Abstract: This paper discusses the neural network PID control strategy, utilizes the self-learning ability of the neural network to tune the PID control parameters online, and conducts simulation research using Matlab software. Simulation results show that the neural network PID controller has simple parameter tuning, high accuracy, and strong adaptability, and can achieve satisfactory control results. Keywords : neural network, BP neural network, PID, parameter tuning, simulation 1. Introduction PID control is widely used in industrial control due to its advantages such as good intuitiveness, simple implementation, high reliability, and strong robustness, especially suitable for deterministic systems with established accurate mathematical models. However, the effectiveness of conventional PID control directly depends on the quality of the selected control parameters. Traditional methods determine PID parameters based on a certain tuning principle after obtaining a mathematical model of the controlled object. However, modern industrial processes are complex, often exhibiting nonlinearity, time-varying, variable parameter, and variable structure uncertainties, making it difficult to determine an accurate mathematical model. Therefore, conventional PID control rarely achieves satisfactory control results. Furthermore, conventional PID control suffers from problems such as fixed control parameter forms, difficulty in online adjustment, long parameter tuning processes, and mutual influence between parameters, which to some extent affect its use and control effectiveness. To enable the controller to have better adaptability and achieve automatic adjustment of control parameters, neural network control can be used. Utilizing the nonlinear mapping capability, self-learning capability, and generalization capability of neural networks, combined with conventional PID control theory, and by absorbing the advantages of both, the system becomes adaptive, automatically adjusting control parameters to adapt to changes in the controlled process, thus improving control performance and reliability. For details, please click: Neural Network PID Control Strategy and its Matlab Simulation Research.