Sideslip angle estimation of ground vehicles: a comparative study

• Published in: IET control theory & applications Vol. 14; no. 20; pp. 3490 - 3505
• Main Authors: Liu, Jizheng, Wang, Zhenpo, Zhang, Lei, Walker, Paul
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 27.12.2020
• Subjects: dual GPS receivers; dynamics stability; dynamics‐based estimators; Global Positioning System; ground vehicles; hardware‐in‐loop system; kinematics; kinematics‐based methods; mechanical engineering computing; neural nets; neural network‐based estimator; Research Article; road vehicles; sideslip angle estimation; stability; steering angle; steering systems; tyres; vehicle dynamics; vehicle sideslip angle; vehicle tires; vehicle velocities; tyres; hardware-in-loop system; kinematics-based methods; dynamics stability; dual GPS receivers; kinematics; vehicle dynamics; steering systems; vehicle tires; Global Positioning System; road vehicles; mechanical engineering computing; vehicle velocities; dynamics-based estimators; steering angle; sideslip angle estimation; ground vehicles; neural nets; stability; neural network-based estimator; vehicle sideslip angle
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/iet-cta.2020.0516

Vehicle sideslip angle is a major indicator of dynamics stability for ground vehicles; but it is immeasurable with commercially-available sensors. Sideslip angle estimation has been the focus of intensive research in past decades, resulting in a rich library of related literature. This study presents a comprehensive evaluation of state-of-the-art sideslip angle estimation methods, with the primary goal of quantitatively revealing their strengths and limitations. These include kinematics-, dynamics- and neural network-based estimators. A hardware-in-loop system is purposely established to examine their performance under four typical manoeuvres. The results show that the dynamics-based estimators are suitable at low vehicle velocities when tires operate in the linear region. In contrast, the kinematics-based methods yield superior estimation performance at high vehicle velocities, and the inclusion of the dual GPS receivers is beneficial even when there is large disturbance to the steering angle. Of utmost importance, it is experimentally manifested that the neural network-based estimator can perform well in all manoeuvres once the training datasets are properly selected.