Combined direct/indirect adaptive fuzzy-neural networks control with state observer and supervisory controller for nonlinear dynamic systems

Chi-Hsu Wang; Tsung Chih Lin; Han Leih Liu

doi:10.1109/FUZZ.2001.1009096

Combined direct/indirect adaptive fuzzy-neural networks control with state observer and supervisory controller for nonlinear dynamic systems

Chi-Hsu Wang^*, Tsung Chih Lin, Han Leih Liu

^*Corresponding author for this work

Department of Electrical and Computer Engineering

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

A state observer-based combined direct/indirect adaptive FNN controller with supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The combined adaptive FNN controller, whose free parameters can be tuned on-line by observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which is adjusted by trade-off between plant knowledge and control knowledge, appended between indirect adaptive FNN control and direct adaptive FNN control. A supervisory controller is appended into the FNN controller to force the state to be within the constraint set.

Original language	English
Pages	880-883
Number of pages	4
DOIs	https://doi.org/10.1109/FUZZ.2001.1009096
State	Published - 1 Dec 2001
Event	10th IEEE International Conference on Fuzzy Systems - Melbourne, Australia Duration: 2 Dec 2001 → 5 Dec 2001

Conference

Conference	10th IEEE International Conference on Fuzzy Systems
Country/Territory	Australia
City	Melbourne
Period	2/12/01 → 5/12/01

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10.1109/FUZZ.2001.1009096

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author = "Chi-Hsu Wang and Lin, {Tsung Chih} and Liu, {Han Leih}",

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AB - A state observer-based combined direct/indirect adaptive FNN controller with supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The combined adaptive FNN controller, whose free parameters can be tuned on-line by observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which is adjusted by trade-off between plant knowledge and control knowledge, appended between indirect adaptive FNN control and direct adaptive FNN control. A supervisory controller is appended into the FNN controller to force the state to be within the constraint set.

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Combined direct/indirect adaptive fuzzy-neural networks control with state observer and supervisory controller for nonlinear dynamic systems

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