Schildbach, Georg
Zeilinger, Melanie Nicole
Morari, Manfred
Jones, Colin
Input-to-state stabilization of feasible model predictive controllers for linear systems
Proceedings of the IEEE Conference on Decision & Control
Proceedings of the IEEE Conference on Decision & Control
Proceedings of the IEEE Conference on Decision & Control
Proceedings of the IEEE Conference on Decision & Control
2011
2011
Research on sub-optimal Model Predictive Control (MPC) has led to a variety of optimization methods based on explicit or online approaches, or combinations thereof. Its foremost aim is to guarantee essential controller properties, i.e. recursive feasibility, stability, and robustness, at reduced and predictable computational cost, i.e. computation time and storage space. This paper shows how the input sequence of any (not necessarily stabilizing) sub-optimal controller and the shifted input sequence from the previous time step can be used in an optimal convex combination, which is easy to determine online, in order to guarantee input-to-state stability for the closed-loop system. The presented method is thus able to stabilize a wide range of existing sub-optimal MPC schemes that lack a formal stability guarantee, if they can be considered as a continuous map from the state space to the space of feasible input sequences.
Proceedings of the IEEE Conference on Decision & Control
Conference Papers