Research on the Optimization of PID Temperature Control for High-Precision Applications

This paper reviews the fundamental model, experimental methods, and achievable control performance of traditional Proportional-Integral-Derivative (PID) temperature control. Building on this foundation, it further investigates multi-module coordination and algorithm optimization to enhance the response speed, accuracy, and adaptability of industrial temperature control systems. The study integrates the flexibility of fuzzy control with the high-precision characteristics of the PID algorithm and explores intelligent PID temperature control schemes, such as the control logic of fuzzy PID controllers. Additionally, it analyzes relevant application cases, particularly the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) combined with incremental PID in radiofrequency ablation (RFA) temperature control. Compared to traditional PID control systems, this method demonstrates superiority in high-precision applications, such as fiber optic gyroscope temperature control and LED thermal management, and provides a robust solution that combines theoretical and practical approaches for complex thermal environments. Through multi-module coordination and algorithm optimization, this research effectively improves the performance of industrial temperature control systems and offers new perspectives for the advancement of precise temperature control technology.