状态—参数联合估计方法及其在液体火箭发动机健康监控中的应用(英文)

The Joint Estimation Approach of States and Parameters for Liquid Rocket Engine Health Monitoring

基于传统的扩展卡尔曼滤波器（ＥＫＦ），本文提出了一种带次优渐消因子的ＥＫＦ用于非线性时变随机动态系统状态与参数的联合估计。应用于液体火箭发动机健康监控算法的仿真研究表明，本文所提出的联合估计器具有较好的收敛性、实时性和动态跟踪能力。此外，文中还讨论了联合估计器应用于实际系统的有关问题。

Based on a coventional Extended Kalman Filter(EKF),a suboptimal fading factor EKF is proposed in this paper, which can be used for the joint estimation of states and parameters of nonlinear timevarying stochastic systems. It is used for health monitoring in such a complex system as liquid rocket engine.Numerical simulation result shows the proposed estimator has better properties such as convergence,real time,and dynamic tracking ability etc..In addition,some problems connected with the joint estimation and the applicability for real plants are also discussed.