Control ofa ThermoelectricCooling Module by Metaheuristic Optimization Algorithms

Abstract

In this study, the proportional-integral-derivative (PID) controller of a sample thermoelectric cooler(TEC)system model is optimized using four different metaheuristic optimization algorithms. For this aim, the classical PID and the metaheuristic optimization algorithms as Coronavirus Herd Immunity Optimization (CHIO), Atomic Search Optimization (ASO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO)were used for control of a TEC system. The settling time and maximum overshoot criteria are used to compare performances of the optimized controllers. -20°C is the desired temperature for the cold side of this thermoelectric module. Since TEC systems requirequick cooling, CHIO-PID performs the best because it is the first to reach the set temperature of -20 ̊C in 42 seconds at the 1% band limit.

Keywords

• transfer function
• modelling
• coronavirus herd immunity
• atomic search optimization
• thermoelectric module.