Adaptively controlling nonlinear continuous-time systems using neural networks

Fu-Chuang Chen*, Chen Chung Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Layered neural networks are used in a nonlinear adaptive control problem. The plant is an unknown feedback-linearizable continuous-time system, represented in a state space form. A transformation is made on the plant to decompose the plant into two parts: The first part is modeled and controlled by multilayer neural networks. The second part is unobservable and can not be directly influenced by the control; this part is assumed to be stable. The control law is defined in terms of the neural network model to control the plant to track a reference command. The network parameters are updated on-line according to the tracking error. A theorem is given on the convergence of i) the tracking error and ii) the weight updating. The simulation is performed using Advanced Continuous Simulation Language (ACSL).

Original languageEnglish
Title of host publicationProceedings of the American Control Conference
PublisherPubl by American Automatic Control Council
Pages46-50
Number of pages5
ISBN (Print)0780302109
DOIs
StatePublished - 1 Dec 1992
EventProceedings of the 1992 American Control Conference - Chicago, IL, USA
Duration: 24 Jun 199226 Jun 1992

Publication series

NameProceedings of the American Control Conference
Volume1
ISSN (Print)0743-1619

Conference

ConferenceProceedings of the 1992 American Control Conference
CityChicago, IL, USA
Period24/06/9226/06/92

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