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 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Ltd
01.11.2023
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| Schlagworte: | |
| ISSN: | 0005-1098, 1873-2836 |
| Online-Zugang: | Volltext |
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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. |
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| ISSN: | 0005-1098 1873-2836 |
| DOI: | 10.1016/j.automatica.2023.111279 |