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首页> 外文期刊>Journal of guidance, control, and dynamics >Evolutionary Neurocontrol: A Novel Method for Low-Thrust Gravity-Assist Trajectory Optimization
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Evolutionary Neurocontrol: A Novel Method for Low-Thrust Gravity-Assist Trajectory Optimization

机译:进化神经控制:一种低推力重力辅助弹道优化的新方法

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摘要

The combination of low-thrust propulsion and gravity assists to enhance deep-space missions has proven to be a remarkable task. In this paper, we present a novel method that is based on evolutionary neurocontrollers. The main advantage in the use of a neurocontroller is the generation of a control law with a limited number of decision variables. On the other hand, the evolutionary algorithm allows one to look for globally optimal solutions more efficiently than with a systematic search. In addition, a steepest-ascent algorithm is introduced that acts as a navigator during the planetary encounter, providing the neurocontroller with the optimal insertion parameters. Results are presented for a Mercury rendezvous with a Venus gravity assist and for a Pluto flyby with a Jupiter gravity assist.
机译:低推力推进和重力辅助相结合以增强深空任务已被证明是一项艰巨的任务。在本文中,我们提出了一种基于进化神经控制器的新颖方法。使用神经控制器的主要优点是可以生成决策变量数量有限的控制律。另一方面,进化算法比系统搜索更有效地寻找全局最优解。另外,引入了最速上升算法,该算法在行星遭遇期间充当导航器,为神经控制器提供了最佳的插入参数。给出了金星重力辅助下的水星集合点和木星重力辅助下的冥王星掠过的结果。

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