Design of adaptive neural net controller

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3 Citations (Scopus)

Abstract

This paper presents an adaptive neural net controller for controlling given plants which are unknown. In the neural net structure, a two-layered network is used to emulate the unknown plant dynamics, and another two-layer neural network, which is the inverse of the estimator, is used to generate the control action on-line. A modified Widrow-Hoff delta rule is adopted as a learning algorithm to minimize the error between the real plant response and the output of the estimator. An effective learning method which is based on sliding motions is provided to tune the control action to improve the system performance and convergence. The major advantage of the proposed approach is that the lengthy training of the controller might be eliminated. The effectiveness of the proposed approach is illustrated through simulations of controlling a unstable plant and a normalized motor model with noise disturbances.

Original languageEnglish
Title of host publicationInternational IEEE/IAS Conference on Industrial Automation and Control: Emerging Technologies, Proceedings
PublisherIEEE
Pages335-341
Number of pages7
Publication statusPublished - 1995
EventProceedings of the 1995 International IEEE/IAS Conference on Industrial Automation and Control: Emerging Technologies - Taipei, Taiwan
Duration: 1995 May 221995 May 27

Other

OtherProceedings of the 1995 International IEEE/IAS Conference on Industrial Automation and Control: Emerging Technologies
CityTaipei, Taiwan
Period95/5/2295/5/27

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Yeh, Z. M. (1995). Design of adaptive neural net controller. In International IEEE/IAS Conference on Industrial Automation and Control: Emerging Technologies, Proceedings (pp. 335-341). IEEE.