USING AN ADAPTIVE HIGH-GAIN EXTENDED KALMAN FILTER WITH A CAR EFFICIENCY MODEL

被引：0
|
作者：
Sebesta, Kenneth D. [1 ]
Boizot, Nicolas [1 ]
Busvelle, Eric
Sachau, Juergen [1 ]
机构：
[1] Univ Luxembourg, Luxembourg, Luxembourg
来源：
PROCEEDINGS OF THE ASME DYNAMIC SYSTEMS AND CONTROL CONFERENCE 2010, VOL 1 | 2010年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The authors apply the Adaptive High-Gain Extended Kalman Filter (AEKF) to the problem of estimating engine efficiency with data gathered from normal driving. The AEKF is an extension of the traditional Kalman Filter that allows the filter to be reactive to perturbations without sacrificing noise filtering. An observability normal form of the engine efficiency model is developed for the AEKE The continuous-discrete AEKF is presented along with strategies for dealing with asynchronous data. Empiric test results are presented and contrasted with EKF-derived results.
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页码：899 / 906
页数：8
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