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arXiv · 2505.07182

Economic data-enabled predictive control using machine learning

Mingxue Yan·Xuewen Zhang·Kaixiang Zhang·Zhaojian Li·Xunyuan Yin
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Abstract

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.

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Mingxue Yan, Xuewen Zhang, Kaixiang Zhang, Zhaojian Li, Xunyuan Yin. 2025-05-12. Economic data-enabled predictive control using machine learning. https://doi.org/10.1016/j.ifacol.2025.07.116

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