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Design, Implementation and Simulation of an MPC algorithm for Switched Nonlinear Systems under Combinatorial Constraints

Adrian Bürger (adrian.buerger***at***hs-karlsruhe.de)
Clemens Zeile (clemens.zeile***at***ovgu.de)
Angelika Altmann-Dieses (angelika.altmann-dieses***at***hs-karlsruhe.de)
Sebastian Sager (sager***at***ovgu.de)
Moritz Diehl (moritz.diehl***at***imtek.uni-freiburg.de)

Abstract: Within this work, we present a warm-started algorithm for Model Predictive Control (MPC) of switched nonlinear systems under combinatorial constraints based on Combinatorial Integral Approximation (CIA). To facilitate high-speed solutions, we introduce a preprocessing step for complexity reduction of CIA problems, and include this approach within a new toolbox for solution of CIA problems with special focus on MPC. The proposed algorithm is implemented and utilized within an MPC simulation study for a solar thermal climate system with nonlinear system behavior and uncertain operation conditions. The results are analyzed in terms of solution quality, constraint satisfaction and runtime of the solution steps, showing the applicability of the proposed algorithm and implementations.

Keywords: Model predictive control, switched dynamic systems, mixed-integer nonlinear programming, optimal control, approximation methods and heuristics

Category 1: Nonlinear Optimization (Systems governed by Differential Equations Optimization )

Category 2: Integer Programming ((Mixed) Integer Nonlinear Programming )

Citation: Journal of Process Control 81, pp.15-30, 2019. DOI 10.1016/j.jprocont.2019.05.016 Article link https://www.sciencedirect.com/science/article/pii/S0959152419303592

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Entry Submitted: 07/13/2018
Entry Accepted: 07/13/2018
Entry Last Modified: 11/16/2020

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