Research on the Servo System based on DBO-BPPID Algorithm

Aiming at the requirements of high precision, strong robustness, and strong anti-disturbance ability of the anti-aircraft gun servo system, as well as the problems of internal parameter perturbation and external shooting torque interference, a BP neural network tuning PID control method based on the Dung Beetle Optimizer (DBO) is proposed. Firstly, the mathematical model of the artillery servo system is established, and the PID controller model is designed accordingly. Secondly, a BP neural network is added. A BP neural network can adjust PID parameters online through adaptive learning. However, the initial weights of the BP neural network are randomly selected, which makes it easy to fall into the local optimum during weight parameter training and affects the control performance of the system. Therefore, the DBO optimization algorithm is introduced to optimize the initial weights of the BP neural network by using DBO’s good global search ability, so as to speed up network convergence and improve the control performance of the system. Finally, the simulation results show that the DBO-BP-PID control algorithm designed in this paper can well meet the control requirements of the artillery servo system and has a good application prospect in the field of intelligent artillery servo control.