کنترل عصبی-فازی نوع ۲ بهینه‌ برای مانورهای مداری مقاوم تحت خطاهای عملگر اثر هال و عدم قطعیت

قائمی, سحرانه

doi:10.22034/jsst.2026.1561

کنترل عصبی-فازی نوع ۲ بهینه‌ برای مانورهای مداری مقاوم تحت خطاهای عملگر اثر هال و عدم قطعیت

نوع مقاله : مقالة‌ پژوهشی‌

نویسنده

سحرانه قائمی

10.22034/jsst.2026.1561

چکیده

انتقال مداری با استفاده از پیشرانه‌های هال (HET) با چالش‌های قابل توجهی مواجه است، به‌ویژه به دلیل ناپایداری‌های ذاتی در نیروی محرکه و خطاهای غیرخطی در اکچواتورها. این مطالعه یک کاربرد نوآورانه از کنترل‌کننده فازی عصبی نوع ۲ را ارائه می‌دهد که به‌طور خاص برای اطمینان از مانورهای مقاوم در شرایط عدم‌قطعیت شدید طراحی شده است. برخلاف مطالعات قبلی که عمدتاً به شرایط خطای خفیف و مقایسه محدود کنترل‌کننده‌ها محدود می‌شوند، رویکرد پیشنهادی به‌طور سیستماتیک در مقایسه با یک کنترل‌کننده فازی عصبی نوع ۱ و یک تنظیم‌کننده خطی متعارف ارزیابی می‌شود. یک شبکه عصبی، ساختار کنترل را آغاز می‌کند، در حالی که یک الگوریتم ژنتیک تنها برای کاهش قواعد فازی و تسریع فرآیند آموزش استفاده می‌شود، و بنابراین کارایی محاسباتی را بدون تنظیم مستقیم کنترل‌کننده بهبود می‌بخشد. شبیه سازی های عددی نشان می‌دهد که در سناریوهای خطا با دامنه بالا و فرکانس بالا، کنترل‌کننده نوع ۲ پیشنهادی دقت ردیابی مداری و پایداری سیستم را حفظ می‌کند. در مقابل، روش‌های مقایسه‌ای عملکرد قابل توجهی ضعیف‌تر را نشان می‌دهند. این نتایج یک چارچوب کنترلی قابل اعتماد و مقاوم در برابر خطا برای مأموریت‌های مدار پایین زمین ارائه می‌دهد که در آن استراتژی‌های کنترلی مقاوم متعارف ناکافی هستند.

کلیدواژه‌ها

الگوریتم ژنتیک

مانور انتقال مداری

کنترل کننده عصبی فازی

بهینه سازی

اثر هال

موضوعات

مهندسی برق، کنترل

[1] J. A. Starek, B. Açıkmeşe, I. A. Nesnas, and M. Pavone, "Spacecraft autonomy challenges for next-generation space missions," in Advances in Control System Technology for Aerospace Applications, E. Feron, Ed. Heidelberg, Berlin, Springer, 2015, pp. 1-48. https://doi.org/10.1007/978-3-662-47694-9_1.

[2] N. Li, H. Sun, and Q. Zhang, "Robust passive adaptive fault tolerant control for stochastic wing flutter via delay control," European Journal of Control, vol. 48, pp. 74-82, 2019, https://doi.org/10.1016/j.ejcon.2019.04.008.

[3] A. A. Amin and K. Mahmood-ul-Hasan, "Robust passive fault tolerant control for air fuel ratio control of internal combustion gasoline engine for sensor and actuator faults," IETE Journal of Research, vol. 69, no. 5, pp. 2846-2861, 2023, https://doi.org/10.1080/03772063.2021.1906767.

[4] M. Leomanni, A. Garulli, A. Giannitrapani, and F. Scortecci, "Propulsion options for very low Earth orbit microsatellites," Acta Astronautica, vol. 133, pp. 444-454, 2017, https://doi.org/10.1016/j.actaastro.2016.11.001.

