Design and Implementation of PID Control Program Based on BP Neural Network

This article introduces the design and implementation of a PID control program based on Backpropagation (BP) Neural Network, accompanied by simulation graphs generated after program execution. The program design comprehensively explains the underlying principles of both BP neural networks and PID control, demonstrating their integration methodology to achieve optimized control performance. The implementation typically involves training the BP neural network to dynamically adjust PID parameters (proportional, integral, and derivative gains) through gradient descent optimization, where the neural network learns to minimize control errors by adapting its weights and biases. Following program execution, we present a series of simulation graphs that visually demonstrate the control system's performance characteristics and dynamic response. These graphs may include error convergence plots, system output responses, and parameter adaptation curves, providing intuitive insights into the control effectiveness. Through detailed technical explanations and graphical demonstrations, readers will gain practical understanding and application capabilities for BP neural network-based PID control program design.