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Iterative Learning Predictive Control for Batch Processes

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Iterative Learning Stabilization and Fault-Tolerant Control for Batch Processes

Abstract

Although robust ILC methods proposed in the previous chapters of this book can effectively improve the control performance [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19], the state of the system does not always change according to the solution of the control law in actual batch production processes. When the system state deviates from a given value, if the same control law is still used to control the system, the state deviation of the system may become larger and larger, and even have a serious impact on the stable operation and product performance.

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Author information

Authors and Affiliations

  1. College of Mathematics and Statistics, Hainan Normal University, Haikou, Hainan, China

    Dr. Limin Wang

  2. Hangzhou Dianzi University, Hangzhou, Zhejiang, China

    Prof. Ridong Zhang

  3. Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Hongkong, China

    Prof. Furong Gao

Authors
  1. Dr. Limin Wang
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  2. Prof. Ridong Zhang
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  3. Prof. Furong Gao
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Corresponding author

Correspondence to Limin Wang .

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Wang, L., Zhang, R., Gao, F. (2020). Iterative Learning Predictive Control for Batch Processes. In: Iterative Learning Stabilization and Fault-Tolerant Control for Batch Processes. Springer, Singapore. https://doi.org/10.1007/978-981-13-5790-9_6

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