详细信息

An Interval Type-2 Fuzzy Controller Based on Data-Driven Parameters Extraction for Cement Calciner Process  ( SCI-EXPANDED收录 EI收录)  

文献类型:期刊文献

英文题名:An Interval Type-2 Fuzzy Controller Based on Data-Driven Parameters Extraction for Cement Calciner Process

作者:Zheng, Jinquan[1];Du, Wenli[1];Nascu, Ioana[1];Zhu, Yuanming[1];Zhong, Weimin[1]

机构:[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份:2020

卷号:8

起止页码:61775

外文期刊名:IEEE ACCESS

收录:;EI(收录号:20201608458170);WOS:【SCI-EXPANDED(收录号:WOS:000527413600002)】;

基金:This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0303403, in part by the National Science Fund for Distinguished Young Scholars under Grant 61725301, in part by the Programme of Introducing Talents of Discipline to Universities (111 Project) under Grant B17017, and in part by the Fundamental Research Funds for the Central Universities.

语种:英文

外文关键词:Calciner process control; interval type-2 fuzzy C-means cluster; interval type-2 fuzzy controller

摘要:The stable control of the calciner plant is vital for the clinker quality and energy consumption in the cement calcination process. However, traditional calciner control strategies cannot efficiently deal with complicated characteristics, such as the changes in the material component, process disturbances and uncertainty, and meanwhile cannot obtain knowledge from data. For these challenges, an intelligent control strategy based on an interval type-2 fuzzy logic controller (IT2FLC) is proposed by making full use of process data in this work. The feedback IT2FLC combines with feedforward IT2FLC, taking into account various disturbances. An improved interval type-2 fuzzy C-means (IT2FCM) clustering algorithm is used to extract membership functions and rules, taking into account process uncertainty. Finally, the proposed control strategy is applied to control a calciner simulation process with practical data. The results show that the proposed strategy has better performance than the type-1 fuzzy logic controller (T1FLC) and can better meet the demand for real-life applications.

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