PID motor control system based on ACO-BP neural network

The traditional proportional-integral-derivative (PID) motor control method is only suitable for linear systems, and there are some problems with this method, such as difficult parameter setting and low control precision. The BP neural network has the problem of local minimum owing to the large gap between the initial parameters and the optimal solution, which leads to the problems of a large overshoot and system oscillation in the BP neural network tuning the PID control. Therefore, this paper presents a method based on an improved ant colony algorithm to optimize a BP neural network and adjust PID control parameters online in real time. In this study, the ant colony algorithm was used to search for the optimal initial parameter value globally, and then BP neural network self-learning was used to adjust the PID control parameters online in real time, to obtain the global optimal solution and reduce the system oscillation. Through simulation experiments, it was found that the proposed method has the advantages of a small overshoot, fast motor response and small error, and has a certain reference significance in the application of motor control.