Adaptive control of a class of nonlinear discrete-time systems using hybrid neural networks

Teh-Lu Liao, J. H. Horng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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. An 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 a simulation example.

Original languageEnglish
Title of host publicationECC 1997 - European Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9783952426906
Publication statusPublished - 1997 Apr 8
Event4th European Control Conference, ECC 1997 - Brussels, Belgium
Duration: 1997 Jul 11997 Jul 4

Publication series

NameECC 1997 - European Control Conference


Other4th European Control Conference, ECC 1997

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering


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