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Asynchronous stochastic learning control with accelerative factor in multivariable systems

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.