首页|Data-Driven Learning Control Algorithms for Unachievable Tracking Problems
Data-Driven Learning Control Algorithms for Unachievable Tracking Problems
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
国家科技期刊平台
NETL
NSTL
万方数据
维普
For unachievable tracking problems,where the sys-tem 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 P-type learning control scheme.Initially,we demonstrate the neces-sity 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 low-memory 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.
国家科技期刊平台
NSTL
万方数据
维普
