RECURSIVE IDENTIFICATION OF NONLINEAR SYSTEMS BY MEANS OF THE EXTENDED KALMAN FILTER

机译：通过扩展的卡尔曼滤波器递归非线性系统的识别

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The Extended Kalman Filter (EKF) is a time domain identification procedure suitable to identify linear and nonlinear systems. This recursive algorithm consists of a predictor and a corrector part and estimates parameters and states simultaneously. The method is applied to a simple rotor with nonlinear behaviour due to a cracked shaft. At first, the dynamical behaviour of the rotor under different operating conditions and crack depths is simulated and unknown parameters are identified. A test rig was built in order to be able to apply the EKF to measured data, too. Furthermore, the seat suspension system of a truck is investigated.

机译：扩展卡尔曼滤波器（EKF）是适于识别线性和非线性系统的时域识别过程。该递归算法包括预测器和校正器部分，并同时估计参数和状态。该方法应用于具有由于裂纹轴的非线性行为的简单转子。首先，模拟在不同操作条件下的转子的动态行为和裂缝深度是模拟的，并且识别出未知的参数。建立了测试钻机，以便能够将EKF应用于测量数据。此外，研究了卡车的座椅悬架系统。

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《International Modal Analysis Conference》|2004年||共8页
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S. Seibold; C.-P. Fritzen;
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中图分类 TP391.4-53;
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1. The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise [J] . Wang Xuehai, Zhu Fang, Ding Feng International Journal of Adaptive Control and Signal Processing . 2020,第10期

机译：基于修改的扩展卡尔曼基于Wiener非线性系统具有过程噪声和测量噪声的递归估计
2. A novel unscented Kalman filter for recursive state-input-system identification of nonlinear systems [J] . Lei Ying, Xia Dandan, Erazo Kalil, Mechanical systems and signal processing . 2019,第JULa15期

机译：非线性系统递归状态输入系统辨识的新型无味卡尔曼滤波器
3. A novel unscented Kalman filter for recursive state-input-system identification of nonlinear systems [J] . Lei Ying, Xia Dandan, Erazo Kalil, Mechanical systems and signal processing . 2019,第Jul15期

机译：一种用于非线性系统的递归状态输入系统识别的新型无需卡尔曼滤波器
4. RECURSIVE IDENTIFICATION OF NONLINEAR SYSTEMS BY MEANS OF THE EXTENDED KALMAN FILTER [C] . S. Seibold, C.-P. Fritzen International Modal Analysis Conference . 2004

机译：通过扩展的卡尔曼滤波器递归非线性系统的识别
5. An adaptive Kalman filter design using recursive maximum likelihood identification. [D] . Kelly, Donald Andrew. 1990

机译：使用递归最大似然识别的自适应卡尔曼滤波器设计。
6. Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising [O] . Taku Nonomura, Hisaichi Shibata, Ryoji Takaki -1

机译：基于扩展卡尔曼滤波器的动态模式分解可同时进行系统识别和去噪
7. Recursive Identification of Continuous Time Variant Dynamical Systems with the Extended Kalman Filter and the Recursive Least Squares State-Variable Filter [O] . Flávio Luiz Rossini, Guilherme Santos Martins, João Paulo Silva Gonçalves, 2018

机译：使用扩展卡尔曼滤波器和递归最小二乘滤波器递归识别连续时间变体动态系统
8. Nonlinear Filtering, the Link between Kalman and Extended Kalman Filters. [R] . Emara-Shabaik, H. E. 1982

机译：非线性滤波，卡尔曼与扩展卡尔曼滤波器之间的联系。

RECURSIVE IDENTIFICATION OF NONLINEAR SYSTEMS BY MEANS OF THE EXTENDED KALMAN FILTER

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