PID parameter tuning of DC motor based on improved particle swarm optimization

Xue-Ting Pang; Chao Chen; Xin Tong; Qian-Li Wei

doi:10.1117/12.2684885

19 October 2023 PID parameter tuning of DC motor based on improved particle swarm optimization

Xue-Ting Pang, Chao Chen, Xin Tong, Qian-Li Wei

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270966 (2023) https://doi.org/10.1117/12.2684885
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Aiming at the problem of complex and time-consuming PID parameter tuning of Brushless DC reduction motor. A PID parameter tuning method based on improved particle algorithm is proposed. Firstly, the principle of orbiting each particle is proposed, which improves the inertia weight of traditional particle swarm optimization algorithm; Then, the particle swarm mutation method is proposed to ensure that there will be no local optimization in the iterative process. The transfer function of the motor is measured by experimental method, and then the modeling and simulation analysis are carried out using MATLAB. The experimental and simulation results show that the improved particle swarm optimization algorithm will not fall into local optimization compared with the ordinary particle swarm optimization algorithm. Compared with the parameters obtained by the PID parameter tuning and the parameters obtained by the Zn and AC tuning methods, the rising time is less, the time required for stabilization is shorter, and the deviation after stabilization is smaller. Using improved particle algorithm for PID parameter tuning can improve the accuracy of PID control and reduce the complexity and timeconsuming problem of PID parameter tuning.

Citation Download Citation

Xue-Ting Pang, Chao Chen, Xin Tong, and Qian-Li Wei "PID parameter tuning of DC motor based on improved particle swarm optimization", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270966 (19 October 2023); https://doi.org/10.1117/12.2684885

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