不变结构半弹道式再入飞行器的建模与多模型方法估计

梁勇奇; 韩崇昭; 孙耀杰; 林燕丹; 杨永安

doi:10.3724/SP.J.1004.2011.00700

不变结构半弹道式再入飞行器的建模与多模型方法估计

doi: 10.3724/SP.J.1004.2011.00700

1.
西安交通大学航天学院西安 710049;
2.
西安交通大学智能网络与网络安全教育部重点实验室、机械制造系统工程国家重点实验室, 电子信息与工程学院综合自动化研究所西安 710049;
3.
复旦大学光源与照明工程系先进照明技术教育部工程研究中心上海 200433;
4.
西安卫星测控中心西安 710043

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出版历程
- 收稿日期: 2010-02-08
- 修回日期: 2010-07-26
- 刊出日期: 2011-06-20

Modeling and Multiple-model Estimation of Invariable-structure Semi-ballistic Reentry Vehicle

1.
Xi'an Jiaotong University, Xi'an 710049;
2.
Key Laboratory for Intelligent Networks and Network Security of Ministry of Education, State Key Laboratory for Manufacturing Systems Engineering, Institute of Integrated Automation, School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049;
3.
Department of Light Sources and Illuminating Engineering, Engineering Research Center of Advanced Lighting Technology, Ministry of Education, Fudan University, Shanghai 200433;
4.
Xi'an Satellite Control Center, Xi'an 710043

摘要

摘要: 以配平攻角状态再入的不变结构半弹道式再入飞行器(Semi-ballistic reentry vehicle, SBRV)不同于传统的弹道式再入飞行器(Ballistic reentry vehicle, BRV)和机动再入飞行器(Maneuvering reentry vehicle, MaRV), 本文分析了该飞行器再入特征, 提出了新的模型并分析了该模型与传统再入模型间的关系. 对该再入问题的多模型混合状态估计器引入了F-均匀模型集与期望模式补偿(Expected-mode augmentation, EMA)集. 根据SBRV的圆柱体状模式空间的需求, 文中扩展了现有的方法以设计F-均匀模型集, 进而提出一种EMA集的实现形式. 前者在分布最小失配意义下使估计器最优; 后者相比于前者具有更高的估计精度. 仿真结果表明, 相比于传统Monte-Carlo法生成的模型集, 在模型集势相当的情况下这两种模型集对不变结构SBRV再入的初始阶段有更高的模式估计的精度, 在该飞行器状态变化剧烈时有更高的混合状态估计精度.
- 半弹道式再入飞行器 /
- 多模型方法 /
- 模型集设计 /
- F均匀模型集 /
- 期望模式补偿集 /
- Monte-Carlo方法
Abstract: A trim flying semi-ballistic reentry vehicle (SBRV), with invariable structure during reentry process, is different from the conventional ballistic reentry vehicle (BRV) or the maneuvering reentry vehicle (MaRV). Characteristics of the vehicle are analyzed in this paper, then the SBRV model is proposed and compared to the conventional models used for reentry vehicles. For the multiple-model hybrid state estimator of the reentry problem, the F-uniform model set and the expected-mode augmentation (EMA) set are applied. According to SBRV's cylinder mode space, the present design method is expanded for the F-uniform model set, then an EMA approach is proposed. The former optimizes the estimator in the sense of minimum distribution mismatch and the latter has higher accuracy than the former. Simulation results show that the two model sets have higher accuracy in mode estimation for the initial part of the invariable-structure SBRV's reentry process and have higher hybrid state estimation accuracy when the vehicle's state changes greatly, as compared with the model set generated by the conventional Monte-Carlo method.
- Semi-ballistic reentry vehicle (SBRV) /
- multiple-model approach /
- model-set design /
- F-uniform model set /
- expected-mode any mentation (EMA) set /
- Monte-Carlo method

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