Enhancing Temperature Control Method of Thermal Vacuum Chamber for Satellite Testing Using Optimization Algorithm: A Review

Salleh, Norhafizah; Daud, Salwani Mohd; Sabri, Sharizal Fadlie; Shariff, Sya Azmeela; Yusof, Yudariah Mohammad; Adam, Mohamad Zulkefli

doi:10.11113/jt.v78.8688

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…Among the optimization algorithms that had been used by other researchers, GA is found to be the most widely used and it is also suitable for tuning the PID controller (Salleh et al, 2016). Nevertheless, in this study, there are a few objectives that need to be achieved.…”

Section: Investigationmentioning

confidence: 99%

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

Salleh

2017

Int. j. adv. appl. sci.

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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.

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Section: Investigationmentioning

confidence: 99%

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

Salleh

2017

Int. j. adv. appl. sci.

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“…The thermal vacuum test, which provides an environment close to a vacuum and simulates high temperature caused by solar irradiation as well as low temperature without the sun [1], is an important procedure in the development of spacecraft and loads to test their ultimate working performance [2][3][4]. The thermal vacuum chamber (TVC) is the most important equipment of the TVT, and can provide an environment close to vacuum by extracting air.…”

Section: Introductionmentioning

confidence: 99%

Grouping Neural Network-Based Smith PID Temperature Controller for Multi-Channel Interaction System

Li,

Yang,

et al. 2024

Electronics

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The thermal vacuum test (TVT) is an important verification process in the development of spacecraft and load. There are often multiple temperature points on the device under test (DUT) that require control. The interaction among multiple channels poses a challenge for temperature control in the TVT. To solve this problem, a multi-channel Smith proportional–integral–derivative (PID) controller based on a grouping neural network (Grouping-NN) is proposed. Firstly, the mathematical derivation for a typical multi-channel temperature control model of the TVT is carried out. Then, the multi-channel interaction system is identified using a Grouping-NN to predict the output temperature of each channel by grouping the hidden layer neurons according to the number of channels. Finally, two Grouping-NNs are utilized to update the Smith predictor, and the time-delay error is fed back to the PID controller, which is used to optimize the control effect of the multi-channel interaction system under high time delay. The proposal is compared with the traditional PID controller and Smith predictor-based PID controller through simulation. The simulation results show that the proposed method has better suppression of overshooting. In addition, the algorithm is verified by controlling the temperature of six channels in a practical thermal vacuum test.

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Enhancing Temperature Control Method of Thermal Vacuum Chamber for Satellite Testing Using Optimization Algorithm: A Review

Cited by 2 publications

References 27 publications

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

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

Grouping Neural Network-Based Smith PID Temperature Controller for Multi-Channel Interaction System

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