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A novel approach for optimal trajectory design with multiple operation modes of propulsion system, part 1

机译:一种新的推进系统多功能模式的最佳轨迹设计方法,第1部分

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Equipping a spacecraft with multiple solar-powered electric engines (of the same or different types) compounds the task of optimal trajectory design due to presence of both real-valued inputs (power input to each engine in addition to the direction of thrust vector) and discrete variables (number of active engines). Each engine can be switched on/off independently and "optimal" operating power of each engine depends on the available solar power, which depends on the distance from the Sun. Application of the Composite Smooth Control (CSC) framework to a heliocentric fuel-optimal trajectory optimization from the Earth to the comet 67P/Churyumov-Gerasimenko is demonstrated, which presents a new approach to deal with multiple-engine problems. Operation of engine clusters with 4, 6, 10 and even 20 engines of the same type can be optimized. Moreover, engine clusters with different/mixed electric engines are considered with either 2, 3 or 4 different types of engines. Remarkably, the CSC framework allows us 1) to reduce the original multi-point boundary-value problem to a two-point boundary-value problem (TPBVP), and 2) to solve the resulting TPBVPs using a single-shooting solution scheme and with a random initialization of the missing costates. While the approach we present is a continuous neighbor of the discontinuous extremals, we show that the discontinuous necessary conditions are satisfied in the asymptotic limit. We believe this is the first indirect method to accommodate a multi-mode control of this level of complexity with realistic engine performance curves. The results are interesting and promising for dealing with a large family of such challenging multi-mode optimal control problems.
机译:用多个太阳能电动发动机(相同或不同类型)的宇宙飞船化合物,由于存在实际值输入(除了推力向量的方向)以及除了推力向量的方向上的电源输入)和离散变量(有源引擎的数量)。每个发动机可以独立接通/断开,并且每个发动机的“最佳”操作功率取决于可用的太阳能,这取决于距离太阳的距离。将复合光滑控制(CSC)框架应用于从地球到彗星67P / Churyumov-Gerasimenko的高兴燃料最佳轨迹优化,这提出了一种处理多发动机问题的新方法。可以优化带有4,6,10甚至20发动机的发动机簇的操作。此外,具有不同/混合电动发动机的发动机簇用2,3或4种不同类型的发动机考虑。值得注意的是,CSC框架允许我们1)将原始的多点边值问题(TPBVP)和2)将原始的多点边值问题(TPBVP)和2)缩短,以使用单次拍摄解决方案方案来解决结果的TPBVPS缺失成本的随机初始化。虽然我们所呈现的方法是不连续极值的连续邻居,但我们表明渐近极限满足了不连续的必要条件。我们相信这是第一种间接方法,以适应与现实发动机性能曲线的这种复杂程度的多模式控制。结果是处理大家庭如此挑战的多模式最佳控制问题的有趣和有趣。

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