A Review and Comparative Analysis (2021-2025) on Various Types of Temperature Controllers

Abstract

Temperature control is one of the key processes in industrial, thermal, food, chemical, microchip manufacturing, 3D-printing, energy, and laboratory equipment. From the simplest on–off control and PID schemes to fuzzy, neural, model predictive, sliding-mode, adaptive, and reinforcement-learning control, temperature controllers are extensively studied and applied. This review article compares the main types of temperature controllers regarding their accuracy, overshoot, settling time, computational complexity, robustness, model dependency, ease of implementation, and applicability to nonlinear/variable thermal systems. Particular emphasis is placed on recently published literature reviews and journal articles from 2021 to 2025. While the transition from PID to more sophisticated approaches is evident, increased “intelligence” does not always translate into better performance due to complexities of implementation and the need for training data. In terms of accuracy, overshoot, and implementation simplicity, the review highlights PID control, fuzzy-PID, and PID-based adaptive controllers as the best performers. For multivariable systems with constraints, model predictive control (MPC) is also superior. Neural-network and reinforcement-learning methods are highlighted as the future of temperature control.