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| Koopman operator-based stable economicmodel predictive control
for nonlinear systems |
| JieWu1,KeLi1,2,FeiLiu1 |
|
|
| (Key Laboratory of Advanced Process Control for Light
Industry (Ministry of Education), Institute of Automation,
Jiangnan University, Wuxi 214122, Jiangsu, China;School of Intelligent Manufacturing, Jiangnan University,
Wuxi 214122, Jiangsu, China) |
|
| 摘要: |
| This paper presents a data-driven economic model predictive control (EMPC) framework for nonlinear systems. Leveraging
Koopman operator theory and the extended dynamic mode decompositionmethod, a lifted linear model in the high-dimensional
function space of the nonlinear dynamics is first identified from the collected dataset. Then, an EMPC strategy used to optimize
process economics is designed in the lifted space, which employs the Koopman linear model as the predictor. To guarantee
closed-loop stability, an artificial constraint is constructed by solving a convex quadratic programming problem. The recursive
feasibility and closed-loop stability of the proposed approach are rigorously analyzed. Benefiting from the linear structure
of the Koopman model, the online computational burden of the EMPC is substantially reduced. The effectiveness of the
proposed method is demonstrated through simulations on a nonlinear chemical reactor. |
| 关键词: Closed-loop stability · Economic model predictive control · Koopman operator · Recursive feasibility |
| DOI：https://doi.org/10.1007/s11768-025-00298-y |
|
| 基金项目:This work was partially supported by the National Natural Science
Foundation of China (No. 61833007) and the 2025 Fundamental
Research Funds for the Central Universities (Youth Program
1332050205256400). |
|
| Koopman operator-based stable economicmodel predictive controlfor nonlinear systems |
| Jie Wu1,Ke Li1,2,Fei Liu1 |
| (Key Laboratory of Advanced Process Control for Light
Industry (Ministry of Education), Institute of Automation,
Jiangnan University, Wuxi 214122, Jiangsu, China;School of Intelligent Manufacturing, Jiangnan University,
Wuxi 214122, Jiangsu, China) |
| Abstract: |
| This paper presents a data-driven economic model predictive control (EMPC) framework for nonlinear systems. Leveraging
Koopman operator theory and the extended dynamic mode decompositionmethod, a lifted linear model in the high-dimensional
function space of the nonlinear dynamics is first identified from the collected dataset. Then, an EMPC strategy used to optimize
process economics is designed in the lifted space, which employs the Koopman linear model as the predictor. To guarantee
closed-loop stability, an artificial constraint is constructed by solving a convex quadratic programming problem. The recursive
feasibility and closed-loop stability of the proposed approach are rigorously analyzed. Benefiting from the linear structure
of the Koopman model, the online computational burden of the EMPC is substantially reduced. The effectiveness of the
proposed method is demonstrated through simulations on a nonlinear chemical reactor. |
| Key words: Closed-loop stability · Economic model predictive control · Koopman operator · Recursive feasibility |
