Ellipsoid invariant set-based robust model predictive control for repetitive processes with constraints

摘要

The idea of combining an iterative learning control with a feedback control under a two-time dimensional (2D) framework has been widely applied in the control of unconstrained repetitive processes. However, how to extend the method to cover constrained systems remains an issue. In this study, a robust model predictive controller, with an iterative learning control incorporated under the 2D framework, is designed for constrained repetitive processes. This controller is able to explicitly guarantee 2D stability and consistent feasibility, making all choices of tuning parameters feasible to constraints. In this way, complicated tuning procedures are avoided and more freedom for controller design and performance optimisation is allowed. The method is applicable to both stable and unstable systems.