An iterative learning control method for nonlinear systems with randomly varying trial lengths

This paper presents an iterative learning control (ILC) method for nonlinear systems where the trial lengths could be randomly varying in the iteration domain. Based on the concept of error tracking, a unique equivalent error is given to deal with the tracking tasks with non-uniform trial lengths, which can effectively mitigate the requirements on classic ILC that all trial lengths must be identical and the initial condition must remain to be a fixed value for each iteration. The learning law is derived from the Lyapunov-like approach, which can guarantee the convergence of the tracking error in mathematical expectation. Several simulations are presented to demonstrate the effectiveness of the proposed ILC scheme.