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[1] Liu Lu, Shan Liang, Jiang Chao, Dai Yuewei, et al. Parameter identification of the fractional-order systemsbased on a modified PSO algorithm [J]. Journal of Southeast University (English Edition), 2018, 34 (1): 6-14. [doi:10.3969/j.issn.1003-7985.2018.01.002]
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Parameter identification of the fractional-order systemsbased on a modified PSO algorithm()
基于改进粒子群算法的分数阶系统参数辨识
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
34
Issue:
2018 1
Page:
6-14
Research Field:
Computer Science and Engineering
Publishing date:
2018-03-20

Info

Title:
Parameter identification of the fractional-order systemsbased on a modified PSO algorithm
基于改进粒子群算法的分数阶系统参数辨识
Author(s):
Liu Lu1, Shan Liang1, Jiang Chao2, Dai Yuewei1, Liu Chenglin3, Qi Zhidong1
1School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
2Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA
3Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China
刘璐1, 单梁1, 蒋超2, 戴跃伟1, 刘成林3, 戚志东1
1南京理工大学自动化学院, 南京 210094; 2 Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA; 3江南大学轻工过程先进控制教育部重点实验室, 无锡214122
Keywords:
particle swarm optimization;  Tent mapping;  parameter identification;  fractional-order systems;  passive congregation
粒子群优化;  Tent映射;  参数辨识;  分数阶系统;  被动聚集
PACS:
TP301.6
DOI:
10.3969/j.issn.1003-7985.2018.01.002
Abstract:
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), and the improved PSO algorithm with passive congregation(IPSO). Based on the systems with known model structures and 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 full 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 value of position information, so as not to fall into the local optimal value. In brief, the MPSO algorithm is an effective and 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.

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Memo

Memo:
Biographies: Liu Lu(1990—), male, Ph.D. candidate; Shan Liang(corresponding author), male, doctor, associate professor, shanliang@njust.edu.cn.
Foundation items: 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).
Citation: Liu Lu, Shan Liang, Jiang Chao, et al. Parameter identification of the fractional-order systems based on a modified PSO algorithm[J].Journal of Southeast University(English Edition), 2018, 34(1):6-14.DOI:10.3969/j.issn.1003-7985.2018.01.002.
Last Update: 2018-03-20