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    Abstract: In view of the problem that traditional PID control fails to achieve accurate control in the permanent magnet synchronous motor control system, a PID controller of BP neural network based on improved dung beetle optimization algorithm is proposed. The controller uses BP neural network to adjust the weight coefficient by adaptive method, which solves the shortcoming that PID can not adjust parameters online.Then, aiming at the high probability of BP neural network falling into local optimum when performing back propagation, the improved dung beetle optimization algorithm is introduced to continuously update the core parameters of the BP neural network through the adaptive value, so as to improve the optimization rate of the BP neural network. Furthermore, addressing issues like low initial population quality and inadequate search capability in the improved dung beetle optimization algorithm, a hybrid strategy is implemented to optimize the improved dung beetle optimization algorithm, which greatly improves the solving efficiency and accuracy of the improved dung beetle optimization algorithm. Experimental results demonstrate that the improved dung beetle optimization algorithm effectively enhances the response speed of the control system, reduces overshoot, and it has strong robustness to the case of speed and load sudden change.

    Key words: permanent magnet synchronous motor; improved dung beetle optimization algorithm; BP neural network; PID control