University of Cambridge
Robust model predictive control for constrained, linear systems through approximate dynamic programming
The talk begins by reviewing how one can approach robust model predictive control for discrete-time, uncertain, constrained systems by dynamic programming. We then specialize to a certain class of linear systems with parametric uncertainties, so-called polyhedral dynamic programming, and demonstrate how to represent the cost-to-go functions and feasible sets exactly and compactly in terms of polyhedra in this case. As a method to lower the computational complexity we then present an approximation technique for dynamic programming that is suitable for this problem class. This is at the expense of optimality, but nevertheless allows to generate robustly stable feedback laws that are guaranteed to respect all constraints.