نویسندگان

دانشگاه صنعتی مالک اشتر، مجتمع دانشگاهی هوافضا

چکیده

در این مقاله، یک روش بهینه‌سازی مقاوم برای حل مسئله طراحی مسیر ماهواره‌بر در حضور عدم قطعیت‌ها با استفاده از الگوریتم قدرتمند بهینه‌سازی ازدحام ذرات (PSO) توسعه داده شده است. با توجه به وجود عدم قطعیت‌هایی چون نامعینی در مقادیر واقعی ضرایب آیرودینامیکی، نیروی تراست موتور و جرم در مرحله صعود یک ماهواره‌بر، دستیابی به مسیر بهینه‌ای که نسبت به این عدم قطعیت‌ها مقاوم باشد حائز اهمیت است، چراکه منجر به بهبود عملکرد پروازی، کاهش بار کاری سیستم هدایت-کنترل و افزایش قابلیت اطمینان ماهواره‌بر می‌شود. لذا برای این منظور، ابتدا مسئله بهینه‌سازی با بکارگیری معیار حداکثرسازی جرم محموله به‌عنوان تابع هزینه و معادلات حرکت سه بعدی به‌عنوان قیود حاکم بر مسئله درنظر گرفته شده است. سپس با اضافه کردن پارامترهای میانگین و انحراف استاندارد عدم قطعیت‌ها، مدل بهینه‌ساز مقاوم توسعه‌یافته و از الگوریتم مذکور جهت بهینه‌سازی عددی مدل مزبور استفاده شده است. همچنین به‌منظور تحلیل نتایج عدم‌قطعیت‌ها و بازخورد مستمر آن به مدل بهینه‌ساز، از دیدگاه مونت‌کارلو استفاده شده است. در نهایت مسیر بهینه‌ای به‌دست آمده که نسبت به عدم‌قطعیت‌های مزبور، مقاوم است. نتایج شبیه‌سازی حاصله، صحت این ادعا را نشان می‌دهد

کلیدواژه‌ها

عنوان مقاله [English]

Robust Optimal Trajectory Design of a Launch Vehicle Using Particle Swarm Optimization

نویسندگان [English]

  • R. Zardashti
  • S. A. Saadatdar Arani
  • S. M. Hosseini

چکیده [English]

In this paper, a robust optimization method is developed to solve the Satellite Launch Vehicle (SLV) trajectory design problem in the presence of uncertainties using a powerful Particle Swarm Optimization (PSO) algorithm. Given the uncertainties such as uncertainties in the actual values ​​of aerodynamic coefficients, engine thrust, and mass in the ascent phase of a SLV, it is important to achieve an optimal trajectory that is robust to these uncertainties; because it improves the flight performance, reduces the workload of the guidance-control system, and increases the reliability of the satellite. For this purpose, first the optimization problem is considered by using the criterion of minimizing the flight time of the SLV as a cost function, and three-dimensional equations of motion as constraints governing the problem. Then, by adding the mean parameters and the standard deviation of uncertainties in the cost function, a robust optimizer model is developed and the algorithm is used to numerically optimize the model. Monte Carlo's perspective has also been used to analyze the results of uncertainties and their continuous feedback to the optimization model. Finally, the optimal trajectory is obtained that is robust to the uncertainties. The resulting simulation results show the accuracy of this claim.

کلیدواژه‌ها [English]

  • Robust optimization
  • Trajectory design
  • Uncertainty
  • Particle swarm optimization algorithm
  • Satellite launch vehicle
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