航天器转动惯量参数在轨辨识的最优激励

根据矩阵分析理论研究了转动惯量参数的可辨识性问题,从姿态动力学方程导出关于未知参数的线性回归模型,将回归矩阵的条件数作为参数可辨识度的定量指标,以可辨识度最大为目标函数建立最优控制模型;应用解最优控制问题的伪谱法,计算控制力矩陀螺的指令轨线,使得惯量参数的辨识精度和收敛速度显著提高;在仿真中采用双无迹卡尔曼滤波算法进行参数辨识,结果验证了所提出的最优激励设计方法可以改善参数辨识性能,同时该方法对先验信息误差具有鲁棒性。     

Estimating component characteristics from system failure‐time data

Suppose that failure times are available from a random sample of N systems of a given, fixed design with components which have i.i.d. lifetimes distributed according to a common distribution F. The inverse problem of estimating F from data on observed system lifetimes is considered. Using the known relationship between the system and component lifetime distributions via signature and domination theory, the nonparametric maximum likelihood estimator _N(t) of the component survival function (t) is identified and shown to be accessible numerically in any application of interest. The asymptotic distribution of _N(t) is also identified, facilitating the construction of approximate confidence intervals for (t) for N sufficiently large. Simulation results for samples of size N = 50 and N = 100 for a collection of five parametric lifetime models demonstrate the utility of the recommended estimator. Possible extensions beyond the i.i.d. framework are discussed in the concluding section. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010