The traditional PID controller is widely used to improve the stability of the system because of its simplicity and convenience. However, the disadvantage of the control method is that the control parameters are difficult to adjust in real-time, and it can not adapt to the needs of variable control targets in different states, affecting the controller’s practical application effect. Therefore, using the idea of hierarchical learning self-adjusting control, this paper optimizes the traditional PID controller based on BP neural network. The experimental results show that the controller can automatically adjust the control parameters, improve the control accuracy and achieve rapid response.

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