Data-driven Koopman MPC using Mixed Stochastic-Deterministic Tubes
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908609825210368 |
|---|---|
| author | Zhong, Zhengang del Rio-Chanona, Ehecatl Antonio Petsagkourakis, Panagiotis |
| author_facet | Zhong, Zhengang del Rio-Chanona, Ehecatl Antonio Petsagkourakis, Panagiotis |
| contents | This paper presents a novel data-driven stochastic MPC design for discrete-time nonlinear systems with additive disturbances by leveraging the Koopman operator and a distributionally robust optimization (DRO) framework. By lifting the dynamical system into a linear space, we achieve a finite-dimensional approximation of the Koopman operator. We explicitly account for the modeling approximation and additive disturbance error by a mixed stochastic-deterministic tube for the lifted linear model. This ensures the regulation of the original nonlinear system while complying with the prespecified constraints. Stochastic and deterministic tubes are constructed using a DRO and a hyper-cube hull, respectively. We provide finite sample error bounds for both types of tubes. The effectiveness of the proposed approach is demonstrated through numerical simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21308 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven Koopman MPC using Mixed Stochastic-Deterministic Tubes Zhong, Zhengang del Rio-Chanona, Ehecatl Antonio Petsagkourakis, Panagiotis Systems and Control This paper presents a novel data-driven stochastic MPC design for discrete-time nonlinear systems with additive disturbances by leveraging the Koopman operator and a distributionally robust optimization (DRO) framework. By lifting the dynamical system into a linear space, we achieve a finite-dimensional approximation of the Koopman operator. We explicitly account for the modeling approximation and additive disturbance error by a mixed stochastic-deterministic tube for the lifted linear model. This ensures the regulation of the original nonlinear system while complying with the prespecified constraints. Stochastic and deterministic tubes are constructed using a DRO and a hyper-cube hull, respectively. We provide finite sample error bounds for both types of tubes. The effectiveness of the proposed approach is demonstrated through numerical simulations. |
| title | Data-driven Koopman MPC using Mixed Stochastic-Deterministic Tubes |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.21308 |