Fuzzy control for nonlinear systems via neural-network-based approach

Feng Hsiag Hsiao; Wei Ling Chiang; Cheng Wu Chen

doi:10.1080/15502280590923612

Fuzzy control for nonlinear systems via neural-network-based approach

Feng Hsiag Hsiao, Wei Ling Chiang, Cheng Wu Chen

Department of Civil Engineering

Research output: Contribution to journal › Article › peer-review

35 Scopus citations

Abstract

The stabilization problem is considered in this study for a nonlinear system. It is shown that the stability analysis of nonlinear systems can be reduced into linear matrix inequality (LMI) problems. First, the neural-network (NN) model is employed to approximate a nonlinear system via the back propagation algorithm. Then, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. In terms of Lyapunov’s direct method, a sufficient condition is provided to guarantee the stability of nonlinear systems. Based on this criterion, a model based fuzzy controller is then designed to stabilize the nonlinear system and the H control performance is achieved at the same time. Finally, two examples with numerical simulations are given to illustrate the control methodology.

Original language	English
Pages (from-to)	145-152
Number of pages	8
Journal	International Journal of Computational Methods in Engineering Science and Mechanics
Volume	6
Issue number	3
DOIs	https://doi.org/10.1080/15502280590923612
State	Published - 2005

Keywords

Fuzzy control
Neural network

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10.1080/15502280590923612

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title = "Fuzzy control for nonlinear systems via neural-network-based approach",

abstract = "The stabilization problem is considered in this study for a nonlinear system. It is shown that the stability analysis of nonlinear systems can be reduced into linear matrix inequality (LMI) problems. First, the neural-network (NN) model is employed to approximate a nonlinear system via the back propagation algorithm. Then, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. In terms of Lyapunov{\textquoteright}s direct method, a sufficient condition is provided to guarantee the stability of nonlinear systems. Based on this criterion, a model based fuzzy controller is then designed to stabilize the nonlinear system and the H control performance is achieved at the same time. Finally, two examples with numerical simulations are given to illustrate the control methodology.",

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AU - Hsiao, Feng Hsiag

AU - Chiang, Wei Ling

AU - Chen, Cheng Wu

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N2 - The stabilization problem is considered in this study for a nonlinear system. It is shown that the stability analysis of nonlinear systems can be reduced into linear matrix inequality (LMI) problems. First, the neural-network (NN) model is employed to approximate a nonlinear system via the back propagation algorithm. Then, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. In terms of Lyapunov’s direct method, a sufficient condition is provided to guarantee the stability of nonlinear systems. Based on this criterion, a model based fuzzy controller is then designed to stabilize the nonlinear system and the H control performance is achieved at the same time. Finally, two examples with numerical simulations are given to illustrate the control methodology.

AB - The stabilization problem is considered in this study for a nonlinear system. It is shown that the stability analysis of nonlinear systems can be reduced into linear matrix inequality (LMI) problems. First, the neural-network (NN) model is employed to approximate a nonlinear system via the back propagation algorithm. Then, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. In terms of Lyapunov’s direct method, a sufficient condition is provided to guarantee the stability of nonlinear systems. Based on this criterion, a model based fuzzy controller is then designed to stabilize the nonlinear system and the H control performance is achieved at the same time. Finally, two examples with numerical simulations are given to illustrate the control methodology.

KW - Fuzzy control

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JF - International Journal of Computational Methods in Engineering Science and Mechanics

IS - 3

ER -

Fuzzy control for nonlinear systems via neural-network-based approach

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Keywords

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