Indirect adaptive control using hopfield-based dynamic neural network for SISO nonlinear systems

Ping Cheng Chen*, Chi-Hsu Wang, Tsu Tian Lee

*Corresponding author for this work

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

Abstract

In this paper, we propose an indirect adaptive control scheme using Hopfield-based dynamic neural network for SISO nonlinear systems with external disturbances. Hopfield-based dynamic neural networks are used to obtain uncertain function estimations in an indirect adaptive controller, and a compensation controller is used to suppress the effect of approximation error and disturbance. The weights of Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov, so that the stability of the closed-loop system can be guaranteed. In addition, the tracking error can be attenuated to a desired level by selecting some parameters adequately. Simulation results illustrate the applicability of the proposed control scheme. The designed parsimonious structure of the Hopfield-based dynamic neural network makes the practical implementation of the work in this paper much easier.

Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks - 11th International Conference, EANN 2009, Proceedings
Pages336-349
Number of pages14
DOIs
StatePublished - 1 Dec 2009
Event11th International Conference on Engineering Applications of Neural Networks, EANN 2009 - London, United Kingdom
Duration: 27 Aug 200929 Aug 2009

Publication series

NameCommunications in Computer and Information Science
Volume43 CCIS
ISSN (Print)1865-0929

Conference

Conference11th International Conference on Engineering Applications of Neural Networks, EANN 2009
CountryUnited Kingdom
CityLondon
Period27/08/0929/08/09

Keywords

  • dynamic neural network
  • Hopfield-based dynamic neural network
  • indirect adaptive control
  • Lyapunov stability theory

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