首页 | 本学科首页   官方微博 | 高级检索  
     

应用多模型粒子群优化算法的过热汽温模糊控制
引用本文:郝万君,刘国良,颜世佐,强文义. 应用多模型粒子群优化算法的过热汽温模糊控制[J]. 动力工程, 2006, 26(5): 650-655
作者姓名:郝万君  刘国良  颜世佐  强文义
作者单位:1. 哈尔滨工业大学,航天学院,哈尔滨,150001;北华大学,计算机学院,吉林,132021
2. 哈尔滨工业大学,航天学院,哈尔滨,150001
摘    要:针对单一模型的粒子群参数优化算法存在鲁棒性差的问题,提出一种应用多模型粒子群参数优化的方法,将其应用于模糊控制器参数的优化,有效地避免了模糊控制器设计中复杂的参数调试,使其获得良好的控制品质。通过对锅炉过热汽温系统的仿真实验,在负荷大范围变化的条件下,控制系统仍保持了良好的控制性能,并且具有较强的鲁棒性。仿真结果表明了所设计的控制器的有效性和所提出的优化算法的优越性。图6表2参11

关 键 词:自动控制技术  模糊控制  多模型  参数优化  粒子群优化算法
文章编号:1000-6761(2006)05-650-06
收稿时间:2006-03-06
修稿时间:2006-03-06

Fuzzy Control of Saturated Steam Temperature by Multi-Model Particle Swarm Optimization
HAO Wan-Jun,LIU Guo-Liang,YAN Shi-Zuo,QIANG Wen-Yi. Fuzzy Control of Saturated Steam Temperature by Multi-Model Particle Swarm Optimization[J]. Power Engineering, 2006, 26(5): 650-655
Authors:HAO Wan-Jun  LIU Guo-Liang  YAN Shi-Zuo  QIANG Wen-Yi
Affiliation:1. School of Astronautics, Harbin University of Technology, Harbin 150001, China; 2. School of Computers, Beihua University, Jilin 132021, China
Abstract:A multi-model particle swarm optimization(PSO) algorithm is being proposed for solving the problem of poor robustness of single-model PSO.By using it for optimizing the parameters of fuzzy controllers,complex adjustment requirements of parameters can be avoided in the design of the fuzzy controllers and thus improve their control properties.Control simulation results of a boiler's superheater system show that the control system can still maintain good control properties,even during large load variations,exhibiting simultaneously strong robustness.This demonstrates the effectiveness of the designed controller.Figs 6,tables 2 and refs 11.
Keywords:automatic control technique   fuzzy control   multiple models   parameter optimization   particle swarm optimization algorithm
本文献已被 CNKI 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号