Conformal Prediction-Based MPC for Stochastic Linear Systems
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914487679844352 |
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| author | Vogel, Lukas Carron, Andrea Vlahakis, Eleftherios E. Dimarogonas, Dimos V. |
| author_facet | Vogel, Lukas Carron, Andrea Vlahakis, Eleftherios E. Dimarogonas, Dimos V. |
| contents | We propose a stochastic model predictive control (MPC) framework for linear systems subject to joint-in-time chance constraints under unknown disturbance distributions. Unlike existing approaches that rely on parametric or Gaussian assumptions, or require expensive offline computation, the method uses conformal prediction to construct finite-sample confidence regions for the system's error trajectories with minimal computational effort. These probabilistic sets enable relaxation of the joint-in-time chance constraints into a deterministic closed-loop formulation based on indirect feedback, ensuring recursive feasibility and chance constraint satisfaction. Further, we extend to the output feedback setting and establish analogous guarantees from output measurements alone, given access to noise samples. Numerical examples demonstrate the effectiveness and advantages compared to existing approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_10738 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conformal Prediction-Based MPC for Stochastic Linear Systems Vogel, Lukas Carron, Andrea Vlahakis, Eleftherios E. Dimarogonas, Dimos V. Systems and Control Robotics We propose a stochastic model predictive control (MPC) framework for linear systems subject to joint-in-time chance constraints under unknown disturbance distributions. Unlike existing approaches that rely on parametric or Gaussian assumptions, or require expensive offline computation, the method uses conformal prediction to construct finite-sample confidence regions for the system's error trajectories with minimal computational effort. These probabilistic sets enable relaxation of the joint-in-time chance constraints into a deterministic closed-loop formulation based on indirect feedback, ensuring recursive feasibility and chance constraint satisfaction. Further, we extend to the output feedback setting and establish analogous guarantees from output measurements alone, given access to noise samples. Numerical examples demonstrate the effectiveness and advantages compared to existing approaches. |
| title | Conformal Prediction-Based MPC for Stochastic Linear Systems |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2512.10738 |