Asynchronous stochastic learning control with accelerative factor in multivariable systems

Author

Deng, Zhidong ; Zhang, Zaixing ; Sun, Zengqi

Author_Institution

Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China

Volume

4

fYear

1995

fDate

22-25 Oct 1995

Firstpage

3790

Abstract

The asynchronous stochastic learning control system (ASLC) proposed by Zengqi Sun and Zhidong Deng (1993), which is able to cope with unrepetitiveness of SISO systems with measurement noise, is extended to multivariable control systems in this paper. By using stochastic approximation algorithms, an asynchronous stochastic learning control law is derived and corresponding convergence proofs are strictly given. To speed up the convergence of stochastic learning. The ASLC with accelerative factor is presented. A simulation example is given

Keywords

approximation theory; convergence; learning systems; multivariable control systems; stochastic systems; accelerative factor; asynchronous stochastic learning control; convergence proofs; measurement noise; multivariable systems; stochastic approximation algorithms; unrepetitiveness; Acceleration; Approximation algorithms; Automatic logic units; Control systems; Convergence; MIMO; Noise measurement; Optimal control; Stochastic resonance; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-2559-1

Type

conf

DOI

10.1109/ICSMC.1995.538378

Filename

538378