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 proceedingChapterpeer-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 publicationAspects of Soft Computing, Intelligent Robotics and Control
EditorsJ. Fodor, Janusz Kacprzyk
Pages189-204
Number of pages16
DOIs
StatePublished - 26 Oct 2009

Publication series

NameStudies in Computational Intelligence
Volume241
ISSN (Print)1860-949X

Keywords

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

Fingerprint Dive into the research topics of 'Indirect adaptive control using hopfield-based dynamic neural network for SISO nonlinear systems'. Together they form a unique fingerprint.

Cite this