مدلسازی زیرسیستم تأمین توان ماهواره سنجش از دور برپایه سیستم‌های فازی چند ورودی-چند خروجی

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسنده

عضو هیئت علمی ، دپارتمان مهندسی مکانیک، دانشکده فنی انقلاب اسلامی، دانشگاه فنی و حرفه ای استان تهران، ایران .

چکیده

از دیرباز، مسئله مدل‌سازی و تحلیل سیستم‌ها خصوصاً در سیستم‌های پیچیده با دینامیک بالا همراه با نویز و عدم‌قطعیت در شناخت رفتار سیستم‌ها و تصمیم‌گیری، بسیار بااهمیت می‌باشد. در طراحی سیستم فازی مورد نظر از چهار متغیر به‌عنوان ورودی و دو متغیر به‌ عنوان خروجی استفاده می‌شود. قوانین فازی برمبنای تجربه فرد خبره در طراحی زیرسیستم تأمین توان الکتریکی ماهواره سنجش از دور ارائه می‌گردد؛ لذا انتظار می‌رود نتایج عملی‌ و منطقی‌تری براساس رفتار واقعی سیستم به‌دست آید. طراحی سیستم فازی پیشنهادی توانایی مدل‌کردن اطلاعات کیفی یک طراح خبره را نیز داراست. نتایج شبیه‌سازی به‌دست‌آمده در طراحی مفهومی دارای دقت مناسبی در مقایسه با داده‌های تجربی ماهواره‌های سنجش از دور می‌باشند. همچنین نتایج نشان می‌دهند که سیستم‌های فازی می‌توانند برای طراحی زیرسیستم تأمین توان الکتریکی یک ماهواره سنجش از دور به‌طور مؤثری مورد استفاده قرار گیرند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Electric Power Subsystem Modeling of a Remote Sensing Satellite Based on Multi-Input and Output Fuzzy Systems

نویسنده [English]

  • Morteza Ramezani
Faculty Member, Department of Mechanical Engineering, Faculty of Enghelab, Technical and Vocational University (TVU), Tehran, Iran.
چکیده [English]

Modeling and analysis of systems, particularly in complex systems with noise and uncertainty in behavior recognition and decision making of systems, is of vital importance. In this article, such system problems were solved to the greatest degree possible based on fuzzy theory. This fuzzy system has four input variables and two output variables. Since the fuzzy rules were obtained from an experienced expert in Electric Power Subsystem (EPS) design, the results were expected to be more practical and logical in comparison with the real behavior of the subsystem. The proposed fuzzy system can model the qualitative data of an expert. The simulated results had suitable accuracy in conceptual design compared with the practical data of remote sensing satellites. The results also showed that fuzzy systems can be used effectively to design the EPS of a remote sensing satellite.

کلیدواژه‌ها [English]

