Stochastic Iterative Learning Control With Faded Signals  

作　　者：Ganggui Qu Dong Shen 

机构地区：[1]the College of Information Science and Technology,Beijing University of Chemical Technology,Beijing 100029,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2019年第5期1196-1208,共13页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(61673045);the Fundamental Research Funds for the Central Universities(XK1802-4)

摘　　要：Stochastic iterative learning control(ILC) is designed for solving the tracking problem of stochastic linear systems through fading channels. Consequently, the signals used in learning control algorithms are faded in the sense that a random variable is multiplied by the original signal. To achieve the tracking objective, a two-dimensional Kalman filtering method is used in this study to derive a learning gain matrix varying along both time and iteration axes. The learning gain matrix minimizes the trace of input error covariance. The asymptotic convergence of the generated input sequence to the desired input value is strictly proved in the mean-square sense. Both output and input fading are accounted for separately in turn, followed by a general formulation that both input and output fading coexists.Illustrative examples are provided to verify the effectiveness of the proposed schemes.Stochastic iterative learning control(ILC) is designed for solving the tracking problem of stochastic linear systems through fading channels. Consequently, the signals used in learning control algorithms are faded in the sense that a random variable is multiplied by the original signal. To achieve the tracking objective, a two-dimensional Kalman filtering method is used in this study to derive a learning gain matrix varying along both time and iteration axes. The learning gain matrix minimizes the trace of input error covariance. The asymptotic convergence of the generated input sequence to the desired input value is strictly proved in the mean-square sense. Both output and input fading are accounted for separately in turn, followed by a general formulation that both input and output fading coexists.Illustrative examples are provided to verify the effectiveness of the proposed schemes.

关 键 词：FADING channels ITERATIVE learning control (ILC) KALMAN filtering  mean-square convergence STOCHASTIC systems 

分 类 号：TP[自动化与计算机技术]