计算机应用研究2013，Vol.30Issue(8)：2301-2303,2314,4.DOI:10.3969/j.issn.1001-3695.2013.08.015
基于改进量子粒子群算法的NCS模糊控制器参数优化
Tuning of optimal fuzzy NCS controller parameters based on improved quantum particle swarm
摘要
Abstract
The traditiaonal experience method is difficult to design measure-parameter and scal-parameter of fuzzy controller in networked control systems(NCS),this paper advanced a new method to select these parameters depended on improved quantum particle swam optimization(IQPSO) algorithm.To improve the performance of quantum particle swam optimization (QPSO)algorithm,this paper proposed an adaptive mutation QPSO algotithm based on search operator of artificial bee colony (ABC)algorithm.The method used the ITAE indx as the fitness function of the IQPSO algorithm to optimize the fuzzy controller parameters.Simulation results of the typical indusrical process show that the optimal fuzzy controller using IQPSO has better control performance and adaptability than the optimal fuzzy controller using QPSO and the optimal PID controller using IQPSO.关键词
网络控制系统/改进量子粒子群优化/模糊控制/人工蜂群算法Key words
NCS(networked control systems) / IQPSO/ fuzzy control/ ABC分类
信息技术与安全科学引用本文复制引用
李炜,蔡翔..基于改进量子粒子群算法的NCS模糊控制器参数优化[J].计算机应用研究,2013,30(8):2301-2303,2314,4.基金项目
国家自然科学基金资助项目(60964003) （60964003）
