Kernel-based auto-associative P-type iterative learning control strategy  

作　　者：LAN Tianyi LIN Hui LI Bingqiang 

机构地区：[1]School of Automation,Northwestern Polytechnical University,Xi’an 710129,China

出　　处：《Journal of Systems Engineering and Electronics》2020年第2期383-392,共10页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(51777170);the Aeronautical Science Foundation of China(20162853026);the Project Supported by Natural Science Basic Research Plan in Shannxi Province of China(2019JM-462,2020JM-151)。

摘　　要：In order to accelerate the convergence speed of iterative learning control(ILC), taking the P-type learning algorithm as an example, a correction algorithm with kernel-based autoassociative is proposed for the linear system. The learning mechanism of human brain associative memory is introduced to the traditional ILC. The control value of the subsequent time is precorrected with the current time information by association in each iterative learning process. The learning efficiency of the whole system is improved significantly with the proposed algorithm. Through the rigorous analysis, it shows that under this new designed ILC scheme, the uniform convergence of the state tracking error is guaranteed. Numerical simulations illustrate the effectiveness of the proposed associative control scheme and the validity of the conclusion.

关 键 词：iterative LEARNING control(ILC) ASSOCIATIVE LEARNING CONVERGENCE speed tracking CONVERGENCE 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]