面向高超声速飞行器双重不确定性的自适应状态估计  

Adaptive State Estimate of Hypersonic Vehicle System with Dual Uncertainties

作　　者：冯肖雪 刘萌 李笑宇 潘峰[1,2] FENG Xiao-xue;LIU Meng;LI Xiao-yu;PAN Feng(School of Automation,Beijing Institute of Technology,Beijing 100081,China;Kunming-BIT Industry Technology Research Institute INC,Kunming 650106,China)

机构地区：[1]北京理工大学自动化学院,北京100081 [2]北京理工大学昆明产业技术研究院,昆明650106

出　　处：《宇航学报》2020年第12期1561-1570,共10页Journal of Astronautics

基　　金：国家自然科学基金(61603040,61433003);云南省基础研究计划项目(201701CF00037);云南省科技厅重点研发计划工业领域(2018BA070);广东省科技创新战略专项资金(skjtdzxrwqd2018001)。

摘　　要：将高超声速飞行器双重不确定性因素建模为未知干扰输入项,针对状态演化方程和量测方程含有不同未知干扰输入的高超声速飞行器控制系统状态估计问题开展研究,提出一种基于自适应方差极小化的递推状态估计器(Adaptive-variance-minimization-based Recursive Estimator,AVMRE)。首先建立了状态估计递推滤波器模型,实现滤波误差中的量测未知干扰解耦,之后引入自适应调整因子刻画状态未知干扰并推导了最小上界估计误差协方差矩阵,最后,基于最小方差估计准则设计了滤波器中的量测增益反馈矩阵。以外部突风和传感器故障为例,受内外部双重不确定性因素影响下的高超声速飞行器仿真实验验证了本文算法的有效性,与相关算法的仿真对比反映了本文算法的优越性。The internal and external uncertainties of a hypersonic vehicle are modeled as unknown disturbances in this paper.The state estimate of the hypersonic vehicle system in which the state equation and the measurement equation contain different unknown disturbance inputs is concerned here.The adaptive-variance-minimization-based recursive estimator is proposed.Firstly,the recursion model of the filter is established,which realizes the measurement unknown disturbance decoupling from the estimate error.Then,the adaptive adjust factor is introduced to characterize the unknown state disturbance,and the minimum upper bound of the estimate error covariance matrix is obtained.Finally,the measurement gain feedback matrix in the recursive filter is deduced based on the minimum variance estimation criterion.The simulation results of the hypersonic vehicle system subject to the gusty wind and sensor fault verify the effectiveness of the proposed method.The comparison results with the related methods show the superiority of the proposed method.

关 键 词：高超声速飞行器 未知干扰输入 自适应方差极小化 状态估计 干扰解耦 

分 类 号：V249[航空宇航科学与技术—飞行器设计]