PID Control System Using BP Neural Network

This program implements a PID control system based on BP neural network technology. The system architecture comprises three core components: a conventional PID controller, a neural network module, and a data processing module. The system employs BP neural network algorithms to learn and optimize PID controller parameters (proportional, integral, and derivative gains) through backpropagation training, achieving more precise and stable control performance. The neural network module analyzes system input-output data patterns using gradient descent optimization to automatically adjust PID parameters in real-time, adapting to varying operational conditions and control requirements. The data processing module handles sensor data acquisition and signal conditioning, providing accurate input data to the neural network through filtering and normalization routines. By integrating traditional PID control with neural network technology, this system enhances control system performance and adaptability through machine learning capabilities, resulting in more efficient and reliable control operations with improved disturbance rejection characteristics.