Hirt, Sebastian ; Pfefferkorn, Maik ; Mesbah, Ali ; Findeisen, Rolf (2024)
Stability-informed Bayesian Optimization for MPC Cost Function Learning.
In: IFAC-PapersOnLine, 58 (18)
doi: 10.1016/j.ifacol.2024.09.032
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging. This work explores closed-loop learning for predictive control parameters under imperfect information while considering closed-loop stability. We employ constrained Bayesian optimization to learn a model predictive controller's (MPC) cost function parametrized as a feedforward neural network, optimizing closed-loop behavior as well as minimizing model-plant mismatch. Doing so offers a high degree of freedom and, thus, the opportunity for efficient and global optimization towards the desired and optimal closed-loop behavior. We extend this framework by stability constraints on the learned controller parameters, exploiting the optimal value function of the underlying MPC as a Lyapunov candidate. The effectiveness of the proposed approach is underlined in simulations, highlighting its performance and safety capabilities.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Hirt, Sebastian ; Pfefferkorn, Maik ; Mesbah, Ali ; Findeisen, Rolf |
| Art des Eintrags: | Bibliographie |
| Titel: | Stability-informed Bayesian Optimization for MPC Cost Function Learning |
| Sprache: | Englisch |
| Publikationsjahr: | 2024 |
| Verlag: | Elsevier |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | IFAC-PapersOnLine |
| Jahrgang/Volume einer Zeitschrift: | 58 |
| (Heft-)Nummer: | 18 |
| DOI: | 10.1016/j.ifacol.2024.09.032 |
| Kurzbeschreibung (Abstract): | Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging. This work explores closed-loop learning for predictive control parameters under imperfect information while considering closed-loop stability. We employ constrained Bayesian optimization to learn a model predictive controller's (MPC) cost function parametrized as a feedforward neural network, optimizing closed-loop behavior as well as minimizing model-plant mismatch. Doing so offers a high degree of freedom and, thus, the opportunity for efficient and global optimization towards the desired and optimal closed-loop behavior. We extend this framework by stability constraints on the learned controller parameters, exploiting the optimal value function of the underlying MPC as a Lyapunov candidate. The effectiveness of the proposed approach is underlined in simulations, highlighting its performance and safety capabilities. |
| Zusätzliche Informationen: | 8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024, Kyoto, Japan, 21.08.2024-24.08.2024 |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik und Mechatronik > Control and Cyber-Physical Systems (CCPS) DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 2761 LokoAssist – Nahtlose Integration von Assistenzsystemen für die natürliche Lokomotion des Menschen |
| Hinterlegungsdatum: | 06 Nov 2024 15:33 |
| Letzte Änderung: | 06 Nov 2024 15:33 |
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