[5] S. Ueda and A. Noumi, "Precise rendezvous guidance in low earth orbit via machine learning," in International Symposium on Control Systems (SICE ISCS), Kumamoto, Japan, 2019, pp. 27-32, https://doi.org/10.23919/SICEISCS.2019.8758738.

[6] M. Saied, B. Lussier, I. Fantoni, H. Shraim, and C. Francis, "Active versus passive fault-tolerant control of a redundant multirotor UAV," The Aeronautical Journal, vol. 124, no. 1273, pp. 385-408, 2020, https://doi.org/10.1017/aer.2019.149.

[7] D. O’Reilly, G. Herdrich, and D. F. Kavanagh, "Electric propulsion methods for small satellites: A review," Aerospace, vol. 8, no. 1, 2021, Art. no. 22, https://doi.org/10.3390/aerospace8010022.

[8] J. D. Stefanovski, "Passive fault tolerant perfect tracking with additive faults," Automatica, vol. 87, pp. 432-436, 2018, https://doi.org/10.1016/j.automatica.2017.09.011.

[9] M. Pontani and M. Pustorino, "Nonlinear Earth orbit control using low-thrust propulsion," Acta Astronautica, vol. 179, pp. 296-310, 2021, https://doi.org/10.1016/j.actaastro.2020.10.037.

[10] M. Kiantaj, M. Farhid, and M. Shameli, "Robust model predictive control for satellite orbit maintenance using hall effect thrusters," Journal of Technology in Aerospace Engineering, vol. 8, no. 4, pp. 27-38, 2024, (in Persian), https://doi.org/10.22034/jtae.2024.8.4.3.

[11] M. Pontani and M. Pustorino, "Nonlinear Earth orbit control using low-thrust propulsion," Acta Astronautica, vol. 179, pp. 296-310, 2021, https://doi.org/10.1016/j.actaastro.2020.10.037.

[12] M. Mansoury, M. A. Badamchizadeh, and H. Kharrati, "Stochastic Model Predictive Control Based on Online Learning for a Class of Nonlinear Constrained Systems," in 32nd International Conference on Electrical Engineering (ICEE), Tehran, Iran, 2024, pp. 1-7, https://doi.org/10.1109/ICEE63041.2024.10667954.

[13] Y. Gui, Q. Jia, H. Li, and Y. Cheng, "Reconfigurable fault-tolerant control for spacecraft formation flying based on iterative learning algorithms," Applied Sciences, vol. 12, no. 5, 2022, Art. no. 2485, https://doi.org/10.3390/app12052485.

[14] Y. Mu, H. Zhang, R. Xi, Z. Wang, and J. Sun, "Fault-tolerant control of nonlinear systems with actuator and sensor faults based on T–S fuzzy model and fuzzy observer," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 9, pp. 5795-5804, 2021, https://doi.org/10.1109/TSMC.2021.3131495.

[15] D. Izzo, M. Märtens, and B. Pan, "A survey on artificial intelligence trends in spacecraft guidance dynamics and control," Astrodynamics, vol. 3, no. 4, pp. 287-299, 2019, https://doi.org/10.1007/s42064-018-0053-6.

[16] J. P. Zand, J. Katebi, and S. Yaghmaei-Sabegh, "A hybrid clustering-based type-2 adaptive neuro-fuzzy forecasting model for smart control systems," Expert Systems with Applications, vol. 239, 2024, Art. no. 122445, https://doi.org/10.1016/j.eswa.2023.122445.

[17] S. Danyali, M. Babaeifard, M. Shirkhani, A. Azizi, J. Tavoosi, and Z. Dadvand, "A new neuro-fuzzy controller based maximum power point tracking for a partially shaded grid-connected photovoltaic system," Heliyon, vol. 10, no. 17, 2024, Art. no. e36747, https://doi.org/10.1016/j.heliyon.2024.e36747.

