Author(s)

Zi Wang ¹, Weiluo Wang ²

Affiliation(s)

¹ School of Smart City and Transportation, Southwest Jiaotong University, Chengdu, 611756, China
² College of Mechanical Engineering, Chongqing University of Technology, Chongqing, 400054, China

Corresponding Author

Zi Wang

ABSTRACT

The current research mainly focuses on the application of active disturbance rejection controller (ADRC) in the motor control field, but its parameter tuning method is still highly dependent on experiences or optimization algorithms, which has the shortcomings of slow convergence speed and easy to falling into the local optimal result. In this paper, a hybrid optimization strategy combining the global search ability of genetic algorithm (GA) and the local optimization advantages of particle swarm optimization (PSO) is proposed to achieve parameter tuning of ADRC. In the simulation, compared with the performance of genetic algorithm, particle swarm optimization and dynamic weight adjustment hybrid algorithm under parameter disturbance and load interference, the hybrid algorithm has the fastest convergence and the best global search, which is significantly better than the particle swarm optimization and genetic algorithm that are prone to local optimization. The optimization strategy is based on extended state observer (ESO)bandwidth balance dynamic response and noise immunity, and realizes high real-time robust control of motor drive with minimum overshoot, fast adjustment and high tracking accuracy.

KEYWORDS

Reluctance motor, Parameter tuning method, Hybrid optimization strategy

CITE THIS PAPER

Zi Wang, Weiluo Wang, Parameter Tuning Method of Reluctance Motor Based on Hybrid Optimization Strategy. Journal of Electrotechnology, Electrical Engineering and Management (2025) Vol. 8: 111-119. DOI: http://dx.doi.org/10.23977/jeeem.2025.080114.

REFERENCES

[1] Zhang, H., & Cheng, M. (2023). A novel torque ripple suppression method for switched reluctance motors using harmonic current injection. IEEE Transactions on Energy Conversion, 38(1), 432-442.
[2] Lee, D. H., & Ahn, J. W. (2019). Torque ripple minimization of switched reluctance motors using model predictive control with adaptive compensation. IEEE Transactions on Industrial Electronics, 66(9), 6813-6822.
[3] Wang, J., & Gao, Z. (2020). Active disturbance rejection control for permanent magnet synchronous motor drives: A comparative study. IEEE Transactions on Power Electronics, 35(10), 10561-10572.
[4] Li, X., & Wang, Y. (2021). A hybrid GA-PSO algorithm for parameter tuning of nonlinear systems with application to robotic control. IEEE Transactions on Cybernetics, 51(6), 3124-3135.
[5] Zhang, Y., & Li, S. (2022). A self-adaptive particle swarm optimization with dynamic inertia weight for multi-objective optimization. Swarm and Evolutionary Computation, 68, 101005.
[6] Gupta, S., & Deep, K. (2019). A hybrid grey wolf optimizer and genetic algorithm for constrained optimization problems. Engineering Applications of Artificial Intelligence, 87, 103249.
[7] Liu, C., & Luo, Y. (2022). Real-time adaptive control of switched reluctance motors under parameter uncertainties. IEEE/ASME Transactions on Mechatronics, 27(2), 1025-1035.
[8] Chen, W. H., Yang, J., & Li, S. (2021). Bandwidth-parameterized ESO design for uncertain systems with applications to UAVs. Control Engineering Practice, 112, 104813.

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