PID Neural Network Control Algorithm with Implementation Code

This article presents a PID neural network-based control algorithm implementation, complete with simulation-ready code. The PID neural network represents an innovative control approach that integrates the advantages of traditional PID control with neural network capabilities. The algorithm operates by feeding input signals into a neural network architecture, which subsequently generates control signals to regulate system behavior. The implementation typically involves three key components: proportional, integral, and derivative neural network layers that process error signals through adjustable weight matrices. Through PID neural network control, we achieve enhanced precision and stability in control systems while maintaining superior adaptability to system variations and disturbances. The code implementation includes neural network training routines using gradient descent methods, error backpropagation algorithms, and real-time parameter adjustment mechanisms. This article details the complete implementation methodology for the PID neural network control algorithm and provides fully functional control code, enabling readers to conduct simulation experiments and advance further research. The code structure includes neural network initialization functions, weight update algorithms, and control signal calculation modules that can be readily integrated into various control system simulations.