Iterative learning control for systems with both parametric and non-parametric uncertainties

In this work, a new iterative learning control (ILC) algorithm performing state tracking in the presence of both parametric and non-parametric uncertainties is proposed. To deal with time-varying parametric uncertainties, iterative updating based on the previous iteration´s control signal and current iteration´s tracking error is employed. For norm-bounded nonparametric uncertainties, iterative updating combined with a robust control scheme is implemented. A kind of energy-function-based approach is utilized for control law design and learning convergence. Rigorous mathematical proof shows that the integration of the two different updating laws and the robust control scheme can guarantee convergence of the proposed iterative learning algorithm.