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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, (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()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
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
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
Keywords:
particle swarm optimization Tent mapping parameter identification fractional-order systems passive congregation
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.

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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