Design of iterative learning control schemes for systems with sector-bounded nonlinearities

This paper uses a convex optimization setting to design iterative learning control laws for discrete-time systems with a static nonlinearity. The nonlinearity is assumed to be sector bounded, where such nonlinearities often arise in applications. For systems with such nonlinearities the repetitive process setting is exploited to develop a linear matrix inequality based conditions for computing the feedback and feedforward (learning) controllers. These controllers guarantee acceptable dynamics along the trials and ensure monotonic convergence of the trial-to-trial error dynamics, respectively. A simulation example is given to illustrate the new design.