复杂系统建模与控制
承担的主要项目
[1] 重庆市教委科学技术研究项目，变工况批次过程的复合迭代学习控制方法研究，2024.10-2027.09，主持。
[2] 企业横向，矿用光储电源系统关键技术研究，2024.01-2027.12，主持。
[3] 国家自然科学基金面上项目，跨域综合智慧能源系统可变拓扑状态下的多网群智能决策研究，2024.11-2027.10，主研。
[4] 国家自然科学基金面上项目，不确定性间歇过程的二维实时优化控制方法研究，2018.01-2021.12，主研。
[5] 国家自然科学基金面上项目，复合切换系统的稳定性分析、符号模型构建及其互联网络的组合式设计，2022.01-2025.12，主研。
代表性成果
[1] Chengyu Zhou, Li Jia, Jianfang Li, Yan Chen. Data-driven two-dimensional integrated control for nonlinear batch processes[J]. Journal of Process Control, 2024, 135: 103160.
[2] Chengyu Zhou, Li Jia, Jianfang Li. Improved iterative learning model predictive control for nonlinear batch processes[J]. International Journal of Robust and Nonlinear Control, 2024.
[3] Chengyu Zhou, Li Jia, Yang Zhou. A two-stage robust iterative learning model predictive control for batch processes[J]. ISA transactions, 2023, 135: 309-324.
[4] Chengyu Zhou, Li Jia, Yang Zhou. Tube-based iterative-learning-model predictive control for batch processes using pre-clustered just-in-time learning methodology[J]. Chemical Engineering Science, 2022, 259: 117802.
[5] Chengyu Zhou, Li Jia. A local dynamic extreme learning machine based iterative learning control of nonlinear batch process[J]. Optimal Control Applications and Methods, 2022, 43(1): 257-282.
[6] Chengyu Zhou, Li Jia, Yang Zhou. High‐order iterative learning model predictive control for batch chemical processes[J]. The Canadian Journal of Chemical Engineering, 2023, 101(12): 6995-7014.
[7] Chengyu Zhou, Li Jia, Yang Zhou. Tube‐based batch model predictive control for polystyrene polymerization reaction process[J]. Asia‐Pacific Journal of Chemical Engineering, 2023, 18(4): e2906.
[8] Chengyu Zhou, Li Jia. A just-in-time learning based integrated IMC-ILC control strategy for batch processes[C]//2021 40th Chinese Control Conference (CCC). IEEE, 2021: 2580-2585.
[9] 周成宇, 杨鑫. 自适应JITL-PID控制器设计方法（英文）[J].计算机与应用化学, 2017, 34(10):796-801.
[10] 杨鑫, 周成宇. 即时学习法在过程工业中的应用研究进展[J].计算机与应用化学,2018,35(09):746-758.
[11] Jianfang Li, Li Jia, Chengyu Zhou. Probability density function based adaptive ensemble learning with global convergence for wind power prediction[J]. Energy, 2024: 133573.
[12] Wei Yang, Li Jia, Chengyu Zhou. Dynamic just-in-time learning based model predictive control for variable pitch wind energy conversion system[J]. Journal of Renewable and Sustainable Energy, 2022, 14(6).
[13] Rui Hou, Li Jia, Xuhui Bu, Chengyu Zhou. Dynamic neural network predictive compensation-based point-to-point iterative learning control with nonuniform batch length[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 35(9): 13005 - 13016.
[14] Benshen Ly, Li Jia, Feng Li, Chengyu Zhou. Identification of multivariable Hammerstein CARMA system using special test signals[J]. Journal of Dynamic Systems, Measurement, and Control, 2022, 144(12): 121001.
