Comparison of Neural Network PID Control Performance vs Traditional PID Control

By implementing neural network PID control methodology and comparing it with conventional PID control approaches, we conclude that neural network PID control achieves higher precision. The neural network PID method utilizes neural network models to perform system modeling and optimization, enabling better adaptation to system dynamic variations and thereby improving control accuracy. This approach typically involves training a neural network (using algorithms like backpropagation) to learn system dynamics and adjust PID parameters (proportional, integral, derivative gains) in real-time based on system responses. Key implementation aspects include designing the network architecture (number of hidden layers, activation functions), establishing training datasets from system input-output relationships, and implementing online adjustment mechanisms for PID parameters through the neural network's predictive capabilities. Compared to fixed-parameter traditional PID controllers, this adaptive control strategy significantly enhances performance in dealing with nonlinear systems and time-varying processes.