BP-PID Neural Network Control

This project implements BP-PID neural network control using MATLAB as the primary development language. The control methodology integrates Backpropagation (BP) neural networks with PID control algorithms to achieve precise regulation and optimization during system control processes. The BP neural network component learns nonlinear mapping relationships within the system, while the PID control algorithm ensures stable system operation. The implementation typically involves configuring neural network layers with sigmoid activation functions and employing gradient descent optimization for weight updates. Key MATLAB functions utilized include 'newff' for creating feed-forward networks, 'train' for network training, and custom PID controller functions for error correction. This combined approach enhances system performance and response speed, achieving more accurate control outcomes. Using MATLAB facilitates efficient model establishment, algorithm implementation, and system simulation through its built-in neural network toolbox and control system functionalities. This control method demonstrates significant application potential in industrial automation domains, particularly for complex systems requiring adaptive control capabilities.