A Two-Dimensional Approach to Iterative Learning Control with Randomly Varying Trial Lengths  

作　　者：LIU Chen SHEN Dong WANG Jinrong 

机构地区：[1]College of Information Science and Technology,Beijing University of Chemical Technology,Beijing 100029,China [2]School of Mathematics,Renmin University of China,Beijing 100872,China [3]Department of Mathematics,Guizhou University,Guiyang 550025,China [4]School of Mathematical Sciences,Qufu Normal University,Qufu 273165,China

出　　处：《Journal of Systems Science & Complexity》2020年第3期685-705,共21页系统科学与复杂性学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant Nos.61673045and 11661016。

摘　　要：In this paper,iterative learning control(ILC)is considered to solve the tracking problem of time-varying linear stochastic systems with randomly varying trial lengths.Using the two-dimensional Kalman filtering technique,the authors can establish a recursive framework for designing the learning gain matrix along both time and iteration axes by optimizing the trace of input error covariance matrix.It is strictly proved that the input error converges to zero asymptotically in mean square sense and thus the tracking error covariance converges.The extensions to that prior distribution of nonuniform trial lengths is unknown are also investigated with an asymptotical estimation method.Numerical simulations are provided to verify the effectiveness of the proposed framework.

关 键 词：Iterative learning control Kalman filtering recursive estimation varying trial lengths 

分 类 号：O231[理学—运筹学与控制论]