Building a PID Controller Model Using BP Neural Network

This work demonstrates my dedicated implementation of a PID controller model constructed using Backpropagation (BP) Neural Network. The core implementation involves training the neural network to approximate PID control parameters through gradient descent optimization, where the network learns to adjust proportional, integral, and derivative gains dynamically. The model architecture likely includes input layers for error signals, hidden layers with activation functions (typically sigmoid or tanh), and output layers generating control signals. Key implementation aspects may involve error backpropagation algorithms, weight adjustment mechanisms, and training data normalization techniques. Through this BP neural network approach, the system achieves enhanced PID controller functionality with adaptive tuning capabilities, yielding satisfactory control performance. This implementation represents substantial engineering effort and thoughtful design, aiming to contribute valuable insights to control system research and practical applications in related fields.