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Fuel-optimal trajectory optimization for lunar soft landing via enhanced simultaneous dynamic optimization approach

机译:通过增强的同时动态优化方法优化月球软着陆的燃油最优轨迹

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In this study, the fuel-optimal trajectory optimization problem of lunar soft landing using variable-thrust propulsion is considered. First, we formulate the trajectory optimization problem of lunar soft landing with three-dimensional kinematics and dynamics, boundary constraints, and path constraints strictly described. The formulated trajectory optimization problem is then solved by the simultaneous dynamic optimization approach. With bounds imposed on the magnitude of the engine thrust, the optimal thrust profile typically has a “bang-bang” profile. The general simultaneous dynamic optimization approach has difficulty handling breakpoints of control profiles. A novel adaptive mesh refinement strategy based on a constant Hamiltonian profile is proposed to address the difficulty of locating breakpoints of the thrust profile. Simulation results show that the enhanced simultaneous dynamic optimization approach with the adaptive mesh refinement strategy can effectively capture breakpoints of the optimal thrust profile and obtain more refined optimal solutions, compared with the general simultaneous dynamic optimization approach.
机译:在这项研究中,考虑了使用可变推力推进的月球软着陆的燃料最优轨迹优化问题。首先,我们用严格的三维运动学,动力学,边界约束和路径约束来制定月球软着陆的轨迹优化问题。然后通过同时动态优化方法解决了制定的轨迹优化问题。在对发动机推力的大小施加限制的情况下,最佳推力曲线通常具有“砰砰”的轮廓。通用的同时动态优化方法难以处理控制配置文件的断点。提出了一种基于恒定哈密顿分布的新型自适应网格细化策略,以解决定位推力分布断点的难题。仿真结果表明,与常规的同时动态优化方法相比,采用自适应网格细化策略的增强的同时动态优化方法可以有效地捕获最优推力剖面的断点,并获得更多的优化最优解。

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