Enhancing Iterative Learning Control With Fractional Power Update Law  

作　　者：Zihan Li Dong Shen Xinghuo Yu 

机构地区：[1]the School of Mathematics,Renmin University of China,Beijing 100872,China [2]the School of Engineering,RMIT University,Melbourne VIC 3001,Australia

出　　处：《IEEE/CAA Journal of Automatica Sinica》2023年第5期1137-1149,共13页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(62173333);Australian Research Council Discovery Program(DP200101199)。

摘　　要：The P-type update law has been the mainstream technique used in iterative learning control(ILC)systems,which resembles linear feedback control with asymptotical convergence.In recent years,finite-time control strategies such as terminal sliding mode control have been shown to be effective in ramping up convergence speed by introducing fractional power with feedback.In this paper,we show that such mechanism can equally ramp up the learning speed in ILC systems.We first propose a fractional power update rule for ILC of single-input-single-output linear systems.A nonlinear error dynamics is constructed along the iteration axis to illustrate the evolutionary converging process.Using the nonlinear mapping approach,fast convergence towards the limit cycles of tracking errors inherently existing in ILC systems is proven.The limit cycles are shown to be tunable to determine the steady states.Numerical simulations are provided to verify the theoretical results.

关 键 词：Asymptotic convergence convergence rate finiteiteration tracking fractional power learning rule limit cycles 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]