An Improved Differential Evolution Algorithm for Fractional Order System Identification

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

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.

Key words: differential evolution algorithm, fractional-order system, system identification, adaptive adjustment, permanent magnet synchronous motor

CLC Number:

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.

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