Neural Network PID Control Source Code Implementation Guide

This document provides a detailed explanation of developing neural network PID control source code using MATLAB. The article demonstrates how to implement PID control utilizing MATLAB's Neural Network Toolbox. The implementation begins by importing the Neural Network Toolbox along with other essential toolkits using commands like `addpath` or through the MATLAB environment setup. Next, we create a neural network object using functions such as `feedforwardnet` or `fitnet`, specifying the network architecture (number of hidden layers, neurons) appropriate for PID control applications. The network is then trained with historical control data using the `train` function, where we configure training parameters like learning rate, epochs, and performance goals. After training completion, we validate the neural network's performance using test datasets through the `sim` function, analyzing metrics like mean squared error and control stability. The trained neural network is then deployed for PID control implementation, where it dynamically adjusts PID parameters (proportional, integral, derivative gains) based on system feedback. We evaluate control effectiveness by comparing traditional PID performance with neural network-enhanced PID through simulation results and performance indices. Additionally, the document covers other key functionalities of the Neural Network Toolbox, including data preprocessing methods (`mapminmax` for normalization), different training algorithms (`trainlm` for Levenberg-Marquardt, `trainbr` for Bayesian regularization), and real-time implementation considerations. These explanations help users better understand and utilize the toolbox for advanced control system development.