Learning Approximate Semi-Explicit Hybrid MPC with an Application to Microgrids. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Masti, Daniele; Pippia, Tomas; Bemporad, Alberto; Schutter, Bart De
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 5207
• Page End: 5212
• Publication Date: 2020
• Subjects: Model Predictive Control -- Machine Learning -- Mixed-Integer Optimization -- Modeling -- Simulation of Power Systems; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1192
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.