BP Neural Network-Based PID Control Program

The BP neural network-based PID control program utilizes a backpropagation neural network to implement PID control algorithms. BP neural networks are artificial neural networks that can learn and adapt to various control tasks through training processes. PID control is a widely adopted method in industrial automation that maintains system stability by adjusting three parameters: proportional, integral, and derivative gains. In implementation, the program typically involves creating a neural network structure with input layers receiving system error signals, hidden layers processing nonlinear relationships, and output layers generating optimized PID parameters. The backpropagation algorithm continuously adjusts network weights during training to minimize control error. Key functions include error calculation, gradient descent optimization, and real-time parameter updates. This approach enables automatic tuning of control parameters through neural network training, resulting in more precise and stable control performance compared to conventional PID methods. The program often incorporates adaptation mechanisms where the network continuously refines parameters based on system feedback, making it particularly effective for complex nonlinear systems.