Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2503.16076 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909545649930240 |
|---|---|
| author | Houska, Boris Müller, Matthias A. Villanueva, Mario E. |
| author_facet | Houska, Boris Müller, Matthias A. Villanueva, Mario E. |
| contents | This paper proposes novel approaches for designing control Lyapunov functions (CLFs) for constrained linear systems. We leverage recent configuration-constrained polyhedral computing techniques to devise piecewise affine convex CLFs. Additionally, we generalize these methods to uncertain systems with both additive and multiplicative disturbances. The proposed design methods are capable of approximating the infinite horizon cost function of both nominal and min-max optimal control problems by solving a single, one-stage, convex optimization problem. As such, these methods find practical applications in explicit controller design as well as in determining terminal regions and cost functions for nominal and min-max model predictive control (MPC). Numerical examples illustrate the effectiveness of this approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16076 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Control Lyapunov Function Design via Configuration-Constrained Polyhedral Computing Houska, Boris Müller, Matthias A. Villanueva, Mario E. Optimization and Control Systems and Control This paper proposes novel approaches for designing control Lyapunov functions (CLFs) for constrained linear systems. We leverage recent configuration-constrained polyhedral computing techniques to devise piecewise affine convex CLFs. Additionally, we generalize these methods to uncertain systems with both additive and multiplicative disturbances. The proposed design methods are capable of approximating the infinite horizon cost function of both nominal and min-max optimal control problems by solving a single, one-stage, convex optimization problem. As such, these methods find practical applications in explicit controller design as well as in determining terminal regions and cost functions for nominal and min-max model predictive control (MPC). Numerical examples illustrate the effectiveness of this approach. |
| title | Control Lyapunov Function Design via Configuration-Constrained Polyhedral Computing |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2503.16076 |