Parameter identification of the fractional-order systems based on a modified PSO algorithm  

基于改进粒子群算法的分数阶系统参数辨识（英文）

作　　者：Liu Lu Shan Liang Jiang Chao Dai Yuewei Liu Chenglin Qi Zhidong 刘璐;单梁;蒋超;戴跃伟;刘成林;戚志东(南京理工大学自动化学院,南京210094;Department of Electrical and Computer Engineering,Stevens Institute of Technology,Hoboken,NJ 07030,USA;江南大学轻工过程先进控制教育部重点实验室,无锡214122)

机构地区：[1]School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China [3]Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education,Jiangnan University,Wuxi 214122,China

出　　处：《Journal of Southeast University(English Edition)》2018年第1期6-14,共9页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.61374153,61473138,61374133);the Natural Science Foundation of Jiangsu Province(No.BK20151130);Six Talent Peaks Project in Jiangsu Province(No.2015-DZXX-011);China Scholarship Council Fund(No.201606845005)

摘　　要：In order to better identify the parameters of the fractional-order system,a modified particle swarm optimization(MPSO)algorithm based on an improved Tent mapping is proposed.The MPSO algorithm is validated with eight classical test functions,and compared with the POS algorithm with adaptive time varying accelerators(ACPSO),the genetic algorithm(GA),a d the improved PSO algorithm with passive congregation(IPSO).Based on the systems with known model structures a d unknown model structures,the proposed algorithm is adopted to identify two typical fractional-order models.The results of parameter identification show that the application of average value of position information is beneficial to making f 11 use of the information exchange among individuals and speeds up the global searching speed.By introducing the uniformity and ergodicity of Tent mapping,the MPSO avoids the extreme v^ue of position information,so as not to fall into the local optimal value.In brief the MPSOalgorithm is an effective a d useful method with a fast convergence rate and high accuracy.为了更好地辨识分数阶系统的参数,提出了一种基于Tent映射的改进粒子群算法(MPSO).采用8个经典测试函数对MPSO算法的性能进行了测试,并与自适应时变加速器算法(ACPSO)、改进的被动聚集粒子群算法(IPSO)以及遗传算法(GA)进行对比,验证了所提算法的有效性.在已知模型结构和未知模型结构的基础上,利用所提算法对2种典型分数阶模型进行参数辨识.参数辨识结果表明,应用位置信息的平均值有利于充分共享个体间的信息,从而能够加快全局搜索速度;Tent映射具有的均匀性和遍历性能够防止位置信息中极值的产生,避免算法陷入局部最优.MPSO算法收敛速度快、精度高,是一种有效且实用的方法.

关 键 词：particle swarm optimization Tent mapping parameter identification fractional-order systems passive congregation 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]