Parameter-dependent Lyapunov function-based robust iterative learning control for discrete systems with actuator faults

This paper considers iterative learning control for a class of uncertain multiple-input multiple-output discrete linear systems with polytopic uncertainties and actuator faults. The stability theory for linear repetitive processes is used to develop control law design algorithms that can be computed using linear matrix inequalities. A class of parameter-dependent Lyapunov functions is used with the aim of enlarging the allowed polytopic uncertainty range for successful design. The effectiveness and feasibility of the new design algorithms are illustrated by a gantry robot case study