An Improved Differential Evolution Algorithm for Fractional Order System Identification

Authors

Yu Wei, 1. School of Mechanical and Electrical Engineering and Automation, Foshan University, Foshan 528000, China; ;
Henghui Liang, 1. School of Mechanical and Electrical Engineering and Automation, Foshan University, Foshan 528000, China; ;
Luo Ying, 2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;

Abstract

Abstract: In order to build a high-precision fractional-order model, which needs to identify more parameters, an improved differential evolution algorithm is proposed for the identification of fractional-order systems. In the mutation strategy, the basis vector is randomly selected from the optimal individual population, and the scaling factor and cross-probability factor are adaptively adjusted according to the information of the successfully mutated individual during the search process to improve the exploration and mining capabilities of the algorithm. By solving the five test functions, the improved algorithm is proved to have strong solving ability. Taking the fractional-order model of permanent magnet synchronous motor as an example, the identification results show that the improved differential evolution algorithm has better performance in solving accuracy and convergence speed.

Recommended Citation

Wei, Yu; Liang, Henghui; and Ying, Luo (2021) "An Improved Differential Evolution Algorithm for Fractional Order System Identification," Journal of System Simulation: Vol. 33: Iss. 5, Article 16.
DOI: 10.16182/j.issn1004731x.joss.20-0853
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss5/16

First Page

1157

Revised Date

2021-01-05

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0853

Last Page

1166

CLC

N945.14;TP391

Recommended Citation

Yu Wei, Liang Henghui, Luo Ying. An Improved Differential Evolution Algorithm for Fractional Order System Identification[J]. Journal of System Simulation, 2021, 33(5): 1157-1166.

DOI

10.16182/j.issn1004731x.joss.20-0853