@inproceedings{9129b809768744b189652090ed87a30f,
title = "State Estimation and Fault Detection for Nonlinear Dynamic Systems",
abstract = "In practical problems, the dynamic systems are usually nonlinear. In this case, the traditional Kalman filter cannot be used for state estimation or fault detection. The two typical extension based on Kalman filtering framework is the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Theoretically speaking, UKF is better than EKF when estimation accuracy is concerned, especially for high degree nonlinear cases. This paper is concerned with the state estimation and fault detection problem for a class of nonlinear dynamic systems. A novel fault detection and analyse method is presented based on the period residual of EKF and UKF. For different kind of faults, mainly, the system parameter error, the sensor/data error, EKF and UKF are used, and the estimation and fault detection effects are compared and analyzed.",
keywords = "Fault detection, Nonlinear system, Parameter error, Sensor error, State estimation",
author = "Baohua Lu and Liping Yan and Hongxue Chen and Yuanqing Xia and Mengyin Fu and Bo Xiao",
note = "Publisher Copyright: {\textcopyright} 2018 Technical Committee on Control Theory, Chinese Association of Automation.; 37th Chinese Control Conference, CCC 2018 ; Conference date: 25-07-2018 Through 27-07-2018",
year = "2018",
month = oct,
day = "5",
doi = "10.23919/ChiCC.2018.8483892",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "6038--6043",
editor = "Xin Chen and Qianchuan Zhao",
booktitle = "Proceedings of the 37th Chinese Control Conference, CCC 2018",
address = "United States",
}