Quasi-Newton-type optimized iterative learning control for discrete linear time invariant systems  

作　　者：Yan GENG Xiaoe RUAN 

机构地区：[1]School of Mathematics and Statistics,Xi'an Jiaotong University

出　　处：《Control Theory and Technology》2015年第3期256-265,共10页控制理论与技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(Nos.F010114-60974140,61273135)

摘　　要：In this paper, a quasi-Newton-type optimized iterative learning control （ILC） algorithm is investigated for a class of discrete linear time-invariant systems. The proposed learning algorithm is to update the learning gain matrix by a quasi-Newton-type matrix instead of the inversion of the plant. By means of the mathematical inductive method, the monotone convergence of the proposed algorithm is analyzed, which shows that the tracking error monotonously converges to zero after a finite number of iterations. Compared with the existing optimized ILC algorithms, due to the superlinear convergence of quasi-Newton method, the proposed learning law operates with a faster convergent rate and is robust to the ill-condition of the system model, and thus owns a wide range of applications. Numerical simulations demonstrate the validity and effectiveness.In this paper, a quasi-Newton-type optimized iterative learning control （ILC） algorithm is investigated for a class of discrete linear time-invariant systems. The proposed learning algorithm is to update the learning gain matrix by a quasi-Newton-type matrix instead of the inversion of the plant. By means of the mathematical inductive method, the monotone convergence of the proposed algorithm is analyzed, which shows that the tracking error monotonously converges to zero after a finite number of iterations. Compared with the existing optimized ILC algorithms, due to the superlinear convergence of quasi-Newton method, the proposed learning law operates with a faster convergent rate and is robust to the ill-condition of the system model, and thus owns a wide range of applications. Numerical simulations demonstrate the validity and effectiveness.

关 键 词：Iterative learning control optimization quasi-Newton method inverse plant 

分 类 号：O231[理学—运筹学与控制论] TP273.22[理学—数学]