Research and Analysis on PID Control and Its Improvement Methods

Abstract

This paper systematically reviews the control principles, application scenarios, advantages and disadvantages of three main improved algorithms: fuzzy Proportional–Integral–Derivative (PID), adaptive PID, and neural network PID. Fuzzy PID is used for systems with empirical-driven nonlinear and uncertain characteristics. It has advantages of low cost, fast response, and strong real-time performance, but its control accuracy is limited. Adaptive PID is used for systems with parameter time-varying, variable operating conditions, and known models. It can achieve parameter self-tuning and strong adaptability. However, it has a higher cost and is highly dependent on identification accuracy and computing resources. Neural network PID is used for systems with high nonlinearity, strong coupling, and difficult modeling. It has outstanding nonlinear learning capabilities. Through sample training, it can achieve intelligent regulation, nonlinear compensation, and strong robustness. However, the training process is complex, and it is highly dependent on data quality and computing resources. Its generalization ability is limited. The future development of PID will tend towards algorithm integration and intelligent optimization. By using multiple hybrid improved methods, the adaptability and accuracy of the system will be enhanced.