👨🎓 About Me
I am currently pursuing a Ph.D. in the School of Automation at Central South University under the supervision of Xiaofang Chen and Lihui Cen, where I also received my bachelor’s degree in Automation. I am currently a visiting Ph.D. student with the Optimization-Based Control Group at TU Ilmenau, under the supervision of Karl Worthmann and Jan Heiland.
My research interests lie at the intersection of control, optimization, and learning, with a particular focus on the Koopman operator framework and its applications in process control systems. If you share similar interests or would like to discuss research questions, please feel free to contact me 📬 at zhong.chen.csu@gmail.com, zhongchen@csu.edu.cn, or zhong.chen@tu-ilmenau.de.
📝 Publications
Applied Mathematics and Computation, vol. 470, article 128577, 2024.
- This paper develops an iterative Koopman MPC method for nonlinear systems with time-varying parameters. A neural network learns lifted coordinates from fixed-parameter data, while a recursive online update, accelerated by the matrix inversion lemma, adapts the Koopman model during closed-loop operation. Simulations on three nonlinear examples demonstrate accurate tracking and improved control performance.
International Journal of Robust and Nonlinear Control, vol. 36, no. 2, pp. 602–628, 2026.
- This paper studies approximation errors in Koopman-based MPC and represents the identified modeling errors as polytopic uncertainty. It develops two min–max robust MPC schemes: a worst-case infinite-horizon feedback design and an improved design that estimates the current model and optimizes the first control move separately. Recursive feasibility, closed-loop stability, and numerical effectiveness are established.
2024 IEEE 63rd Conference on Decision and Control (CDC), pp. 1461–1466, 2024.
- This paper models an unknown nonlinear system as a Koopman-based LPV system with data-driven polytopic uncertainty. A robust MPC controller is then formulated for worst-case conditions using linear matrix inequality constraints. The resulting design guarantees recursive feasibility and closed-loop stability, with numerical simulations validating the modeling and control approach.
📖 Education
- 2022.09–2027.06, Ph.D. student, School of Automation, Central South UniversityChangsha, China.
- 2026.01–2027.01, Visiting Ph.D. student, Optimization-Based Control Group, Institute of Mathematics, Technische Universität IlmenauIlmenau, Germany.
- 2018.06–2022.06, B.Eng. in Automation, School of Automation, Central South UniversityChangsha, China.
💻 Experience
Research Focus
Data-driven Koopman-based modeling, optimization, and model predictive control for nonlinear dynamical systems.
Project Experience
Modeling and Optimization for Irrigation Systems
2024–PresentResearcher · Technical Development
Developed data-driven models and designed optimization and scheduling strategies for cascade pumping stations.
Cigarette Digital Design and Material Optimization
2023–2025Researcher · Technical Development
Built neural-network-based models for smoke-component prediction and developed optimization-driven strategies for auxiliary material design.
Coal Conversion Process Optimization and Fault Diagnosis
2024–PresentResearcher
Designed optimization, control, and fault-diagnosis algorithms for coal chemical processes with the potential for industrial deployment.
Aircraft Data-Driven Control Systems
2023–2025Researcher · Simulation
Developed data-driven modeling and control methods for aircraft attitude systems.