面向高超声速飞行器双重不确定性的自适应状态估计

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。

Adaptive State Estimate of Hypersonic Vehicle Systemwith Dual Uncertainties

The internal and external uncertainties of ahypersonic vehicle are modeled as unknown disturbances in this paper. The stateestimate of the hypersonic vehicle system in which the state equation and themeasurement equation contain different unknown disturbance inputs is concernedhere. The adaptive variance-minimization based recursive estimator is proposed. Firstly, the recursion modelof the filter is established, which realizes the measurement unknowndisturbance decoupling from the estimate error. Then, the adaptive adjustfactor is introduced to characterize the unknown state disturbance, and theminimum upper bound of the estimate error covariance matrix is obtained.Finally, the measurement gain feedback matrix in the recursive filter isdeduced based on the minimum variance estimation criterion. The simulation resultsof the hypersonic vehicle system subject to the gusty wind and sensor faultverify the effectiveness of the proposed method. The comparison results withthe related methods show the superiority of the proposed method.