arXiv subjects

Mingxue Yan

Publications and source records attributed to Mingxue Yan.

3 recordsLinked to original sources

arXiv2025

Learning-based data-enabled economic predictive control with convex optimization for nonlinear systems

In this article, we propose a data-enabled economic predictive control method for a class of nonlinear systems, which aims to optimize the economic operational performance while handling hard constraints on the system outputs. Two lifting functions are constructed via training neural networks, which generate mapped input and mapped output in a higher-dimensional space, where the nonlinear economic cost function can be approximated using a quadratic function of the mapped variables. The data-enabled predictive control framework is extended to address nonlinear dynamics by using the mapped input and the mapped output that belong to a virtual linear representation, which serves as an approximation of the original nonlinear system. Additionally, we reconstruct the system output variables from the mapped output, on which hard output constraints are imposed. The online control problem is formulated as a convex optimization problem, despite the nonlinearity of the system dynamics and the original economic cost function. Theoretical analysis is presented to justify the suitability of the proposed method for nonlinear systems. We evaluate the proposed method through two large-scale industrial case studies: (i) a biological water treatment process, and (ii) a solvent-based shipboard post-combustion carbon capture process. These studies demonstrate its effectiveness and advantages.

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arXiv2025

Economic data-enabled predictive control using machine learning

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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arXiv2024

Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling

In this paper, we propose a physics-informed learning-based Koopman modeling approach and present a Koopman-based self-tuning moving horizon estimation design for a class of nonlinear systems. Specifically, we train Koopman operators and two neural networks - the state lifting network and the noise characterization network - using both data and available physical information. The two neural networks account for the nonlinear lifting functions for Koopman modeling and describing system noise distributions, respectively. Accordingly, a stochastic linear Koopman model is established in the lifted space to forecast the dynamic behavior of the nonlinear system. Based on the Koopman model, a self-tuning linear moving horizon estimation (MHE) scheme is developed. The weighting matrices of the MHE design are updated using the pre-trained noise characterization network at each sampling instant. The proposed estimation scheme is computationally efficient because only convex optimization is involved during online implementation, and updating the weighting matrices of the MHE scheme does not require re-training the neural networks. We verify the effectiveness and evaluate the performance of the proposed method via the application to a simulated chemical process.

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