文献类型：期刊文献

英文题名：Naive Bayesian Classification Based Performance Assessment of Cascade Control System

作者：Sun, Jinggao[1]; Chen, Jialin[1]; Song, Bing[1]; Su, Guanghao[1]; Zhang, Chenyang[1]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

年份：2021

起止页码：2426

外文期刊名：Proceeding - 2021 China Automation Congress, CAC 2021

收录：EI(收录号：20221611968579)

语种：英文

外文关键词：Computation theory - Process control

摘要：Cascade control is a commonly used control strategy in chemical engineering processes. Compared with the single loop configuration, cascade control has obvious advantages in improving dynamic response performance and anti-interference. Although the technique of single loop performance assessment based on minimum variance theory is relatively mature, the research concerning cascade loop performance assessment is still rarely. Aiming at overcoming the drawback of excessive manipulator, weak robustness and huge computation burden caused by minimum variance performance index, this paper innovatively proposes a method of cascade control system performance assessment based on Naive Bayesian Classification(NBC), which owns the ability of predicting the performance of the control system in advance. Based on the above work, identification and retuning process of the unreasonable parameters are employed afterwards to further enhances the control quality of industry process. Finally, the feasibility and effectiveness of the proposed method are demonstrated by simulation experiments. ? 2021 IEEE