基于能量最优的敏捷遥感卫星在轨任务规划
收稿日期: 2016-07-28
修回日期: 2016-11-14
网络出版日期: 2016-11-21
基金资助
国家自然科学基金（61273081）；黑龙江省博士后科研启动金（LBH-Q14054）；中央高校基本科研业务费专项资金（HEUCFD1503）
Energy-optimal in orbit mission planning for agile remote sensing satellites
Received date: 2016-07-28
Revised date: 2016-11-14
Online published: 2016-11-21
Supported by
National Natural Science Foundation of China (61273081);Postdoctoral Scientific Research Developmental Fund of Heilongjiang Province of China (LBH-Q14054);the Fundamental Research Funds for the Central Universities of China (HEUCFD1503)
针对敏捷遥感卫星对多个离散观测点在轨自主任务规划问题，在考虑姿态运动方程耦合性的基础上，将问题分解为空间资源调度问题和连续最优控制问题，进而提出了一种结合伪谱法和遗传算法的混合求解算法。该算法针对基于行商问题（TSP）模型建立的空间资源调度问题模型，选用二维编码结构对观测顺序和相对观测时间进行实数编码，并采用遗传算法求解观测序列和观测时间；针对判断观测时间可行性时涉及的时间最优控制问题、以及姿态转移过程中涉及的最小能量消耗问题，将其归结为连续最优控制问题，并基于Gauss伪谱协态变量映射定理，采用Gauss伪谱法进行求解。通过与基于单纯遗传算法的规划算法进行对比试验，本文所提出的基于伪谱法和遗传算法的混合求解策略针对目标问题，在典型工况下姿态转移过程中能量消耗降低60%。
赵琳 , 王硕 , 郝勇 , 刘源 . 基于能量最优的敏捷遥感卫星在轨任务规划[J]. 航空学报, 2017 , 38(6) : 320654 -320654 . DOI: 10.7527/S1000-6893.2016.0298
A new hybrid algorithm combining pseudospectral method and genetic algorithm is presented in this work to solve the in orbit autonomous mission planning problem for the agile remote sensing satellite at multiple discrete observation points. The problem is broken into space resource scheduling problem and continuous optimal control problem based on the coupling of attitude motion equations. This algorithm, according to the space resource scheduling model built based on the travelling salesman problem (TSP) model, encodes the observation sequence and the relative observation time by a two-dimensional real coding structure, and calculates the observation sequence and the observation time by the genetic algorithm. The time optimal control problem in judging the observation time feasibility and the minimal energy consumption in attitude maneuvering are considered as the continuous optimal control problem, which is then solved by Gauss pseudospectral method based on Gauss pseudospectral costate mapping theorem. A comparative simulation test is carried out for the simple genetic algorithm and the proposed algorithm. The simulation results show that the energy consumption obtained by the proposed algorithm is reduced by 60% compared with that obtained by the simple genetic algorithm under typical simulation conditions.
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