| 基于合作进化算法的月面返回再入轨迹优化设计 |
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| 引用本文： | 王峰波,董长虹. 基于合作进化算法的月面返回再入轨迹优化设计[J]. 北京航空航天大学学报, 2014, 40(5): 629-634. DOI: 10.13700/j.bh.1001-5965.2013.0376 |
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| 作者姓名： | 王峰波 董长虹 |
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| 作者单位： | 1. 北京航空航天大学 航空科学与工程学院, 北京 100191; |
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| 摘 要： | 在考虑热流、过载、动压以及开伞点参数等多种约束条件下，针对低升阻比返回舱月面返回再入轨迹优化设计问题，基于分段线性倾侧角参数化策略，提出了采用合作进化算法进行参数优化的策略来实现再入轨迹的快速高精度优化设计.首先采用以能量为自变量的分段线性倾侧角控制参数化策略，将连续最优控制问题转化为有限维参数寻优问题，然后基于逃逸粒子群算法和自适应差分进化算法的合作进化算法求解该问题.数值仿真验证了倾侧角参数化策略的正确性，对比试验分析表明合作进化算法较传统进化算法有更快的收敛速度和更高的优化精度的综合性能，更加适合月面返回再入轨迹优化设计问题的求解.
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| 关 键 词： | 月面返回 轨迹优化 合作进化算法 逃逸粒子群算法 自适应差分进化 |
| 收稿时间： | 2013-06-27 |
Reentry trajectory optimization design for lunar return through coevolutionary algorithm |
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| Wang Fengbo,Dong Changhong. Reentry trajectory optimization design for lunar return through coevolutionary algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(5): 629-634. DOI: 10.13700/j.bh.1001-5965.2013.0376 |
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| Authors: | Wang Fengbo Dong Changhong |
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| Affiliation: | 1. School of Aeronautic Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;2. School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100191, China |
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| Abstract: | Reentry trajectory optimization with multiple constraints on g-load, dynamic pressure, heat flux on stagnation point of craft and parachute deployment position was studied for low-lift-to-drag lunar return vehicle, and a novel coevolutionary algorithm was presented to solve the parameters optimization problem based on the piece-wise linear bank modulation strategy. Firstly, a piece-wise linear bank modulation versus energy policy was introduced to convert the continuous optimal problem into a finite-dimensional parameter optimization problem. Then, the coevolutionary algorithm consists of escapable particle swarm optimization algorithm and adaptive differential evolution algorithm was employed to solve it. Numerical simulation demonstrates the feasibility of the adopted control parameterization strategy. A performance comparative case was carried out. The coevolutionary algorithm proves to be effective with great accuracy and is well suited for reentry trajectory optimal profile design. |
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