Indirect adaptive control of a class of unknown nonlinear discrete-time systems using hybrid neural networks

Jui Hong Horng, Teh-Lu Liao

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, an indirect adaptive controller based on hybrid neural networks, which are composed of two-layered neural networks and radial basis function (RBF) neural networks, is derived for controlling a class of unknown nonlinear discrete-time systems. A hybrid-neural-network-based estimator is used to characterize the input-output behavior of the unknown systems. The adaptation law which adjusts the connection weights of the neural network is used to minimize the error signal which is difference between the actual response and that of the neural network. The indirect adaptive control law is generated on-line using another hybrid neural network related to the estimator, so that the plant results in a bounded tracking error with respect to a desired reference signal. It is proved that the control objective is achieved by the closed-loop system and that the system remains closed-loop stability. The effectiveness of the proposed control scheme is also demonstrated by two simulation examples.

Original languageEnglish
Pages (from-to)281-299
Number of pages19
JournalSystems Analysis Modelling Simulation
Volume28
Issue number1-4
Publication statusPublished - 1997 Jan 1

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Applied Mathematics

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