Ensemble-Based PID Gain Learning Framework for Adaptive Temperature Control in Micro-Thermoelectric Systems

Abstract

Precise temperature regulation in Micro-Thermoelectric Cooler (Micro-TEC) systems is essential for applications such as polymerase chain reaction (PCR) thermal cyclers, biomedical instrumentation, and microelectronic cooling platforms. Proportional-Integral-Derivative (PID) controllers remain widely adopted for their structural simplicity, but their performance depends heavily on gain selection. Heuristic tuning has limited adaptability under nonlinear thermal dynamics, while optimization-based techniques such as genetic algorithm (GA) and particle swarm optimization (PSO) require repeated, computationally intensive searches. A consolidated PID gain knowledge repository is built by running GA and PSO offline over 2,500 Micro-TEC operating scenarios, which serves as supervised training data for an ensemble regression model composed of Random Forest, Extra Trees, and Gradient Boosting learners combined through stacking. The trained ensemble model learns the mapping between operating conditions and near-optimal PID gains, eliminating the need for repeated evolutionary optimization during deployment. On the Micro-TEC plant model, the proposed ensemble controller lowers overshoot by 37.9% (from 6.31% to 3.92%), cuts settling time by 28.0% (from 16.8 s to 12.1 s), reduces steady-state temperature error to 0.11°C (a 59.3% reduction), and selects its gains about 65 times faster (15 ms against 980 ms) than a conventional PSO-tuned PID. The results show that the framework bridges classical PID control and data-driven learning, providing a scalable, computationally efficient approach for scenario-aware gain scheduling in nonlinear thermal systems.