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MULTIOBJECTIVE OPTIMIZATION OF SKIP TRAJECTORY FOR SMV WITH THRUST ENGINE

机译:带推力发动机的SMV跳过轨迹的多目标优化

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With expansion of space operations over the past few decades, the risk of space debris has considerably increased such as collision with asset in space or survival from re-entry which threats human and property on ground and even aviation. Many active debris removal methods have been investigated and in this paper, a debris remediation method is first proposed based on reusable Space Manoeuvre Vehicles (SMV) with skip trajectory. The focus is to perform controlled re-entry. These vehicles are expected to achieve a transatmospheric maneuver with thrust engine. If debris are released at altitude below 80 km, debris could be captured by the atmosphere drag force and this method improves landing precision. Moreover if the debris are released in a cargo at a much lower altitude, this method protects high value space asset from break up by the atmosphere. This paper presents the simulation results of SMV skip trajectory optimization problem for specified mission. The mission profile includes: 1) descent to predetermined altitude (For example, descent from 120 km to 60 km); 2) ascent back to orbit with thrust (For example, exit out of atmosphere from 60 km to 120 km). Considering the global optimality and high-accuracy, the Evolutionary Collocation based on NSGA-II and collocation method is presented which is actually a two-step optimization approach. State variable and control variable are discreted on collocation point. Then NSGA-II is used to give the multi-objective optimization result. Simulation is conducted and different scenarios are compared. The Evolutionary collocation method gives a truthful re-entry trajectory satisfying path constraints and boundary constraints.
机译:随着过去几十年空间活动的扩展,空间碎片的危险性大大增加,如从再入大气层与资产碰撞空间或生存其中威胁人员和财产的地面,甚至航空。许多积极消除碎片的方法进行了研究,并在本文中,碎片补救方法基于可重复使用的空间机动车辆(SMV)与跳跃的轨迹被首次提出。重点是执行受控再入。这些车辆有望实现与推力发动机transatmospheric演习。如果碎片在高空下面80公里发布,碎片可以通过大气阻力被捕获,该方法提高着陆精度。此外,如果碎片在低得多的高度在货物释放,这种方法保护从大气向上突破的高价值空间资产。本文礼物SMV的模拟结果跳过指定的任务轨迹优化问题。任务剖面包括:1)下降到预定高度(例如,从120公里60公里)下降; 2)上升回到轨道与推力(例如,来自60公里到120公里)退出气氛进行。考虑到全局最优和高精确度,提出了一种基于NSGA-II和搭配方法进化搭配这实际上是一个两步优化方法。状态变量和控制变量的离散化的配点。然后NSGA-II是用来给多目标优化的结果。模拟进行的,不同的方案进行比较。进化搭配方法给出了满足路径约束和边界约束如实再入轨迹。

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