Data-Driven Learning Control Algorithms for Unachievable Tracking Problems  

作　　者：Zeyi Zhang Hao Jiang Dong Shen Samer S.Saab 

机构地区：[1]the School of Mathematics,Renmin University of China,Beijing 100872,China [2]IEEE [3]the School of Engineering,Lebanese American University,Byblos 2038,Lebanon

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第1期205-218,共14页自动化学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (62173333, 12271522);Beijing Natural Science Foundation (Z210002);the Research Fund of Renmin University of China (2021030187)。

摘　　要：For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.

关 键 词：Data-driven algorithms incomplete information iterative learning control gradient information unachievable problems 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]