Dealing with infeasibility in multi-parametric programming for application to explicit model predictive control

Motivated by explicit model predictive control, we address infeasibility in multi-parametric quadratic programming according to the exact penalty function approach, where some user-chosen parameter-dependent constraints are relaxed and the 1-norm of their violation is penalized in the cost function....

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Veröffentlicht in:Automatica (Oxford) Jg. 157; S. 111279
Hauptverfasser: Falsone, Alessandro, Bianchi, Federico, Prandini, Maria
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.11.2023
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ISSN:0005-1098, 1873-2836
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Zusammenfassung:Motivated by explicit model predictive control, we address infeasibility in multi-parametric quadratic programming according to the exact penalty function approach, where some user-chosen parameter-dependent constraints are relaxed and the 1-norm of their violation is penalized in the cost function. We characterize the relation between the resulting multi-parametric quadratic program and the original one and show that, as the penalty coefficient grows to infinity, the solution to the former provides a piecewise affine continuous function, which is an optimal solution for the latter over the feasibility region, while it minimizes the 1-norm of the relaxed constraints violation over the infeasibility region.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2023.111279