引用本文:
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 726次   下载 415 本文二维码信息
码上扫一扫!
分享到: 微信 更多
基于高斯伪谱法的火星表面上升燃耗最优轨迹设计
柯森锎1,2, 李爽1,2, 肖东东3, 王卫华3, 聂钦博3
1.南京航空航天大学 航天学院, 南京 210016;2.南京航空航天大学 航天新技术实验室, 南京 210016;3.上海航天控制技术研究所, 上海 201109
摘要:
火星上升器的设计与其轨迹设计紧密相关,而现有方法大都将MAV(Mars Ascent Vehicle)的分级优化和轨迹优化解耦计算,其计算效率低且鲁棒性较差。提出了一种基于高斯伪谱法的两级MAV分级与轨迹耦合多阶段优化算法,它以MAV的发射总质量最小为目标函数,并考虑了MAV设计约束、MAV的质量模型约束、轨迹的路径约束和控制约束等限制条件。利用该方法可以同时求得发射总质量最小的两级MAV分级参数和一条燃耗最优的上升轨迹,解决了由于不合理的两级MAV分级设计导致的轨迹优化算法无法收敛的问题。数值仿真结果表明该方法具有较快的收敛速度,且对初值选取的敏感度较小、具有较强的鲁棒性。
关键词:  火星采样返回;火星上升器;火箭分级和轨迹耦合优化;高斯伪谱法
DOI:10.15982/j.issn.2095-7777.2018.6.009
分类号:V448.2
基金项目:国家自然科学基金资助项目(11672126)
Minimum-Fuel Mars Ascent Trajectory Design Based on Gauss Peseudospectral Method
KE Senkai1,2, LI Shuang1,2, XIAO Dongdong3, WANG Weihua3, NIE Qinbo3
1.College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2.Laboratory of Space New Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;3.Shanghai Aerospace Control Technology Institute, Shanghai 201109, China
Abstract:
Mars Ascent Vehicle(MAV)design is closely related to trajectory design. However,the existing method mostly decoupled the staging optimization and trajectory optimization,resulting in computational inefficiency and poor robustness. A coupled staging-trajectory multiphase optimization algorithm is proposed in this paper for a two-stage MAV design. Considering given vehicle,mission,path and control constraints,the objective of the optimization algorithm is to minimize the Gross Lift-off Mass(GLOM)of the two-stage MAV,and the algorithm is based on Gauss pseudospectral method. By using this algorithm,an optimal staging design is obtained,and an optimal trajectory is designed to minimize propellant consumption simultaneously. And the algorithm solves the problem that trajectory optimization algorithm couldn't converge due to the unreasonable MAV staging design. In addition,numerical simulations show that the coupled staging-trajectory multiphase optimization algorithm have good robustness and strong convergence.
Key words:  Mars sample return(MSR);Mars ascent vehicle(MAV);coupled staging-trajectory optimization;Gauss pseudospectral method(GPM)