Explicit Model Predictive Control for Large-Scale Systems via Model Reduction

In this paper we present a framework for achieving constrained optimal real-time control for large-scale systems with fast dynamics. The methodology uses the explicit solution of the model predictive control (MPC) problem combined with model reduction, in an output-feedback implementation. The explicit solution of the MPC problem leads to online MPC functionality without having to solve an optimization problem at each time step. Reduced-order models are derived using a goal-oriented, model-constrained optimization formulation that yields efficient models tailored to the control application at hand. The approach is illustrated on a challenging largescale flow problem that aims to control the shock position in a supersonic diffuser.