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

Teh-Lu Liao, J. H. Horng

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題ECC 1997 - European Control Conference
發行者Institute of Electrical and Electronics Engineers Inc.
頁面506-511
頁數6
ISBN(電子)9783952426906
出版狀態Published - 1997 四月 8
事件4th European Control Conference, ECC 1997 - Brussels, Belgium
持續時間: 1997 七月 11997 七月 4

出版系列

名字ECC 1997 - European Control Conference

Other

Other4th European Control Conference, ECC 1997
國家Belgium
城市Brussels
期間97-07-0197-07-04

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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