Optimization of PID Control over DC Servo Motor System based on BP Neural Networks

This paper presents an optimization model of the PID (Proportional-Integral-Derivative) control system for DC (Direct Current) servo motors using BP (Back Propagation) neural networks to enhance the overall system performance. Traditional PID control systems are widely used in DC motor applications due to their stability and accuracy, but they struggle with issues like overshoot, oscillations, and slow response, particularly in nonlinear and time-varying systems. To address these challenges, this paper proposes integrating BP neural networks to automatically ascertain the PID critical parameters, which could lead to a faster response, minimal overshoot, and reduced steady-state error. In this experiment, the DC servo motor model was first simulated using Falstad, with a manually set PID control system to analyze its performance. Then, various control systems were compared to highlight the advantages of the PID controller. Finally, the PID control system was optimized using BP neural networks. As a result, the experimental results show that the optimized system outperforms the manually configured PID controller, offering improved stability and faster response times without oscillations. These findings indicate that BP neural network optimization significantly enhances the robustness and effectiveness of PID control systems for DC servo motors, making them more suitable for practical applications. However, to further improve its universal adaptability, more comprehensive research is needed to apply this optimization model to other types of devices with nonlinear behaviors to improve its viability among various kinds of devices.