Relaxed dissipativity assumptions and a simplified algorithm for multiobjective MPC

Gabriele Eichfelder (), Lars Grüne (), Lisa Krügel () and Jonas Schießl ()
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Gabriele Eichfelder: Technische Universität Ilmenau
Lars Grüne: Universität Bayreuth
Lisa Krügel: Universität Bayreuth
Jonas Schießl: Universität Bayreuth

Computational Optimization and Applications, 2023, vol. 86, issue 3, No 10, 1116 pages

Abstract: Abstract We consider nonlinear model predictive control (MPC) with multiple competing cost functions. In each step of the scheme, a multiobjective optimal control problem with a nonlinear system and terminal conditions is solved. We propose an algorithm and give performance guarantees for the resulting MPC closed loop system. Thereby, we significantly simplify the assumptions made in the literature so far by assuming strict dissipativity and the existence of a compatible terminal cost for one of the competing objective functions only. We give conditions which ensure asymptotic stability of the closed loop and, what is more, obtain performance estimates for all cost criteria. Numerical simulations on various instances illustrate our findings. The proposed algorithm requires the selection of an efficient solution in each iteration, thus we examine several selection rules and their impact on the results. and we also examine numerically how different selection rules impact the results

Keywords: Multiobjective Model Predictive Control; Multiobjective Optimal Control (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10589-022-00398-4

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