Almost sure convergence of iterative learning control for stochastic systems  

作　　者：陈翰馥 

机构地区：[1]Institute of Systems Science, Academy of Mathematics System Sciences, Chinese Academy of Sciences

出　　处：《Science in China(Series F)》2003年第1期67-79,共13页中国科学（F辑英文版）

基　　金：This work was supported by the National Natural Science Foundation of China ;by the Ministry of Science and Technology of China.

摘　　要：This paper proposes an iterative learning control (ILC) algorithm with the purpose of controling the output of a linear stochastic system presented in state space form to track a desired realizable trajectory. It is proved that the algorithm converges to the optimal one a.s. under the condition that the product input-output coupling matrices are full-column rank in addition to some assumptions on noises. No other knowledge about system matrices and covariance matrices is required.This paper proposes an iterative learning control (ILC) algorithm with the purpose of controling the output of a linear stochastic system presented in state space form to track a desired realizable trajectory. It is proved that the algorithm converges to the optimal one a.s. under the condition that the product input-output coupling matrices are full-column rank in addition to some assumptions on noises. No other knowledge about system matrices and covariance matrices is required.

关 键 词：iterative learning control stochastic system a.s. convergence TRACKING stochastic approximation. 

分 类 号：TP273.5[自动化与计算机技术—检测技术与自动化装置]