ForschungPublikationen
A computationally efficient robust model predictive control framework for uncertain nonlinear systems

A computationally efficient robust model predictive control framework for uncertain nonlinear systems

Kategorien Zeitschriftenartikel
Jahr 2020
Autoren Johannes Köhler, Raffaele Soloperto, Matthias A. Müller, Frank Allgöwer
Veröffentlicht in accepted for publication in IEEE Transactions on Automatic Control
Beschreibung

In this paper, we present a nonlinear robust model predictive control (MPC) framework for general (state and input dependent) disturbances. This approach uses an online constructed tube in order to tighten the nominal (state and input) constraints. To facilitate an efficient online implementation, the shape of the tube is based on an offline computed incremental Lyapunov function with a corresponding (nonlinear) incrementally stabilizing feedback. Crucially, the online optimization only implicitly includes these nonlinear functions in terms of scalar bounds, which enables an efficient implementation. Furthermore, to account for an efficient evaluation of the worst case disturbance, a simple function is constructed offline that upper bounds the possible disturbance realizations in a neighbourhood of a given point of the open-loop trajectory. The resulting MPC scheme ensures robust constraint satisfaction and practical asymptotic stability with a moderate increase in the online computational demand compared to a nominal MPC. We demonstrate the applicability of the proposed framework in comparison to state of the art robust MPC approaches with a nonlinear benchmark example.

DOI 10.1109/TAC.2020.2982585