PID Control Tuned by BP Neural Network

Using the PID control method tuned by BP neural network enables more precise control through adjustment of proportional, integral, and derivative control actions. This approach combines the learning capability of neural networks with the stability of PID controllers, allowing automatic adjustment of control parameters under different operating conditions to improve system response speed and stability. The implementation typically involves training the BP neural network to optimize PID parameters (Kp, Ki, Kd) through backpropagation algorithm, where the network learns the optimal control strategy by minimizing the error between desired and actual outputs. Key functions include gradient descent optimization for weight updates and sigmoid activation functions for hidden layer processing. Additionally, by employing BP neural networks, adaptive learning and optimization of system models can be achieved, further enhancing control performance through continuous parameter adjustment based on real-time system feedback.