Work

Economic data-enabled predictive control using machine learning

Year: 2025

Type: article

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.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.07.116

Authors: Mingxue Yan, Xuewen Zhang, Kaixiang Zhang, Zhaojian Li, Xunyuan Yin

Institutions: Nanyang Technological University, Michigan State University

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funders: Ministry of Education - Singapore, Public Utilities Board - Singapore, Nanyang Technological University