Temperature Control Based on Fuzzy Neural Networks for High-Power Laser Diodes

High-power laser diodes (LDs) inherently generate considerable heat during current loading, which presents substantial challenges to the stable operation of laser systems. This study reports a machine learning-based approach that is to be applied to LD temperature control systems, in which a fuzzy neural network (FNN) algorithm is integrated with a proportional-integral-derivative (PID) controller to create an FNN-PID control architecture. The proposed algorithms synergistically integrate fuzzy rule-based systems with neural network learning frameworks, and, furthermore, facilitate adaptive parameter optimization while preserving the interpretability of the decision-making process. Applying the optimized algorithm temperature controller to the LD with output optical power of 110 W @ 888 nm, compared with the conventional PID, the FNN-PID algorithm has shortened the temperature settling time by 77% during 100 W heat generation in LD, the long-term temperature fluctuation is decreased from ±0.126% to ±0.06%, the corresponding optical power steady-state precision is decreased from ±0.09% to ±0.04%, and the step response time of temperature and corresponding power are reduced by 73.4% and 70% from 25 °C to 27 °C, respectively. The FNN-PID outperforms conventional methods (the PID algorithm and the Fuzzy-PID algorithm) in managing thermal fluctuations, and it offers potential for precise laser control applications to enhance beam quality and stability.