文献类型：会议论文

英文题名：Research and application on GA-based two-stage fuzzy temperature control system for a type of industrial furnace

作者：Peng, Xiaohong[1]; Mo, Zhi[2]; Xiao, Laisheng[1]

机构：[1] College of Information Technology, Guangdong Ocean University, Zhanjiang, China; [2] Technology Department, Zhanjiang New Zhongmei Chemical Industries Co. Ltd., Zhanjiang, China

会议论文集：Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010

会议日期：26 June 2010 through 28 June 2010

会议地点：Wuhan

语种：英文

外文关键词：Fuzzy control - Genetic algorithms - Industrial research - Delay control systems - Membership functions - Time delay - Furnaces - Process control - Distributed parameter control systems - Fuzzy rules - Fuzzy inference

外文摘要：Hierarchical fuzzy control can process distributed control parameters, reduce the number of fuzzy rules effectively and easily extract fuzzy rules, so it is suitable for non-linear temperature control for industrial furnaces with features, such as large capacity and long time-delay. But rule sets and membership functions in conventional fuzzy control are often pre-determined according to human experiences and will no longer be changed in whole control process. Therefore, in the case of that there are more uncertain and disturbed factors, its control effect becomes unsatisfactory. In response to this situation, we presented a GA-based two-stage fuzzy temperature control algorithm for industrial furnaces, which can greatly reduce the number of fuzzy rules by taking advantages of hierarchical fuzzy control and taking full account of impact of many procedure parameters upon controlled variables. In the basis of that the fuzzy control decision is made through expert knowledge, we optimized fuzzy control query table using genetic algorithms, which not only avoided the most unreasonable consequence produced in the process of optimization of the control rules, but also greatly increased the convergence rate. Practical application showed that the algorithm can reduce the fuel consumption and possess a high control precision and robustness. Particularly for large time delay, nonlinear systems, its quality was superior to conventional control and general fuzzy control. ? 2010 IEEE.