A strong tracking extended Kalman observer for nonlinear discrete-time systems
A strong tracking extended Kalman observer for nonlinear discrete-time systems
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DOI：
10.1109/9.780419
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发表时间：
1999-08-01
影响因子：
6.8
通讯作者：
Aubry, D
中科院分区：
文献类型：
--
作者：
Boutayeb, M;Aubry, D
在这篇文章中，作者表明，如何扩展卡尔曼滤波器（EKF），用作非线性离散时间系统的观测器或扩展卡尔曼观测器（EKO），成为一个有用的状态估计器时，任意矩阵，即R（k）和Q（k）的文件中，是适当的选择。作为第一步，他们使用[4]中的线性化技术，该技术包括引入未知对角矩阵以考虑近似误差。结果表明，当满足递减李雅普诺夫函数条件时，EKO的收敛性与辅助矩阵R（k）和Q（k）之间存在线性矩阵不等式（LMI）问题.为了满足所得到的线性矩阵不等式，给出了Q（k）的一种特殊设计。在最坏的情况下，通过数值例子将显示所提出的技术的高性能。
In this contribution the authors show how the extended Kalman filter (EKF), used as an observer for nonlinear discrete-time systems or extended Kalman observer (EKO), becomes a useful state estimator when the arbitrary matrices, namely R(k) and Q(k) in the paper, are adequately chosen, As a first step, they use the linearization technique in [4] which consists of introducing unknown diagonal matrices to take the approximation errors into account. It is shown that the decreasing Lyapunov function condition leads to a linear matrix inequality (LMI) problem, which points out the connection between a good convergence behavior of the EKO and the instrumental matrices R(k) and Q(k). In order to satisfy the obtained LMI, a particular design of Q(k) is given, High performances of the proposed technique will be shown through numerical examples under the worst conditions.