文献类型：期刊文献

英文题名：Data-driven adaptive predictive control of hydrocracking process using a covariance matrix adaption evolution strategy

作者：Li, Zhongmei[1];Wang, Xinjie[1];Du, Wenli[1];Yang, Minglei[1];Li, Zhi[1];Liao, Peizhi[1]

机构：[1]East China Univ Sci & Technol, Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2022

卷号：125

外文期刊名：CONTROL ENGINEERING PRACTICE

收录：;EI(收录号：20222412213717);WOS:【SCI-EXPANDED(收录号:WOS:000811070600001)】；

基金：This work was funded in part by National Natural Science Fund for Distinguished Young Scholars (61725301) , National Natural Science Foundation of China (62136003, 62003140) , the Shanghai Sailing Program (20YF1411000) , Shanghai Pujiang Program (20PJ1403000) and Shanghai AI Lab.

语种：英文

外文关键词：Hydrocracking process; Temperature control; Covariance matrix adaption evolution strategy; Input constraint

摘要：In this paper, a new data-driven adaptive predictive control architecture to the reactor temperature control problem in hydrocracking process is proposed with completely unknown system dynamics. Specifically, the nonlinear relationship between the reactor temperature and conversion is established using echo state networks (ESNs) with an intrinsic plasticity rule, and a covariance matrix adaption evolution strategy (CMA-ES) is used to optimize the reactor temperature iteratively using the input and state information. Besides, the prescribed performance function is introduced to guarantee the transient and steady performance of the system. In addition, the input constraints and external disturbance are simultaneously considered. Numerical simulation results and industrial application prove the efficacy of the proposed method.