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.