Multi-objective robust model predictive control using Game Theory

Hajiloo, A.; Xie, Wenfang; Ren, Xuemei

doi:10.1109/icinfa.2015.7279622

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Lars Grüne and Marleen Stieler1

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Cited by 3 publications

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“…In [11] the occuring MO optimization problem is interpreted as a game and solved by means of the Nash-bargaining framework.…”

Section: Lars Grüne and Marleen Stielermentioning

confidence: 99%

Multiobjective model predictive control for stabilizing cost criteria

Grüne¹,

Stieler²

2019

Discrete &Amp; Continuous Dynamical Systems - B

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In this paper we demonstrate how multiobjective optimal control problems can be solved by means of model predictive control. For our analysis we restrict ourselves to finite-dimensional control systems in discrete time. We show that convergence of the MPC closed-loop trajectory as well as upper bounds on the closed-loop performance for all objectives can be established if the 'right' Pareto-optimal control sequence is chosen in the iterations. It turns out that approximating the whole Pareto front is not necessary for that choice. Moreover, we provide statements on the relation of the MPC performance to the values of Pareto-optimal solutions on the infinite horizon, i.e. we investigate on the inifinite-horizon optimality of our MPC controller.

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“…In [11] the occuring MO optimization problem is interpreted as a game and solved by means of the Nash-bargaining framework.…”

Section: Lars Grüne and Marleen Stielermentioning

confidence: 99%