با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Adaptive generalized ZEM-ZEV feedback guidance for planetary landing via a deep reinforcement learning approach
راهنمای بازخورد انطباقی کلی شده ZEM-ZEV برای فرود سیاره ای از طریق یک رویکرد یادگیری تقویت عمیق-2020 Precision landing on large and small planetary bodies is a technology of utmost importance for future human and
robotic exploration of the solar system. In this context, the Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV)
feedback guidance algorithm has been studied extensively and is still a field of active research. The algorithm,
although powerful in terms of accuracy and ease of implementation, has some limitations. Therefore with this
paper we present an adaptive guidance algorithm based on classical ZEM/ZEV in which machine learning is used
to overcome its limitations and create a closed loop guidance algorithm that is sufficiently lightweight to be
implemented on board spacecraft and flexible enough to be able to adapt to the given constraint scenario. The
adopted methodology is an actor-critic reinforcement learning algorithm that learns the parameters of the abovementioned
guidance architecture according to the given problem constraints. Keywords: Optimal landing guidance | Deep reinfocement learning | Closed-loop guidance |
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