Learning approximate semi-explicit hybrid MPC with an application to microgrids


Reference:

D. Masti, T. Pippia, A. Bemporad, and B. De Schutter, "Learning approximate semi-explicit hybrid MPC with an application to microgrids," Proceedings of the 21st IFAC World Congress, Virtual conference, pp. 5207-5212, July 2020.

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.

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Bibtex entry:

@inproceedings{MasPip:20-011,
author={D. Masti and T. Pippia and A. Bemporad and B. {D}e Schutter},
title={Learning approximate semi-explicit hybrid {MPC} with an application to microgrids},
booktitle={Proceedings of the 21st IFAC World Congress},
address={Virtual conference},
pages={5207--5212},
month=jul,
year={2020},
doi={10.1016/j.ifacol.2020.12.1192}
}


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