文献类型：期刊文献

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

作者：Sun, Jing-Gao[1];Chen, Xian-Feng[1];Su, Guang-Hao[1];Wang, Meng[1];Pan, Hong-Guang[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Xian Unvers Sci & Technol, Coll Elect & Control Engn, Xian 710049, Shanxi, Peoples R China

年份：2022

卷号：20

期号：10

起止页码：3187

外文期刊名：INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS

收录：;EI(收录号：20224012842252);WOS:【SCI-EXPANDED(收录号:WOS:000862427400007)】；

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

语种：英文

外文关键词：Bayesian probability weight; MSNMPC; OCFE; online scenario update; semi-batch polymerization

摘要：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.