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Vehicle Sideslip Angle Estimation Based on General Regression Neural Network
Joint Authors
Wei, Wang
Shaoyi, Bei
Yongzhi, Wang
Lanchun, Zhang
Kai, Zhu
Weixing, Hang
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-21
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Aiming at the accuracy of estimation of vehicle’s mass center sideslip angle, an estimation method of slip angle based on general regression neural network (GRNN) and driver-vehicle closed-loop system has been proposed: regarding vehicle’s sideslip angle as time series mapping of yaw speed and lateral acceleration; using homogeneous design project to optimize the training samples; building the mapping relationship among sideslip angle, yaw speed, and lateral acceleration; at the same time, using experimental method to measure vehicle’s sideslip angle to verify validity of this method.
Estimation results of neural network and real vehicle experiment show the same changing tendency.
The mean of error is within 10% of test result’s amplitude.
Results show GRNN can estimate vehicle’s sideslip angle correctly.
It can offer a reference to the application of vehicle’s stability control system on vehicle’s state estimation.
American Psychological Association (APA)
Wei, Wang& Shaoyi, Bei& Lanchun, Zhang& Kai, Zhu& Yongzhi, Wang& Weixing, Hang. 2016. Vehicle Sideslip Angle Estimation Based on General Regression Neural Network. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1111980
Modern Language Association (MLA)
Wei, Wang…[et al.]. Vehicle Sideslip Angle Estimation Based on General Regression Neural Network. Mathematical Problems in Engineering No. 2016 (2016), pp.1-7.
https://search.emarefa.net/detail/BIM-1111980
American Medical Association (AMA)
Wei, Wang& Shaoyi, Bei& Lanchun, Zhang& Kai, Zhu& Yongzhi, Wang& Weixing, Hang. Vehicle Sideslip Angle Estimation Based on General Regression Neural Network. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1111980
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1111980