Stability of model predictive control based on reduced-order models

— In this paper, we present a systematic procedure for obtaining closed-loop stable output-feedback model predictive control based on reduced-order models. The design uses linear state estimators, and applies to open-loop stable systems with hard input- and soft state constraints. Robustness against the model reduction error is obtained by choosing the cost function parameters so as to satisfy a linear matrix inequality condition. We also show by means of an example, that performance is maintained even when the model reduction error is relatively large.

• Type: Conference
• Year: 2008
• Where: CDC
• Authors: Svein Hovland, Christian Løvaas, Jan Tommy Gravdahl, Graham C. Goodwin
• Keywords: CDC 2008, Control Systems, Linear State Estimators, Model Reduction Error, Stable Output-feedback Model