A Stochastic Tube-Based MPC Framework with Hard Input Constraints
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910030195851264 |
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| author | Karam, Carlo Tacchi, Matteo Fiacchini, Mirko |
| author_facet | Karam, Carlo Tacchi, Matteo Fiacchini, Mirko |
| contents | This work presents a stochastic tube-based model predictive control framework that guarantees hard input constraint satisfaction for linear systems subject to unbounded additive disturbances. The approach relies on a structured design of probabilistic reachable sets that explicitly incorporates actuator saturation into the error dynamics and bounds the resulting nonlinearity within a convex embedding. The proposed controller retains the computational efficiency and structural advantages of stochastic tube-based approaches while ensuring state chance constraint satisfaction alongside hard input limits. Recursive feasibility and mean-square stability are established for our scheme, and a numerical example illustrates its effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19867 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Stochastic Tube-Based MPC Framework with Hard Input Constraints Karam, Carlo Tacchi, Matteo Fiacchini, Mirko Systems and Control This work presents a stochastic tube-based model predictive control framework that guarantees hard input constraint satisfaction for linear systems subject to unbounded additive disturbances. The approach relies on a structured design of probabilistic reachable sets that explicitly incorporates actuator saturation into the error dynamics and bounds the resulting nonlinearity within a convex embedding. The proposed controller retains the computational efficiency and structural advantages of stochastic tube-based approaches while ensuring state chance constraint satisfaction alongside hard input limits. Recursive feasibility and mean-square stability are established for our scheme, and a numerical example illustrates its effectiveness. |
| title | A Stochastic Tube-Based MPC Framework with Hard Input Constraints |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2602.19867 |