Iterative learning control with parameter estimation for non-repetitive time-varying systems

This paper presents an extension method of iterative learning control (ILC) to address the applications associated with non-repetitive time-varying systems (NTVSs). Conventional ILC approaches employ fixed nominal system models, but non-repetitive time-varying models may lead to accumulated model un...

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Bibliographic Details
Published in:Journal of the Franklin Institute 2024-02, Vol.361 (3), p.1455-1466
Main Authors: Wang, Lei, Huangfu, Ziwei, Li, Ruiwen, Wen, Xiewen, Sun, Yuan, Chen, Yiyang
Format: Article
Language:English
Subjects:
Bayesian regularization
Iterative learning control
Neural network
Non-repetitive time-varying systems
Parameter estimation
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Summary:This paper presents an extension method of iterative learning control (ILC) to address the applications associated with non-repetitive time-varying systems (NTVSs). Conventional ILC approaches employ fixed nominal system models, but non-repetitive time-varying models may lead to accumulated model uncertainties, which fails to satisfy the robust convergence conditions. To tackle this issue, a novel ILC algorithm with parameter estimation is proposed using back propagation neural network. This algorithm incorporates an approach that utilizes Bayesian regularization training mechanism to accurately estimate non-repetitive time-varying parameters. Through comprehensive experiment on Monolithic XY Stage, the performance of proposed algorithm is validated to demonstrate its feasibility and effectiveness while handling tasks on NTVSs.
ISSN:0016-0032
DOI:10.1016/j.jfranklin.2024.01.011