A real-time learning control approach for nonlinear continuous-time system using recurrent neural networks

Author

Chow, Tommy W S ; Li, Xiao-Dong ; Fang, Yong

Author_Institution

Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, Hong Kong

Volume

47

Issue

2

fYear

2000

fDate

4/1/2000 12:00:00 AM

Firstpage

478

Lastpage

486

Abstract

In this paper, a real-time iterative learning control (ILC) approach for a nonlinear continuous-time system using recurrent neural networks (RNNs) with time-varying weights is presented. Two RNNs are utilized in the ILC system. One is used to approximate the nonlinear system and another is used to mimic the desired system response. The ILC rule is obtained by combining the two RNNs to form a neural network control system. Also, a kind of iterative RNNs training algorithm is developed based on the two-dimensional (2-D) system theory. An RNN using the proposed 2-D training algorithm is able to approximate any trajectory to a very high degree of accuracy. Simulation results show that the proposed ILC approach is very efficient. The newly developed 2-D RNNs training algorithms provides a new dimension to the application of RNNs in a nonlinear continuous-time system

Keywords

continuous time systems; control system analysis; control system synthesis; iterative methods; learning (artificial intelligence); neurocontrollers; nonlinear control systems; recurrent neural nets; 2-D training algorithm; control design; control simulation; iterative training algorithm; nonlinear continuous-time system; real-time iterative learning control approach; recurrent neural networks; system response; time-varying weights; Control systems; Iterative algorithms; Iterative methods; Neural networks; Nonlinear control systems; Nonlinear systems; Real time systems; Recurrent neural networks; Time varying systems; Two dimensional displays;

fLanguage

English

Journal_Title

Industrial Electronics, IEEE Transactions on

Publisher

ieee

ISSN

0278-0046

Type

jour

DOI

10.1109/41.836364

Filename

836364