PID Control Tuned by Improved BP Neural Network

This program implements PID control tuned by an improved Backpropagation (BP) neural network for parameter adjustment. The enhanced BP neural network algorithm optimizes PID controller parameters (proportional, integral, and derivative gains) through iterative learning and error minimization. The method significantly improves control system performance and stability by employing gradient descent optimization with momentum terms and adaptive learning rates. The improved BP network enables more precise PID parameter adjustments through its multi-layer feedforward architecture with sigmoid activation functions, resulting in faster and more accurate system responses. Furthermore, the neural network's adaptive tuning capability allows real-time parameter adjustments when system dynamics change or disturbances occur, enhancing system robustness and adaptability. The implementation includes batch training with historical data and online adjustment mechanisms. Therefore, using improved BP neural network-tuned PID control provides an effective approach to enhance control system performance and meet diverse control requirements through its self-learning and optimization capabilities.