Learning Approximate Semi-Explicit Hybrid MPC with an Application to Microgrids

We present a semi-explicit formulation of model predictive controllers for hybrid systems with feasibility guarantees. The key idea is to use a machine-learning approach to learn a compact predictor of the integer/binary components of optimal solutions of the multiparametric mixed-integer linear optimization problem associated with the controller, so that, on-line, only a linear programming problem must be solved. In this scheme, feasibility is ensured by a simple rule-based engine that corrects the binary configuration only when necessary. The performance of the approach is assessed on a well known benchmark for which explicit controllers based on domain-specific knowledge are already available. Simulation results show how our proposed method considerably lowers computation time without deteriorating closed-loop performance.