New approach to adaptive control architecture based on fuzzy neural network and genetic algorithm

Liang-Hsuan Chen; Cheng Hsiung Chiang; John Yuan

New approach to adaptive control architecture based on fuzzy neural network and genetic algorithm

Liang-Hsuan Chen, Cheng Hsiung Chiang, John Yuan

Department of Industrial and Information Management

Research output: Contribution to journal › Conference article › peer-review

4 Citations (Scopus)

Abstract

A novel approach to adaptive control architecture, which formulates the adaptive behavior of human mathematically, is presented. It is made up of the two main modules: the Controller constructed by fuzzy neural network (FNN), and the Adapter composed of three components: the Performance Evaluator (PE), the Action Searcher (AS), and the Rule Constructer (RC). The Controller and Adaptor perform the off-line and on-line learning to learn control strategy and to adapt variant environments. The PE evaluates the system's performance. If the control effect is satisfactory, the Controller keeps on its assignment. Otherwise, the genetic algorithm (GA)-based AS will explore the new control actions. Then, the RC transforms these actions to the fuzzy rules and updates the corresponding fuzzy rules in Controller. An example of the path planning of a mobile robot is to demonstrate this presented method.

Original language	English
Pages (from-to)	347-352
Number of pages	6
Journal	Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume	1
Publication status	Published - 2001 Dec 1
Event	2001 IEEE International Conference on Systems, Man and Cybernetics - Tucson, AZ, United States Duration: 2001 Oct 7 → 2001 Oct 10

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Hardware and Architecture

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abstract = "A novel approach to adaptive control architecture, which formulates the adaptive behavior of human mathematically, is presented. It is made up of the two main modules: the Controller constructed by fuzzy neural network (FNN), and the Adapter composed of three components: the Performance Evaluator (PE), the Action Searcher (AS), and the Rule Constructer (RC). The Controller and Adaptor perform the off-line and on-line learning to learn control strategy and to adapt variant environments. The PE evaluates the system's performance. If the control effect is satisfactory, the Controller keeps on its assignment. Otherwise, the genetic algorithm (GA)-based AS will explore the new control actions. Then, the RC transforms these actions to the fuzzy rules and updates the corresponding fuzzy rules in Controller. An example of the path planning of a mobile robot is to demonstrate this presented method.",

author = "Liang-Hsuan Chen and Chiang, {Cheng Hsiung} and John Yuan",

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note = "2001 IEEE International Conference on Systems, Man and Cybernetics ; Conference date: 07-10-2001 Through 10-10-2001",

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AU - Chen, Liang-Hsuan

AU - Chiang, Cheng Hsiung

AU - Yuan, John

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N2 - A novel approach to adaptive control architecture, which formulates the adaptive behavior of human mathematically, is presented. It is made up of the two main modules: the Controller constructed by fuzzy neural network (FNN), and the Adapter composed of three components: the Performance Evaluator (PE), the Action Searcher (AS), and the Rule Constructer (RC). The Controller and Adaptor perform the off-line and on-line learning to learn control strategy and to adapt variant environments. The PE evaluates the system's performance. If the control effect is satisfactory, the Controller keeps on its assignment. Otherwise, the genetic algorithm (GA)-based AS will explore the new control actions. Then, the RC transforms these actions to the fuzzy rules and updates the corresponding fuzzy rules in Controller. An example of the path planning of a mobile robot is to demonstrate this presented method.

AB - A novel approach to adaptive control architecture, which formulates the adaptive behavior of human mathematically, is presented. It is made up of the two main modules: the Controller constructed by fuzzy neural network (FNN), and the Adapter composed of three components: the Performance Evaluator (PE), the Action Searcher (AS), and the Rule Constructer (RC). The Controller and Adaptor perform the off-line and on-line learning to learn control strategy and to adapt variant environments. The PE evaluates the system's performance. If the control effect is satisfactory, the Controller keeps on its assignment. Otherwise, the genetic algorithm (GA)-based AS will explore the new control actions. Then, the RC transforms these actions to the fuzzy rules and updates the corresponding fuzzy rules in Controller. An example of the path planning of a mobile robot is to demonstrate this presented method.

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