Lifted System Framework for Learning Control with Different Trial Lengths

This paper addresses an iterative learning control (ILC) design problem for discrete-time linear systems with randomly varying trial lengths. Due to the variation of the trial lengths, a stochastic matrix and an iteration-average operator are introduced to present a unified expression of ILC scheme. By using the framework of lifted system, the learning convergence condition of ILC in mathematical expectation is derived without using λ-norm. It is shown that the requirement on classic ILC that all trial lengths must be identical is mitigated and the identical initialization condition can be also removed. In the end, two illustrative examples are presented to demonstrate the performance and the effectiveness of the proposed ILC scheme for both time-invariant and time-varying linear systems.