Optimal iterative learning control design with trial-varying initial conditions

In this paper we present an approach to deal with trial-varying initial conditions in norm-optimal iterative learning control (ILC). Varying initial conditions generally degrade the performance of conventional learning algorithms. We therefore introduce a worst-case optimization problem that accounts for trial-varying of initial conditions. The optimization is then reformulated as a convex minimization problem, which can be solved efficiently to generate the control signal. We investigate the relationship between the proposed approach and classical norm-optimal ILC; where we find that our methodology is equivalent to classical norm-optimal ILC with trial-varying parameters. Finally, simulation results of the presented technique are given.