International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12

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 Volume 4, Issue 12 (December 2017), Pages: 117-124

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 Original Research Paper

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

 Author(s): Nor’asnilawati Salleh 1, Salwani Mohd Daud 2, *, Sharizal Fadlie Sabri 1, Nur Syazarin Natasha Abd Aziz 2

 Affiliation(s):

 1Space System Operational and Development Division, National Space Agency of Malaysia, Selangor, Malaysia
 2Advanced Informatics School, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

 https://doi.org/10.21833/ijaas.2017.012.022

 Full Text - PDF          XML

 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. 

 © 2017 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Thermal vacuum chamber, Temperature control, PID, Global criterion genetic algorithm

 Article History: Received 9 July 2017, Received in revised form 25 September 2017, Accepted 5 October 2017

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2017.012.022

 Citation:

 Salleh N, Daud SM, Sabri SF, and Aziz NSNA (2017). Temperature control method optimization with global criterion genetic algorithm in thermal vacuum chamber for satellite testing. International Journal of Advanced and Applied Sciences, 4(12): 117-124

 Permanent Link:

 http://www.science-gate.com/IJAAS/V4I12/Salleh.html

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 References (12)

  1. Deb K (2012). Optimization for engineering design: Algorithms and examples. PHI Learning, New Delhi, India.  
  2. Loh CSX and Teo KTK (2009). Design and development of an auto-tuning proportional-integral-derivative controller using genetic algorithm. In the 2nd Seminar on Engineering and Information Technology, Kota Kinabalu, Sabah, Malaysia: 70-74.     
  3. Mirzal A, Yoshii S, and Furukawa M (2012). PID parameters optimization by using genetic algorithm. ISTECS Journal, 8(arXiv: 1204.0885): 34-43. Available online at: https://arxiv.org/ftp/arxiv/papers/1204/1204.0885.pdf 
  4. Murray-Smith DJ (2015). Testing and validation of computer simulation models: Principles, methods and applications. Springer, Berlin, Germany. https://doi.org/10.1007/978-3-319-15099-4 
  5. Nadia N and Luiza DMM (2007). System engineering using particle swarm optimisation. Nova Science Publishers, Inc., New York. USA.     
  6. Rahairi RM (2012). Multi-objective optimization of pid controller parameters using genetic algorithm, Ph.D. Dissertation, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.     
  7. Robert H (2012). Temperature control of space simulation chamber. M.Sc. Thesis, KTH Electrical Engineering, Stockholm, Sweden.     
  8. Saad MS, Jamaluddin H, and Darus IZM (2012). Implementation of PID controller tuning using differential evolution and genetic algorithms. International Journal of Innovative Computing, Information and Control, 8(11): 7761-7779.     
  9. Salleh N, Daud SM, Sabri SF, Ahmad NA, Shariff SA, Yusof YM, and Adam MZ (2016). Enhancing temperature control method of thermal vacuum chamber for satellite testing using optimization algorithm: A review. Jurnal Teknologi (Sciences and Engineering), 78(5–7): 1–6. https://doi.org/10.11113/jt.v78.8688 
  10. Srinivas N and Deb K (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3): 221-248. https://doi.org/10.1162/evco.1994.2.3.221 
  11. Stuart B (1994). Real-time computer control: An introduction. Prentice Hall PTR, Upper Saddle River, USA.     
  12. Waldemar K (2006). International encyclopedia of ergonomics and human factors. Taylor and Francis, Abingdon, UK.