Economic data-enabled predictive control using machine learning
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909752555995136 |
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| author | Yan, Mingxue Zhang, Xuewen Zhang, Kaixiang Li, Zhaojian Yin, Xunyuan |
| author_facet | Yan, Mingxue Zhang, Xuewen Zhang, Kaixiang Li, Zhaojian Yin, Xunyuan |
| contents | In this paper, we propose a convex data-based economic predictive control method within the framework of data-enabled predictive control (DeePC). Specifically, we use a neural network to transform the system output into a new state space, where the nonlinear economic cost function of the underlying nonlinear system is approximated using a quadratic function expressed by the transformed output in the new state space. Both the neural network parameters and the coefficients of the quadratic function are learned from open-loop data of the system. Additionally, we reconstruct constrained output variables from the transformed output through learning an output reconstruction matrix; this way, the proposed economic DeePC can handle output constraints explicitly. The performance of the proposed method is evaluated via a case study in a simulated chemical process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07182 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Economic data-enabled predictive control using machine learning Yan, Mingxue Zhang, Xuewen Zhang, Kaixiang Li, Zhaojian Yin, Xunyuan Systems and Control In this paper, we propose a convex data-based economic predictive control method within the framework of data-enabled predictive control (DeePC). Specifically, we use a neural network to transform the system output into a new state space, where the nonlinear economic cost function of the underlying nonlinear system is approximated using a quadratic function expressed by the transformed output in the new state space. Both the neural network parameters and the coefficients of the quadratic function are learned from open-loop data of the system. Additionally, we reconstruct constrained output variables from the transformed output through learning an output reconstruction matrix; this way, the proposed economic DeePC can handle output constraints explicitly. The performance of the proposed method is evaluated via a case study in a simulated chemical process. |
| title | Economic data-enabled predictive control using machine learning |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2505.07182 |