硕士生导师
教师拼音名称:zhangfangkun
电子邮箱:
入职时间:2019-09-02
学历:博士研究生
性别:男
联系方式:18554911864
学位:工学博士
毕业院校:大连理工大学
学科:化学工程
控制理论与控制工程最后更新时间:..
关键字:OPTIMIZATION; OPERATION; DESIGN; GA
摘要:Real-time measurement and control of product composition in extractive distillation continues to remain a challenge; however, composition control has demonstrated the ability to stabilise control systems. In this study, a dynamic control scheme is proposed that uses an improved PID-fused adaptive neuro-fuzzy inference system (ANFIS); herein, ANFIS replaces composition controllers, improving the real-time performance of the control system. The ANFIS algorithm, however, suffers from problems such as easily falling into a local optimum solution. Therefore, the genetic algorithm (GA) and particle swarm optimisation (PSO) algorithm are separately used to optimise the network parameters of ANFIS, and the impact of different number of clusters (NC) on the performance of the control scheme is evaluated. The control scheme of PSO-optimised PID-ANFIS exhibits superior dynamic performance compared to other control schemes; moreover, it does not require the use of component controllers and has a faster response time and smaller transient values.(c) 2023 Elsevier Ltd. All rights reserved.
卷号:269
期号:-
是否译文:否