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Adaptive Integral Backstepping Controller for PMSM with AWPSO Parameters Optimization

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  • Weiran Wang

    (School of Electricity and Information, Jiangsu University of Science and Technology, Zhenjiang 212000, China
    College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Fei Tan

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212000, China)

  • Jiaxin Wu

    (School of Electricity and Information, Jiangsu University of Science and Technology, Zhenjiang 212000, China)

  • Huilin Ge

    (School of Electricity and Information, Jiangsu University of Science and Technology, Zhenjiang 212000, China)

  • Haifeng Wei

    (School of Electricity and Information, Jiangsu University of Science and Technology, Zhenjiang 212000, China)

  • Yi Zhang

    (School of Electricity and Information, Jiangsu University of Science and Technology, Zhenjiang 212000, China)

Abstract

This article presents an adaptive integral backstepping controller (AIBC) for permanent magnet synchronous motors (PMSMs) with adaptive weight particle swarm optimization (AWPSO) parameters optimization. The integral terms of dq axis current following errors are introduced into the control law, and by constructing an appropriate Lyapunov function, the adaptive law with the differential term and the control law with the integral terms of the current error are derived to weaken the influence of internal parameters perturbation on current control. The AWPSO algorithm is used to optimize the parameters of the AIBC. Based on the analysis of single-objective optimization and multi-objective realization process, a method for transforming multi-objective optimization with convex Prato frontier into single-objective optimization is presented. By this method, a form of fitness function suitable for parameters optimization of backstepping controller is determined, and according to the theoretical derivation and large number of simulation results, the corresponding parameters of the optimization algorithm are set. By randomly adjusting the inertia weight and changing the acceleration factor, the algorithm can accelerate the convergence speed and solve the problem of parameters optimization of the AIBC. The feasibility and effectiveness of the proposed controller for PMSM are verified by simulation and experimental studies.

Suggested Citation

  • Weiran Wang & Fei Tan & Jiaxin Wu & Huilin Ge & Haifeng Wei & Yi Zhang, 2019. "Adaptive Integral Backstepping Controller for PMSM with AWPSO Parameters Optimization," Energies, MDPI, vol. 12(13), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2596-:d:246036
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    Cited by:

    1. Feng Jiang & Fan Yang & Songjun Sun & Kai Yang, 2022. "Improved Linear Active Disturbance Rejection Control for IPMSM Drives Considering Load Inertia Mismatch," Energies, MDPI, vol. 15(3), pages 1-22, February.
    2. Jie Chen & Jiajun Wang & Bo Yan, 2022. "Simulation Research on Deadbeat Direct Torque and Flux Control of Permanent Magnet Synchronous Motor," Energies, MDPI, vol. 15(9), pages 1-15, April.

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