دانلود مقاله ISI انگلیسی شماره 138099
ترجمه فارسی عنوان مقاله

یک روش هدایت مجدد سازگار با در نظر گرفتن تاثیر منطقه خاموشی

عنوان انگلیسی
An adaptive reentry guidance method considering the influence of blackout zone
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
138099 2018 37 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Acta Astronautica, Volume 142, January 2018, Pages 253-264

ترجمه کلمات کلیدی
راهنمایی بازنشستگی، منطقه خاموشی، به روز رسانی مسیر برنامه ریزی مسیر کبوتر الهام بخش بهینه سازی، هوشافزاری الگوریتم هورستیک،
کلمات کلیدی انگلیسی
Reentry guidance; Blackout zone; Trajectory updating; Trajectory planning; Pigeon inspired optimization; Swarm intelligence; Heuristic algorithm;
پیش نمایش مقاله
پیش نمایش مقاله  یک روش هدایت مجدد سازگار با در نظر گرفتن تاثیر منطقه خاموشی

چکیده انگلیسی

Reentry guidance has been researched as a popular topic because it is critical for a successful flight. In view that the existing guidance methods do not take into account the accumulated navigation error of Inertial Navigation System (INS) in the blackout zone, in this paper, an adaptive reentry guidance method is proposed to obtain the optimal reentry trajectory quickly with the target of minimum aerodynamic heating rate. The terminal error in position and attitude can be also reduced with the proposed method. In this method, the whole reentry guidance task is divided into two phases, i.e., the trajectory updating phase and the trajectory planning phase. In the first phase, the idea of model predictive control (MPC) is used, and the receding optimization procedure ensures the optimal trajectory in the next few seconds. In the trajectory planning phase, after the vehicle has flown out of the blackout zone, the optimal reentry trajectory is obtained by online planning to adapt to the navigation information. An effective swarm intelligence algorithm, i.e. pigeon inspired optimization (PIO) algorithm, is applied to obtain the optimal reentry trajectory in both of the two phases. Compared to the trajectory updating method, the proposed method can reduce the terminal error by about 30% considering both the position and attitude, especially, the terminal error of height has almost been eliminated. Besides, the PIO algorithm performs better than the particle swarm optimization (PSO) algorithm both in the trajectory updating phase and the trajectory planning phases.