An Experience Transfer Approach for the Initial Data of Iterative Learning Control

Abstract

Iterative learning control (ILC) requires that the operating conditions of the controlled system must remain unchanged in the repetitive learning process. If the parameters of system change, the former control experience of ILC would not be effective anymore. A new process of iterative learning has to restart, which will exhaust more time and resource. Compared with learning from zero experience, appropriate initial data for the first iteration could reduce the turns of iterations to achieve the target tracking accuracy. When the parameters of a linear system change, its structure and nature are still intrinsically related to the original system. So, if the experience obtained from original ILC could be correspondingly adjusted according to the difference of new and original system, and use the adjusted experience as the initial data in the new 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 the initial data of ILC is proposed in reference to the relation between the new and original systems. In this paper, via the method of recombining, translational and amplitude adjusting, the experience of former ILC is transferred as the initial control data of new ILC. Simulation shows that the convergence iteration of ILC with experience transfer approach reduces 55–75%, which demonstrates the effectiveness and advantages of the approach proposed in this paper. Both the deviation of the first iteration in ILC and the turns of iterations for achieving desired accuracy are reduced greatly.