Novel controller design procedure for output based ILC update laws applied to discrete-time polytopic systems

This paper considers the design of iterative learning control laws for discrete-time linear time-invariant systems with polytopic uncertainty. To avoid state feedback or using an observer, the control law uses static output feedback. These systems’ control law design problem is reformulated in the repetitive process setting to ensure robust stability along the trial property. Hence, monotonic convergence in the trial-to-trial direction is guaranteed over the whole uncertainty domain. This approach results in a linear matrix inequality-based design, and an illustrative example is given.