Optimized Type 2 Neuro-Fuzzy Control for Robust Orbital Maneuvers under Hall-Effect Actuator Faults and Uncertainty

Mansoury, Mahdi; Ghaemi, Sehraneh; Mir Mazhari Anvar, Samira; Farhid, Morteza

doi:10.22034/jsst.2026.1561

Optimized Type 2 Neuro-Fuzzy Control for Robust Orbital Maneuvers under Hall-Effect Actuator Faults and Uncertainty

Document Type : Original Research Paper

Authors

Mahdi Mansoury ¹

Sehraneh Ghaemi ¹

Samira Mir Mazhari Anvar ²

Morteza Farhid ²

¹ Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

² Space Trusters Research Institute, Iranian Space Research Center, Tabriz, Iran

10.22034/jsst.2026.1561

Abstract

Orbital transfer utilizing Hall-effect thrusters (HETs) faces significant challenges due to inherent thrust instabilities and nonlinear actuator faults. This study presents a novel application of an optimized Type‑2 neuro‑fuzzy controller specifically designed to ensure robust maneuvering under severe uncertainty. Unlike prior studies that are primarily restricted to mild fault conditions and limited controller comparisons, the proposed approach is systematically evaluated against a type‑1 neuro‑fuzzy controller and a conventional linear quadratic regulator. A neural network initializes the control structure, while a genetic algorithm is used solely for fuzzy rule reduction and acceleration of the training process, thereby improving computational efficiency without direct controller tuning. Numerical simulations demonstrate that, under sustained high‑amplitude and high‑frequency fault scenarios, the proposed Type‑2 controller preserves accurate orbital tracking and system stability. In contrast, the comparative methods exhibit significant performance degradation. These results establish a reliable and fault‑tolerant control framework for low‑Earth orbit missions in which conventional robust control strategies prove inadequate.

Keywords

Neuro-fuzzy controller

Type-2 neuro-fuzzy control

Hall thruster

Orbital transfer maneuver

Genetic algorithm

Subjects

Electrical Engineering, Control

Article Title Persian

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

Author Persian

سحرانه قائمی ¹

Abstract Persian

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.

Keywords Persian

الگوریتم ژنتیک

مانور انتقال مداری

کنترل کننده عصبی فازی

بهینه سازی

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