110th Anniversary: An Overview on Learning-Based Model Predictive Control for Batch Processes

Author:

Lu Jingyi1,Cao Zhixing23ORCID,Zhao Chunhui4ORCID,Gao Furong1ORCID

Affiliation:

1. Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong

2. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

3. Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China

4. School of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Funder

Research Grants Council, University Grants Committee

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

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1. Cross-coupling indirect iterative learning control method for batch processes with time-varying uncertainties;Systems Science & Control Engineering;2023-12-19

2. Deep Koopman-VAE Based Process Monitoring for Industrial Biosystems;2023 IEEE 3rd International Conference on Data Science and Computer Application (ICDSCA);2023-10-27

3. Smart batch process: The evolution from 1D and 2D to new 3D perspectives in the era of Big Data;Journal of Process Control;2023-10

4. Iterative Learning Model Predictive Control for Thermal Management of Urban Air Mobility Vehicles*;2023 IEEE Conference on Control Technology and Applications (CCTA);2023-08-16

5. Intelligent Modeling for Batch Polymerization Reactors with Unknown Inputs;Sensors;2023-06-29

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