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首页> 外文期刊>Journal of guidance, control, and dynamics >Automated Design of Multiphase Space Missions Using Hybrid Optimal Control
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Automated Design of Multiphase Space Missions Using Hybrid Optimal Control

机译:基于混合最优控制的多相空间飞行任务自动化设计

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摘要

A modern space mission is assembled from multiple phases or events such as impulsive maneuvers, coast arcs, thrust arcs, and planetary flybys. Traditionally, a mission planner would resort to intuition and experience to develop a sequence of events for the multiphase mission and to find the space trajectory that minimizes propellant use by solving the associated continuous optimal control problem. This strategy, however, will most likely yield a suboptimal solution, as the problem is sophisticated for several reasons. For example, the number of events in the optimal mission structure is not known a priori, and the system equations of motion change depending on what event is current. In this work a framework for the automated design of multiphase space missions is presented using hybrid optimal control. The method developed uses two nested loops: an outer-loop that handles the discrete dynamics and finds the optimal mission structure in terms of the categorical variables, and an inner-loop that performs the optimization of the corresponding continuous-time dynamical system and obtains the required control history. Genetic algorithms and direct transcription with nonlinear programming are introduced as methods of solution for the outer-loop and inner-loop problems, respectively. Automation of the inner-loop, continuous optimal control problem solver required two new technologies. The first is a method for the automated construction of the nonlinear programming problems resulting from the use of a transcription method for systems with different structures, including different numbers of categorical events. The method assembles modules, consisting of parameters and constraints appropriate to each event, sequentially according to the given mission structure. The other new technology is for a robust initial guess generator required by the inner-loop nonlinear programming problem solver. The method, based on a real genetic algorithm, approximates optimal control histories by incorporating boundary conditions explicitly using a conditional penalty function. The solution of representative multiphase mission design problems shows the effectiveness of the methods developed.
机译:现代太空任务是由多个阶段或事件(例如脉冲机动,海岸弧,推力弧和行星飞越)组成的。传统上,任务计划者会凭直觉和经验来开发多阶段任务的一系列事件,并通过解决相关的连续最优控制问题找到使推进剂使用最少的空间轨迹。但是,由于该问题由于多种原因而复杂,因此该策略很可能会产生次优的解决方案。例如,最优任务结构中的事件数量不是先验已知的,运动的系统方程式取决于当前事件是什么。在这项工作中,提出了使用混合最优控制的多相空间飞行任务自动化设计框架。开发的方法使用两个嵌套循环:一个用于处理离散动力学并根据分类变量找到最佳任务结构的外循环,以及一个用于对相应的连续时间动力学系统进行优化并获得相关信息的内循环。所需的控制历史记录。介绍了遗传算法和带有非线性规划的直接转录作为分别解决外环和内环问题的方法。内环,连续的最优控制问题求解器的自动化需要两项新技术。第一种是用于自动构造非线性编程问题的方法,该非线性编程问题是由于对具有不同结构(包括不同数量的分类事件)的系统使用了转录方法而导致的。该方法根据给定的任务结构顺序地组装模块,该模块由适合于每个事件的参数和约束组成。另一项新技术是用于内环非线性编程问题求解器所需的强大的初始猜测生成器。该方法基于真实的遗传算法,通过使用条件惩罚函数显式合并边界条件来近似最佳控制历史。代表性的多阶段任务设计问题的解决方案表明了所开发方法的有效性。

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  • 来源
    《Journal of guidance, control, and dynamics》 |2013年第5期|1410-1424|共15页
  • 作者单位

    University of Illinois, Urbana, Illinois 61801 Department of Aerospace Engineering,104 South Wright St. MC-236;

    University of Illinois, Urbana, Illinois 61801 Department of Aerospace Engineering, 104 South Wright St.MC-236;

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