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接近观测是一种新兴的天基观测手段,精确地导引飞行器到达目标附近合适的观测点是关键技术之一。针对接近观测最后逼近段制导问题,提出了一种最优末制导方法。在惯性系中建立了相对运动方程,把目标和观测点的相对位置作为终端约束,引入了综合考虑飞行时间与燃料消耗要求的性能指标,从而把接近目标的过程转化为最优控制问题,求解出最优推力方向、发动机工作时间和飞行时间。在此基础上,设计了最优制导方案。仿真分析表明最优末制导方法的精度较高,能够满足抵近观察任务的要求,同时对深空探测、交会对接等任务具有借鉴意义。 相似文献
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《防务技术》2022,18(9):1513-1522
It is important to understand how ballistic materials respond to impact from projectiles such that informed decisions can be made in the design process of protective armour systems. Ballistic testing is a standards-based process where materials are tested to determine whether they meet protection, safety and performance criteria. For the V50 ballistic test, projectiles are fired at different velocities to determine a key design parameter known as the ballistic limit velocity (BLV), the velocity above which projectiles perforate the target. These tests, however, are destructive by nature and as such there can be considerable associated costs, especially when studying complex armour materials and systems. This study proposes a unique solution to the problem using a recent class of machine learning system known as the Generative Adversarial Network (GAN). The GAN can be used to generate new ballistic samples as opposed to performing additional destructive experiments. A GAN network architecture is tested and trained on three different ballistic data sets, and their performance is compared. The trained networks were able to successfully produce ballistic curves with an overall RMSE of between 10 and 20 % and predicted the V50 BLV in each case with an error of less than 5 %. The results demonstrate that it is possible to train generative networks on a limited number of ballistic samples and use the trained network to generate many new samples representative of the data that it was trained on. The paper spotlights the benefits that generative networks can bring to ballistic applications and provides an alternative to expensive testing during the early stages of the design process. 相似文献
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