On the vehicle sideslip angle estimation: a literature review of methods, models and innovations

CHINDAMO, Daniel, LENZO, Basilio and GADOLA, Marco (2018). On the vehicle sideslip angle estimation: a literature review of methods, models and innovations. Applied Sciences, 8 (3), p. 355.

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Official URL: http://www.mdpi.com/2076-3417/8/3/355
Link to published version:: https://doi.org/10.3390/app8030355


Typical active safety systems controlling the dynamics of passenger cars rely on real-time monitoring of the vehicle sideslip angle (VSA), together with other signals like wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, known as the yaw rate. The VSA (aka attitude or “drifting” angle) is defined as the angle between the vehicle longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory therefore it is a vital piece of information enabling directional stability assessment, in transients following emergency manoeuvres for instance. As explained in the introduction the VSA is not measured directly for impracticality and it is estimated on the basis of available measurements like wheel velocities, linear and angular accelerations etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main estimation methods have been categorised, i.e. Observer-based and Neural Network-based, focusing on the most effective and innovative approaches. As the first method normally relies on a vehicle model, a review of the vehicle models has been included. Advantages and limitations of each technique have been highlighted and discussed.

Item Type: Article
Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Engineering and Mathematics
Identification Number: https://doi.org/10.3390/app8030355
Page Range: p. 355
Depositing User: Basilio Lenzo
Date Deposited: 21 Feb 2018 14:15
Last Modified: 18 Mar 2021 15:21
URI: https://shura.shu.ac.uk/id/eprint/18721

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