Research on permanent magnet synchronous motor control of mining locomotive under load mutation
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1.State Key Laboratory of Digital and Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology,Huainan 232001, China;2.Collaborative Innovation Center for Mining Intelligent Technology and Equipment, Anhui University of Science and Technology,Huainan 232001, China;3.School of Mechanical Engineering, Anhui University of Science and Technology,Huainan 232001, China

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TM351;TN830

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    Abstract:

    In order to solve the problems of insufficient anti-interference ability, poor system accuracy and slow convergence speed of permanent magnet synchronous motor control caused by complex underground working conditions of coal mine electric locomotive, an intelligent control method of BP neural network PID based on improved dung beetle-osprey algorithm was proposed. Firstly, the improved dung beetle algorithm and the osprey algorithm were combined to design an improved dung beetle-osprey algorithm, and the global search strategy of the osprey algorithm replaced the rolling ball stage of the dung beetle algorithm. Secondly, the sinusoidal learning factor is introduced to improve the exploration ability of the algorithm, the dynamic spiral search improves the global search performance of the algorithm, and the adaptive t-distribution perturbation and piecewise function methods jump out of the local optimal to improve the quality of the solution. The algorithm optimizes the learning factor and inertia factor of the BP neural network, so that the neural network can output the best PID parameters more quickly. Finally, the voltage feedforward decoupling is added to offset the coupling term of the permanent magnet synchronous motor and improve the dynamic response of the permanent magnet synchronous motor. Through Matlab/Simulink simulation and RT-LAB experiments on the semi-physical platform, the BP neural network PID controller of the improved dung beetle-osprey algorithm is compared with the traditional PID controller, and the results show that when the target speed is 1 200 r/min after the load torque is abrupt, the recovery time and overshoot of the improved dung beetle-osprey algorithm BP neural network PID controller are reduced by about 98.3% and 66%, respectively, compared with the traditional PID control.The experimental results show that compared with PID control, PSO control and BAS-PID control, the IDBO-OOA-PID speed control speed fluctuation is smaller, the speed return time to the set value is shorter, and the current response is more stable when the load burst and speed are abrupt.It is verified that the IDBO-OOA-PID speed controller has good anti-interference ability, stability and robustness.

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  • Online: November 04,2025
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