Reference model iterative learning control for nonlinear systems with repeatable and non-repeatable uncertainties

This paper deals with iterative learning control (ILC) design for nonlinear systems with repeatable and non-repeatable uncertainties and performing repetitive tasks to follow a reference model (also called desired system). This desired system does not necessarily have the same structure, nor the same parameters as the real systems (there is no dependence between the reference model system and the real system). For this purpose, two ILC schemes are considered and analysed. The first controller assures the asymptotic stability with a simple condition to verify, whereas the second assures this stability without condition to verify. The lambda-norm is adopted as the topological measure in our proof of the asymptotic stability of the closed loop system over the whole finite time interval when the iteration number tends to infinity. Finally, two simulation results on nonlinear system are provided to illustrate the effectiveness of the proposed controllers.