一类不确定环境下的再入滑翔飞行器轨迹规划  

Trajectory planning of re-entry gliding vehicle in a class of uncertain environment

作　　者：田牧垠 沈作军[1] TIAN Muyin;SHEN Zuojun(School of Aeronautic Science and Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学航空科学与工程学院,北京100191

出　　处：《北京航空航天大学学报》2024年第8期2514-2523,共10页Journal of Beijing University of Aeronautics and Astronautics

摘　　要：再入飞行器的飞行过程需要跨越从临近空间到地面的广大区域。在此过程中,即便是微小的建模误差和外界扰动也有可能导致飞行器偏离原先目标点或超出约束边界。为使结果更加鲁棒,对一类不确定环境下的再入飞行器轨迹规划方法进行研究,并引入了数据驱动鲁棒优化的概念以处理不确定性,提出一种数据驱动鲁棒优化轨迹规划方法。所提方法的核心思想是利用不确定参数的历史数据,动态构造不确定集合,再用鲁棒优化的方法对包含该集合的问题进行求解。所提方法相比于传统的鲁棒优化或机会约束优化有两大优势:一是无须有关不确定参数分布或范围的先验知识,也无须其符合特定形式;二是通过在线构造数据驱动的支持向量簇,令优化结果不至于过于保守。为提高计算效率,根据再入优化问题的特点进一步对所提方法进行定制。给出了所提方法数值仿真结果与传统方法对比,以说明所提方法的有效性。The flight process of reentry vehicles requires traversing a vast area from the near space to the ground.During this process,even minor modeling errors and external disturbances can lead to deviations from the original target point or exceed the constraint boundaries.To enhance the robustness of the results,this paper investigates a trajectory planning method for reentry vehicles under uncertain environments and introduces the concept of data-driven robust optimization to address uncertainties.A data-driven robust optimization trajectory planning approach is proposed.The core idea of the proposed method is to dynamically construct uncertainty sets using historical data of uncertain parameters and then solve the problem incorporating these sets using robust optimization techniques.Compared to traditional robust optimization or chance-constrained optimization,the proposed method offers two significant advantages:First,it does not require prior knowledge about the distribution or range of uncertain parameters,nor does it demand that they conform to a specific form.Second,by constructing datadriven support vector clusters online,the optimization results are less conservative.To improve computational efficiency,the method is further tailored according to the characteristics of reentry optimization problems.Numerical simulation results are presented and compared with traditional methods to demonstrate the effectiveness of the proposed approach.

关 键 词：再入 轨迹规划 数据驱动 鲁棒优化 不确定性 

分 类 号：V448.235[航空宇航科学与技术—飞行器设计] V412.44[兵器科学与技术—武器系统与运用工程] TJ765[理学—运筹学与控制论] O224[理学—数学]