Robust model predictive control through adjustable variables: an application to path planning

Robustness in model predictive control (MPC) is the main focus of this work. After a definition of the conceptual framework and of the problem's setting, we analyze how a technique developed for studying robustness in convex optimization can be applied to address the problem of robustness in the MPC case. Therefore, exploiting this relationship between control and optimization, we tackle robustness issues for the first setting through methods developed in the second framework. Proofs for our results are included. As an application of this robust MPC result, we consider a path planning problem and discuss some simulations thereabout.