NDLI: An adaptive non-linear state estimator for vehicle lateral dynamics

Content Provider	IEEE Xplore Digital Library
Author	Broderick, D.J. Bevly, D.M. Hung, J.Y.
Copyright Year	2009
Description	Author affiliation: Department of Electrical and Computer Engineering, Auburn University Auburn, Alabama 36849 (Broderick, D.J.; Hung, J.Y.) \|\| Department of Mechanical Engineering, Auburn University Auburn, Alabama 36849 (Bevly, D.M.)
Abstract	Artificial neural networks are used to estimate side slip angle and yaw rate of a vehicle's lateral dynamics. The networks are adapted to varying operating conditions such as a shift in vehicle weight, a change in road surface, and a radical change in tire characteristics. The structure and characteristics of the networks used are detailed. The methods for both offline and online training are described. Adaptation to the changing conditions is investigated with a high fidelity model and evaluated for ability and accuracy. A method of reducing computational burden while preserving model generalization is described. Model accuracy and generalization are examined to evaluate the networks' ability to describe general vehicle behavior. Improvement in estimate error of 3 to 1 and nearly 300 to 1 for two typical scenarios is demonstrated.
Starting Page	1450
Ending Page	1455
File Size	567758
Page Count	6
File Format	PDF
ISBN	9781424446483
ISSN	1553572X
e-ISBN	9781424446506
DOI	10.1109/IECON.2009.5414721
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-03
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	State estimation Vehicle dynamics Predictive models Neurons Automotive engineering Artificial neural networks Convergence Neural networks Control systems Brushless DC motors
Content Type	Text
Resource Type	Article

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