A Data-Driven Approach to Iterative Learning Control via Convex Optimization

A new data-driven iterative learning control methodology is presented which uses the frequency response data of a system in order to avoid the problem of unmodelled dynamics associated with low-order parametric models. A convex optimisation problem is formulated to design the learning filters such that the convergence criterion is minimised. Since the frequency response data of the system is used in obtaining these filters, robustness is ensured by eliminating the uncertainty in the modelling process. The effectiveness of the method is illustrated by considering a case study where the proposed design scheme is applied to a power converter control system for a specific accelerator requirement at CERN.