Robust MEMS Temperature Control Platform With GA PSO-Fuzzy PID Control

This paper presents a robust, low-power MEMS temperature control platform featuring a novel hybrid control strategy combining Genetic Algorithm and Particle Swarm Optimization (GA PSO) with Fuzzy PID control. Two heater structures, loop and bent types, are designed and simulated to enhance temperature uniformity, revealing that the bent configuration with an electrode thickness of 500 nm provides superior stability. The key innovation lies in the hybrid GA PSO-Fuzzy PID approach, which autonomously optimizes proportional–integral–derivative (PID) parameters, removing the necessity for manual tuning. This methodology effectively reduces temperature overshoot and accelerates response times, outperforming conventional GA PSO-PID control. Experimental results demonstrate substantial performance improvements, including a faster rise time by 1.98 seconds, reduced peak time by 13.4 seconds, and a shorter settling time by 4.8 seconds. Additionally, the platform shows superior disturbance rejection capabilities, quickly recovering to target temperatures. The straightforward design and fabrication process ensures adaptability across various MEMS heater geometries, underscoring the approach’s potential for advanced thermal management applications in MEMS devices.