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Multi-stage Nonlinear Model Predictive Control with Online Scenario Update for Semi-batch Polymerization Processes

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  • Control Theory and Applications
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Abstract

In this paper, the problem of multi-stage nonlinear model predictive control with scenario update is investigated for semi-batch polymerization processes. The objective is to propose novel online scenario update schemes such that the more reasonable scenario tree can be generated. Firstly, based on the Orthogonal Configuration of Finite Elements (OCFE) method of direct radau configuration, the dynamic optimization problems are converted to Nonlinear Programing (NLP) problems such that the speed and accuracy of real-time optimization problem solving are effectively improved. Then, the scenario deviation is calculated based on model prediction information of each scenario and process measurement information. After that, calculate the bayesian probability weight of corresponding scenario is obtained. The online scenario reduction scheme uses the weight information update scenarios gradually reduce the scope of scenario tree representation. The online scenario weight update scheme uses the weight information as the basis for weight assignment of each scenario in the optimization problem. They use different methods to make the scenario tree modeling approach the real realization of uncertainty, and reduce the conservativeness compared with the traditional MSNMPC fixed scenario tree method. Through multiple batches numerical simulations of a semi-batch polymerization process, the advantages and effectiveness of the two proposed schemes are verified.

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Authors and Affiliations

  1. School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

    Jing-Gao Sun, Xian-Feng Chen, Guang-Hao Su & Meng Wang

  2. College of Electrical and Control Engineering, Xi’an Unversity of Science and Technology, Shanxi, 710049, China

    Hong-Guang Pan

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  1. Jing-Gao Sun
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  3. Guang-Hao Su
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Correspondence to Jing-Gao Sun.

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This work was supported in part by National Natural Science Foundation of China (No.62003139), and the Natural Science Foundation of Shanghai (No. 20ZR1415200).

Jing-Gao Sun received his Ph.D. degree in control science and control engineering from the School of Information Science and engineering, East China University of Science and Technology, Shanghai, China, in 2003. From July 2014 to January 2015, he was a visiting scholar in Lehigh University, USA. Now, he is an associate professor at East China University of Science and Technology. His research interests include nonlinear model predictive control, active disturbance rejection control, modeling, and optimal control of complex chemical process.

Xian-Feng Chen received his B.S. degree in automation from Wuhan Institute of Technology, Wuhan, China, in 2018. He is currently pursuing an M.S. degree with the College of East China University of Science and Technology, Shanghai, China. His current research interests include nonlinear model predictive control and complex process system control.

Guang-Hao Su received his B.S. degree in measurement and control technology and instrumentation program from Northeastern University, Qinhuangdao, China, in 2019. He is currently pursuing a Master’s degree with East China University of Science and Technology, Shanghai, China. His current research interests include data-driven control, anti-windup compensator synthesis, and disturbance rejection control.

Meng Wang received his Ph.D. degree in the Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, in 2018. He is currently an Associate Professor with School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China. His research interests include robust control and filtering, and fuzzy systems and control.

Hong-Guang Pan received his Ph.D. degree from Xi’an Jiaotong University, Xi’an, China, in 2015. From September 2013 to March 2015, he was a visiting Ph.D. student in Lehigh University, USA. Now he is an associate professor at College of Electrical and Control Engineering, Xi’an Unversity of Science and Technology. His research interests include brain-machine interface.

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Sun, JG., Chen, XF., Su, GH. et al. Multi-stage Nonlinear Model Predictive Control with Online Scenario Update for Semi-batch Polymerization Processes. Int. J. Control Autom. Syst. 20, 3187–3197 (2022). https://doi.org/10.1007/s12555-020-0737-6

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