Research on Servo Motor Control System Based on Adaptive Neural Network

In response to the issue of model uncertainty in servo motors, a control method based on an adaptive neural network with global approximation capability is proposed. Firstly, the mathematical model of the servo motor is established and simplified into a third-order numerical model containing unknown nonlinear functions. Secondly, an adaptive neural network with global approximation capability is designed to address the unknown nonlinear functions in the model. Based on this, a controller is designed, and the stability of the system is demonstrated through Lyapunov theory, ensuring the convergence of the algorithm. Finally, traditional PID control algorithm is introduced for comparison and verification. Simulation results indicate that the proposed method effectively reduces tracking errors and improves control accuracy.