A distributed framework for linear adaptive MPC
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866914755597303808 |
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| author | Parsi, Anilkumar Aboudonia, Ahmed Iannelli, Andrea Lygeros, John Smith, Roy S. |
| author_facet | Parsi, Anilkumar Aboudonia, Ahmed Iannelli, Andrea Lygeros, John Smith, Roy S. |
| contents | Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation. In this paper, a distributed version is proposed to deal with network systems featuring multiple agents and limited communication. To solve the problem in a distributed manner, structure is imposed on the control design ingredients without sacrificing performance. Decentralized and distributed adaptation schemes that allow for a reduction of the uncertainty online compatibly with the network topology are also proposed. The algorithm ensures robust constraint satisfaction, recursive feasibility and finite gain $\ell_2$ stability, and yields lower closed-loop cost compared to robust distributed MPC in simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_05777 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | A distributed framework for linear adaptive MPC Parsi, Anilkumar Aboudonia, Ahmed Iannelli, Andrea Lygeros, John Smith, Roy S. Systems and Control Optimization and Control Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation. In this paper, a distributed version is proposed to deal with network systems featuring multiple agents and limited communication. To solve the problem in a distributed manner, structure is imposed on the control design ingredients without sacrificing performance. Decentralized and distributed adaptation schemes that allow for a reduction of the uncertainty online compatibly with the network topology are also proposed. The algorithm ensures robust constraint satisfaction, recursive feasibility and finite gain $\ell_2$ stability, and yields lower closed-loop cost compared to robust distributed MPC in simulations. |
| title | A distributed framework for linear adaptive MPC |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2109.05777 |