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题名:
空间飞行器编队重构建模与规划方法研究
作者: 王抒雁
答辩日期: 2008-06-04
导师: 李磊
专业: 计算机应用技术
授予单位: 中国科学院研究生院
授予地点: 中国科学院软件研究所
学位: 博士
关键词: 空间飞行器 ; 编队飞行 ; 编队重构 ; 轨道规划 ; 进化计算 ; 多轨道规划 ; 协调规划 ; 多目标优化
其他题名: Research on Modeling and Planning for Spacecraft Formation Reconfiguration
分类号: 暂无
索取号: 暂无
部门归属: 综合信息系统技术国家级重点实验室
摘要: 编队重构是实现空间飞行器编队飞行的一项关键技术,也是自动导航和控制的重要研究领域,无论在理论研究还是实际应用上都具有重大意义。本论文针对空间飞行器编队重构问题展开了深入的研究:(1)编队重构建模;(2)空间飞行器多轨道规划;(3)空间飞行器时间-燃耗的轨道规划;(4)编队重构的协同规划;(5)考虑燃料均衡的编队重构规划。围绕编队重构建模问题,本论文在分析其特点的基础上,完善了传统的单目标优化模型,加入了现有研究通常忽略的发动机推力的精度约束,提高了模型精度以降低控制反馈误差。另外,还根据航天任务的特点,从多目标优化的角度建模了问题,优化的性能指标包括变轨时间、编队的总燃料消耗和飞行器间的燃料均衡。在多轨道规划的研究中,作者基于进化计算和参数优化方法,提出了一种空间飞行器多轨道规划算法。通过将可行轨道按其空间分布分类,并采用一种多轨道保持技术,使新提出的算法能够获得多条在空间中分布较为离散的最优/近优轨道,并可以方便地根据环境和任务要求设定希望获得的轨道之间的差异。该算法充分利用了编队飞行相对动力学方程的解耦性,简化了问题,提高了计算速度,并使最终轨道的选择更加灵活多样。作者还针对空间飞行器的变轨时间与燃料消耗的关系问题展开了研究,基于小生境进化算法,提出了一种时间-燃耗的轨道规划方法。该方法采用一种变长实值染色体编码方式和特定的种群初始化方法,能使生成的轨道满足各种约束条件,并引入等值分享法保证优秀个体具有较大的选中概率和前沿的多样性。实验表明,该算法能有效地搜索到飞行器变轨的时间-燃耗前沿,一次规划生成多个Pareto最优解,为任务制定者选择最合适的变轨方案提供可靠的依据。在编队的协同规划方面,通过分解空间飞行器编队重构问题,并与协同进化的思想相结合,提出了一种两层结构的飞行器编队重构规划算法。高层算法通过优化构型映射来优化编队的总燃料消耗,实现全局规划并确保飞行器之间保持一定的安全距离以避免相互碰撞;低层规划算法采用多轨道规划方法为各飞行器规划满足约束条件的轨道。该算法不仅实现了多颗飞行器的轨道子种群间的协同进化,还实现了高、低层规划结果的协同进化。由于利用编队的分布式结构实现了并行计算,该算法能解决大型编队重构的协同规划问题。虽然给出的最优解是唯一的,但在规划过程中为各飞行器都生成了多条散布在空间中的轨道,提供了可替换解以保证编队重构任务的顺利执行。最后本论文从多目标优化的角度建模了带燃料均衡的多飞行器编队重构规划问题,初步研究了变轨时间、燃料消耗和飞行器间的燃料均衡三个重要指标之间的关系。通过将进化计算与问题领域的知识相结合,提出了一种最优轨道规划方法,能从上述三个指标的角度分别评价一个变轨方案的最优性,并且计算量较小,一次规划能提供多个最优解决方案,能用于实际规划之前进行简单的估算,十分适合方案设计阶段应用。
英文摘要: As one of the key issues of spacecraft formation flying, formation reconfiguration is an important research field of automatic navigation and control. It has a great significance both theoretically and practically. This dissertation addresses the following formation reconfiguration problems: (1) Modeling of formation reconfiguration; (2) Multi-Trajectory planning for spacecraft; (3) Time-Fuel trajectory planning for spacecraft; (4) Cooperative planning for formation reconfiguration; (5) Planning with fuel-balancing for formation reconfiguration. In the modeling of formation reconfiguration, this dissertation perfects the traditional single-objective model based on its attributes, adding the control precision constraint of thrusters to improve the accuracy of the model and to reduce the control feedback error. Furthermore, the multi-objective model of the problem is proposed based on the characteristics of space missions. The objectives include the transfer time, the total fuel consumption and the fuel-balancing between spacecraft. In the research of multi-trajectory planning, a new planner – EMTP (Evolutionary Multiple Trajectory Planner) is presented. By categorizing feasible trajectories according to their distribution in the space and using a retaining method to record multiple trajectories, the algorithm can generate multiple separated trajectories simultaneously, which are optimal or near optimal. The difference of the desired trajectories can be inputted according to the environment and the requirement of missions. The decoupling of the dynamic model is fully used to simplify the problem, decrease the computing time and improve the diversity of the final trajectory selection. Based on niched evolutionary computation, a new time-fuel trajectory planning algorithm is presented. Combining the concepts of evolutionary computation with problem-specific representation of candidates and genetic operators, the generated trajectories can satisfy various constraints. Equivalence class sharing is introduced to guarantee higher selected probability of better individuals and the diversity of the front. The algorithm can find the time-fuel front of the trajectory planning problem effectively. Multiple Pareto solutions can be generated simultaneously to help the decision maker to select the most appropriate solution. In addition, the cooperative planning problem for formation reconfiguration is addressed. The problem is decomposed into two sub-problems and a hierarchical evolutionary algorithm is proposed. The high-level planner performs global planning while ensuring collision avoidance between spacecraft. The low-level planners design multiple optimal or near optimal trajectories which are separated one from another for each spacecraft by parameterizing the controls in terms of Chebyshev polynomials. In the new planner, not only the coevolution of the trajectory sub-populations, but also the coevolution of the high-level and the low-level planners, is achieved. The distributed structure of formation flying is fully used to perform parallel computations. The approach scales well with the number of spacecraft. Although only the best solution is outputted as the final one, multiple separated trajectories for each spacecraft are generated and can be used as alternatives to ensure the execution of the mission. At last, the spacecraft formation reconfiguration problem is modeled as a multi-objective problem and a niched evolutionary algorithm is presented. The relationship of the transfer time, the fuel consumption and the fuel-balancing are investigated simply. Combining the concepts of evolutionary computation with problem specific knowledge, the approach can evaluate one maneuver plan from the above three objectives point of view and generate multiple Pareto optimal maneuver plans simultaneously. It has a small computation cost and is propitious to the simple evaluation of the transfer time and fuel cost.
内容类型: 学位论文
URI标识: http://ir.iscas.ac.cn/handle/311060/6420
Appears in Collections:综合信息系统技术国家级重点实验室 _学位论文

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Recommended Citation:
王抒雁. 空间飞行器编队重构建模与规划方法研究[D]. 中国科学院软件研究所. 中国科学院研究生院. 2008-06-04.
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