Title :
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
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;
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
DOI :
10.1109/ICSMC.1995.538378
