Browse by author
Lookup NU author(s): Dr Jie Zhang
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Iterative learning control (ILC) requires that the operating conditions of the controlledsystem must remain unchanged in the repetitive learning process. If the parameters of systemchange, the former control experience of ILC would not be effective anymore. A new process ofiterative learning has to restart, which will exhaust more time and resource. Compared withlearning from zero experience, appropriate initial data for the first iteration could reduce the turnsof iterations to achieve the target tracking accuracy. When the parameters of a linear systemchange, its structure and nature are still intrinsically related to the original system. So, if theexperience obtained from original ILC could be correspondingly adjusted according to thedifference of new and original system, and use the adjusted experience as the initial data in thenew iterative learning process, it would reduce the time and save the resource in the new ILC.Based on the idea of experience inheritance and transform, an experience transfer approach for theinitial data of ILC is proposed in reference to the relation between the new and original systems. Inthis paper, via the method of recombining, translational and amplitude adjusting, the experienceof former ILC is transferred as the initial control data of new ILC. Simulation shows that theconvergence iteration of ILC with experience transfer approach reduces 55–75%, whichdemonstrates the effectiveness and advantages of the approach proposed in this paper. Both thedeviation of the first iteration in ILC and the turns of iterations for achieving desired accuracy arereduced greatly.
Author(s): Liu S, Liu Z, Zhang J, Hu D
Publication type: Article
Publication status: Published
Journal: Applied Sciences
Online publication date: 11/02/2021
Acceptance date: 07/02/2021
Date deposited: 11/02/2021
ISSN (electronic): 2076-3417
Altmetrics provided by Altmetric