  • satellite conceptual design
  • Fuzzy Systems
  • Multi-input and multi-output fuzzy
  • remote sensing satellite
  • Electric power subsystem (EPS)
  1. Wertz, J. R., Everett, D.F., Puschell, J.J.(2011), Space Mission Engineering: The New SMAD, Microcosm Press.
  2. Pisacane, V. L.(2005), Fundamentals of Space Systems, Oxford University Press.
  3. Ley, W., Wittmann, K., Hallmann, W.(2008), Handbook of Space Technology, John Wiley and Sons.
  4. Larson, W. J., Kirkpatrick, D., Sellers, J. J., Thomas, L. D., Verma, D.(2009), Applied Space Systems Engineering, Space Technology Series.
  5. Wang, L.X.(1997), A Course in Fuzzy Systems and Control, Prentice Hall International Inc.
  6. Cheng C., Shu, S., Cheng, P.(2009), Attitude control of a satellite using fuzzy controllers, Expert Systems with Applications, 36, 6613–6620.
  7. Azizi Oroumieh, M.A., Malaek, M.B., Ashrafizaadeh, M., Taheri, S.M.(April-May 2013), Aircraft design cycle time reduction using artificial intelligence, Vol.26(1), 244-258.
  8. Montazeri-Gh, M., Safari, A.(2011), Tuning of fuzzy fuel controller for aero-engine thrust regulation and safety considerations using genetic algorithm, Aerospace Science and Technology, 15, 183–192.
  9. Shahi Ashtiani, M.A., Malaek, S.M.B.(2008), Optimum selection of ‘number of seats/cargo volume’ for transport in uncertain business environment, AIAA Journal of Aircraft, 45, 98–105.
  10. Navvabi M., Davoodi A., Rehanolu M.(2020), Optimum fuzzy sliding mode control of fuel sloshing in a spacecraft using PSO algorithm , ACTA ASTRONAUTICA, Vol.167, pp.331-342.
  11. Navvabi M., Davoodi A.(2018), Fuzzy Control of Fuel Sloshing in a Spacecraft , In 6th Iranian Joint Congress on Fuzzy and Intelligent Systems
  12. Navvabi M., Rajabalifardi M.(2018), Quaternion Based Fuzzy Gain Scheduled PD Law for Spacecraft Attitude Control , In 6th Iranian Joint Congress on Fuzzy and Intelligent Systems.
  13. Wilamowski B.M., Xiangli L.(2002), Fuzzy system based maximum power point tracking for PV system, IEEE 2002 28th Annual Conference of the Industrial Electronics Society, 5-8 Nov.
  14. Taherbaneh M, Menhaj M.B.(2007), A Fuzzy-Based Maximum Power Point Tracker for Body Mounted Solar Panels in LEO Satellites, 2007 IEEE/IAS Industrial & Commercial Power Systems Technical Conference, 6-11 May.
  15. Mingliang S., Baolong Z., Ruoming A., Huimin S., Shengzhong X., Zhenhua Y.(2019), Data-driven fault diagnosis of satellite power system using fuzzy Bayes risk and SVM, Aerospace Science and Technology, Volume 84, Pages 1092-1105.
  16. Mirshams M., Teshnehlab M., Ramezani M.(2018), Modeling the solar array design of remote sensing satellites based on adaptive neuro-fuzzy inference system, Journal of Space Science Technology, Vol.1, No.1 36, 1-8 (in Persian).
  17. Dabiri M., Safari B.(2016), Application of Integrated Intuitive Fuzzy Hierarchy Process (IFAHP) method in prioritizing new disciplines in each region in the Technical and Vocational University of Iran, Quarterly Journal of Technical and Vocational University Karafan, Vol.13, No.40, 63-75 (in Persian).
  18. Masoumnejad M., Yastibalaghi A., Narimanzade N.(2020), Estimate the travel path of an overhead crane using a fuzzy UHF filter, Quarterly Journal of Technical and Vocational University Karafan, Vol.17, No.1, 123-142 (in Persian).
  19. W. Wang, F. Ismail, A.F. Golnaraghi(2004), A neuro-fuzzy approach to gear system monitoring, IEEE Transactions on Fuzzy Systems 12 (5), 710–723
  20. Marza, D. Seyyedi, L.F. Capretz(2008), Estimation development time of software projects using a neuro fuzzy approach, World Academy of Science, Engineering and Technology 22, 575–579.
  21. A.V. Topalov, E. Kayacan, Y. Oniz, O. Kaynak(2009), Adaptive neuro-fuzzy control with sliding mode learning algorithm: application to antilock braking system, 7th Asian Control Conference, Hong Kong, China, pp. 784–789.
  22. U. Farooq, M.S. Khan, K. Ahmed, M.A. Saeed, S. Abbas(2011), Autonomous system controller for vehicles using neuro-fuzzy, International Journal of Scientific &Engineering Research 2 (6).
  23. Jang, J-S. R.,Sun, C-T., Mizutani, E.(1997), Neuro-Fuzzy and Soft Computing, Prentice Hall International Inc.
  24. https://eoportal.org/web/eoportal/satellite-missions.