Data-driven $H_{\infty}$ predictive control for constrained systems: a Lagrange duality approach
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913737416376320 |
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| author | Wu, Wenhuang Guo, Lulu Li, Nan Chen, Hong |
| author_facet | Wu, Wenhuang Guo, Lulu Li, Nan Chen, Hong |
| contents | This article proposes a data-driven $H_{\infty}$ control scheme for time-domain constrained systems based on model predictive control formulation. The scheme combines $H_{\infty}$ control and minimax model predictive control, enabling more effective handling of external disturbances and time-domain constraints. First, by leveraging input-output-disturbance data, the scheme ensures $H_{\infty}$ performance of the closed-loop system. Then, a minimax optimization problem is converted into a more manageable minimization problem employing Lagrange duality, which reduces conservatism typically associated with ellipsoidal evaluations of time-domain constraints. The study examines key closed-loop properties, including stability, disturbance attenuation, and constraint satisfaction, achieved by the proposed data-driven moving horizon predictive control algorithm. The effectiveness and advantages of the proposed method are demonstrated through numerical simulations involving a batch reactor system, confirming its robustness and feasibility under noisy conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18831 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-driven $H_{\infty}$ predictive control for constrained systems: a Lagrange duality approach Wu, Wenhuang Guo, Lulu Li, Nan Chen, Hong Optimization and Control Systems and Control This article proposes a data-driven $H_{\infty}$ control scheme for time-domain constrained systems based on model predictive control formulation. The scheme combines $H_{\infty}$ control and minimax model predictive control, enabling more effective handling of external disturbances and time-domain constraints. First, by leveraging input-output-disturbance data, the scheme ensures $H_{\infty}$ performance of the closed-loop system. Then, a minimax optimization problem is converted into a more manageable minimization problem employing Lagrange duality, which reduces conservatism typically associated with ellipsoidal evaluations of time-domain constraints. The study examines key closed-loop properties, including stability, disturbance attenuation, and constraint satisfaction, achieved by the proposed data-driven moving horizon predictive control algorithm. The effectiveness and advantages of the proposed method are demonstrated through numerical simulations involving a batch reactor system, confirming its robustness and feasibility under noisy conditions. |
| title | Data-driven $H_{\infty}$ predictive control for constrained systems: a Lagrange duality approach |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2412.18831 |