[18] A. Safari and S. Ghaemi, "NeuroFuzzyMan: A hybrid neuro-fuzzy BiLSTM stacked ensemble model for financial forecasting and analysis: Dataset case studies on JPMorgan, AMZN and TSLA," Expert Systems with Applications, vol. 266, 2025, Art. no. 126037, https://doi.org/10.1016/j.eswa.2024.126037.

[19] J. Tavoosi, A. Mohammadzadeh, and K. Jermsittiparsert, "A review on type-2 fuzzy neural networks for system identification," Soft Computing, vol. 25, no. 10, pp. 7197-7212, 2021, https://doi.org/10.1007/s00500-021-05686-5.

[20] A. Attar, M. A. Badamchizadeh, and S. Ghaemi, "Designing of type-2 fuzzy formation controller for a class of nonlinear multiagent system using JAYA algorithm," in 32nd International Conference on Electrical Engineering (ICEE), Tehran, Iran, 2024, pp. 1-8, https://doi.org/10.1109/ICEE63041.2024.10667744.

[21] A. Aran, M. Mansoury, S. Ghaemi, and J. Katebi, "Designing a fuzzy-based hybrid control system to reduce structure vibration using rooftop tank and MR damper," Journal of Civil and Environmental Engineering, vol. 55, no. 1, pp. 69-83, 2025, (in Persian), https://doi.org/10.22034/CEEJ.2025.62705.2374.

[22] M. Kiantaj, M. Farhid, and M. Shameli, "Robust model predictive control for satellite orbit maintenance using hall effect thrusters," Journal of Technology in Aerospace Engineering, vol. 8, no. 4, pp. 27-38, 2024, (in Persian), https://doi.org/10.22034/jtae.2024.8.4.3.

[23] A. R. Fazlyab, F. Fani Saberi, and M. Kabganian, "Fault-tolerant attitude control of the satellite in the presence of simultaneous actuator and sensor faults," Scientific Reports, vol. 13, no. 1, 2023, Art. no. 20802, https://doi.org/10.1038/s41598-023-48243-w.

[24] L. Zhao, Z. Lu, W. Liao, T. Liu, K. Ling, and K. Zheng, "Fault-tolerant control for satellite autonomous rendezvous with quality characteristics and actuator uncertainties," Aerospace Science and Technology, vol. 150, 2024, Art. no. 109182, https://doi.org/10.1016/j.ast.2024.109182.

[25] M. Leomanni, G. Bianchini, A. Garulli, and A. Giannitrapani, "State feedback control in equinoctial variables for orbit phasing applications," Journal of Guidance, Control, and Dynamics, vol. 41, no. 8, pp. 1815-1822, 2018. https://doi.org/10.2514/1.G003402.

[26] M. Leomanni, G. Bianchini, A. Garulli, A. Giannitrapani, and R. Quartullo, "Orbit control techniques for space debris removal missions using electric propulsion," Journal of Guidance, Control, and Dynamics, vol. 43, no. 7, pp. 1259-1268, 2020, https://doi.org/10.2514/1.G004735.

[27] A. Garulli, A. Giannitrapani, M. Leomanni, and F. Scortecci, "Autonomous low-Earth-orbit station-keeping with electric propulsion," Journal of Guidance, Control, and Dynamics, vol. 34, no. 6, pp. 1683-1693, 2011, https://doi.org/10.2514/1.52985.

[28] E. Jahanbazi, F. Jahangiri, and M. R. Mohammadi, "Neural network based fault tolerant LQR control for orbital maneuvering in LEO satellites using Hall effect thrusters," AUT Journal of Modeling and Simulation, vol. 55, no. 1, pp. 171-182, 2023, https://doi.org/10.22060/miscj.2023.22482.5326.

[29] M. F. Shehzad, A. B. Asghar, M. H. Jaffery, K. Naveed, and Z. Čonka, "Neuro-fuzzy system based proportional derivative gain optimized attitude control of CubeSat under LEO perturbations," Heliyon, vol. 9, no. 10, 2023, Art. no. e20434, https://doi.org/10.1016/j.heliyon.2023.e20434.