Data-Driven Min-Max MPC for LPV Systems with Unknown Scheduling Signal
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912111527985152 |
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| author | Xie, Yifan Berberich, Julian Brändle, Felix Allgöwer, Frank |
| author_facet | Xie, Yifan Berberich, Julian Brändle, Felix Allgöwer, Frank |
| contents | This paper presents a data-driven min-max model predictive control (MPC) scheme for linear parameter-varying (LPV) systems. Contrary to existing data-driven LPV control approaches, we assume that the scheduling signal is unknown during offline data collection and online system operation. Assuming a quadratic matrix inequality (QMI) description for the scheduling signal, we develop a novel data-driven characterization of the consistent system matrices using only input-state data. The proposed data-driven min-max MPC minimizes a tractable upper bound on the worst-case cost over the consistent system matrices set and over all scheduling signals satisfying the QMI. The proposed approach guarantees recursive feasibility, closed-loop exponential stability and constraint satisfaction if it is feasible at the initial time. We demonstrate the effectiveness of the proposed method in simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05624 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Driven Min-Max MPC for LPV Systems with Unknown Scheduling Signal Xie, Yifan Berberich, Julian Brändle, Felix Allgöwer, Frank Systems and Control This paper presents a data-driven min-max model predictive control (MPC) scheme for linear parameter-varying (LPV) systems. Contrary to existing data-driven LPV control approaches, we assume that the scheduling signal is unknown during offline data collection and online system operation. Assuming a quadratic matrix inequality (QMI) description for the scheduling signal, we develop a novel data-driven characterization of the consistent system matrices using only input-state data. The proposed data-driven min-max MPC minimizes a tractable upper bound on the worst-case cost over the consistent system matrices set and over all scheduling signals satisfying the QMI. The proposed approach guarantees recursive feasibility, closed-loop exponential stability and constraint satisfaction if it is feasible at the initial time. We demonstrate the effectiveness of the proposed method in simulation. |
| title | Data-Driven Min-Max MPC for LPV Systems with Unknown Scheduling Signal |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2411.05624 |