Temperature control method optimization with global criterion genetic algorithm in thermal vacuum chamber for satellite testing

Abstract

The proportional integral derivative (PID) controller that is adopted in the temperature control system of thermal vacuum chamber (TVC) is manually tuned has caused the temperature profile required more time to stabilize and fluctuate during satellite testing. Thus, other method is required to do the tuning of the PID controller. An optimization algorithm is an alternative method that can be applied to do the PID tuning and an optimized system can be developed. In this study, the optimization algorithm that is able to do the PID tuning for temperature control system is investigated in order to be implemented in the TVC's temperature control system. The genetic algorithm (GA) is found to be the suitable method that can be implemented as it is able to optimize the settling time and overshoot very quickly in temperature control system compared to other methods. However, due to more than one objective aimed in this study, the global criterion genetic algorithm (GCGA), a multi objective genetic algorithm (MOGA) method become the best approach to be chosen. Two models were designed using PID controller and GCGA-PID controller for the TVC's temperature control system. Simulation testing is done and the settling time and overshoot value are measured to compare both models. Analysis suggests that the optimization tuning by using GCGA method improves the settling time 30% better than using the PID controller alone. Meanwhile, in terms of overshoot, the performance is increased by almost 99.85%. By applying the optimization algorithm, the TVC's temperature control method can be enhanced during satellite testing compare to the current manually implementation. (C) 2017 The Authors. Published by IASE.