Robust Adaptive MPC Using Uncertainty Compensation
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866929515205230592 |
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| author | Tao, Ran Zhao, Pan Kolmanovsky, Ilya Hovakimyan, Naira |
| author_facet | Tao, Ran Zhao, Pan Kolmanovsky, Ilya Hovakimyan, Naira |
| contents | This paper presents an uncertainty compensation-based robust adaptive model predictive control (MPC) framework for linear systems with both matched and unmatched nonlinear uncertainties subject to both state and input constraints. In particular, the proposed control framework leverages an L1 adaptive controller (L1AC) to compensate for the matched uncertainties and to provide guaranteed uniform bounds on the error between the states and control inputs of the actual system and those of a nominal i.e., uncertainty-free, system. The performance bounds provided by the L1AC are then used to tighten the state and control constraints of the actual system, and a model predictive controller is designed for the nominal system with the tightened constraints. The proposed control framework, which we denote as uncertainty compensation-based MPC (UC-MPC), guarantees constraint satisfaction and achieves improved performance compared with existing methods. Simulation results on a flight control example demonstrate the benefits of the proposed framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_13743 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Robust Adaptive MPC Using Uncertainty Compensation Tao, Ran Zhao, Pan Kolmanovsky, Ilya Hovakimyan, Naira Systems and Control This paper presents an uncertainty compensation-based robust adaptive model predictive control (MPC) framework for linear systems with both matched and unmatched nonlinear uncertainties subject to both state and input constraints. In particular, the proposed control framework leverages an L1 adaptive controller (L1AC) to compensate for the matched uncertainties and to provide guaranteed uniform bounds on the error between the states and control inputs of the actual system and those of a nominal i.e., uncertainty-free, system. The performance bounds provided by the L1AC are then used to tighten the state and control constraints of the actual system, and a model predictive controller is designed for the nominal system with the tightened constraints. The proposed control framework, which we denote as uncertainty compensation-based MPC (UC-MPC), guarantees constraint satisfaction and achieves improved performance compared with existing methods. Simulation results on a flight control example demonstrate the benefits of the proposed framework. |
| title | Robust Adaptive MPC Using Uncertainty Compensation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2309.13743 |