm by using the hierarchical identification principle. To improve the convergence rate, they apply the filtering technique to derive a filtering-based HGI algorithm and a filtering-based hierarchical least squares-based iterative (HLSI) algorithm. The simulation examples indicate that the filtering-based HLSI algorithm has the highest computational efficiency among these three algorithms.
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hierarchical estimation algorithms for Multivariable Systems using measurement information
Information Sciences, 2014Co-Authors: Feng DingAbstract:Abstract With the development of industry information technology, many modelling methods have been focusing on the estimation problems of Multivariable Systems, especially for the Multivariable Systems with output error autoregressive noises, from input–output measurement information. Since such a system includes both a parameter vector and a parameter matrix, the conventional methods cannot be applied to parameter estimation and modelling. In order to solve this difficulty, a hierarchical least squares based iterative identification algorithm and a hierarchical generalized least squares identification algorithm are proposed. The basic idea is to decompose the system into two fictitious subSystems, to estimate the parameters of each subsystem, and to coordinate the associated items between the two subSystems. The simulation results indicate that the proposed algorithm is effective.
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Maximum Likelihood Recursive Least Squares Estimation for Multivariable Systems
Circuits Systems and Signal Processing, 2014Co-Authors: Feng Ding, Ping Jiang, Daqi ZhuAbstract:This paper discusses parameter estimation problems of the Multivariable Systems described by input---output difference equations. We decompose a Multivariable system to several subSystems according to the number of the outputs. Based on the maximum likelihood principle, a maximum likelihood-based recursive least squares algorithm is derived to estimate the parameters of each subsystem. Finally, two numerical examples are provided to verify the effectiveness of the proposed algorithm.
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coupled least squares identification for Multivariable Systems
Iet Control Theory and Applications, 2013Co-Authors: Feng DingAbstract:This article studies identification problems of multiple linear regression models, which may be described a class of multi-input multi-output Systems (i.e. Multivariable Systems). Based on the coupling identification concept, a novel coupled-least-squares (C-LS) parameter identification algorithm is introduced for the purpose of avoiding the matrix inversion in the Multivariable recursive least-squares (RLS) algorithm for estimating the parameters of the multiple linear regression models. The analysis indicates that the C-LS algorithm does not involve the matrix inversion and requires less computationally efforts than the Multivariable RLS algorithm, and that the parameter estimates given by the C-LS algorithm converge to their true values. Simulation results confirm the presented convergence theorems.
Madan M. Gupta - One of the best experts on this subject based on the ideXlab platform.
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Control of nonlinear Multivariable Systems using a dynamic neural network
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 1Co-Authors: D.h. Rao, H.c. Wood, Madan M. GuptaAbstract:A complex control system, in general, consists of two or more independently designed and mutually affecting subSystems. Proper coordination and control of multiple subSystems is responsible for the overall functioning of the system. This necessitates the development of control schemes for Multivariable Systems. This is a formidable task; more so if the Systems involved are nonlinear with unknown dynamics. Because of their parallelism, functional approximation and learning capabilities, artificial neural networks can be effectively employed to control Multivariable Systems. The intent of this paper is to describe a neural network called the dynamic neural processor (DNP), and to use this structure to control nonlinear Multivariable Systems. The DNP is a dynamic neural network developed based on the concept of neural subpopulations which is in sharp contrast with the conventionally assumed structure of artificial neural networks. >
