MME-EKF-Based Path-Tracking Control of Autonomous Vehicles Considering Input Saturation

Hu, Chuan, Wang, Zhenfeng, Taghavifar, Hamid, Na, Jing, Qin, Yechen, Guo, Jinghua and Wei, Chongfeng (2019) MME-EKF-Based Path-Tracking Control of Autonomous Vehicles Considering Input Saturation. IEEE Transactions on Vehicular Technology, 68 (6). pp. 5246-5259. ISSN 0018-9545

[img]
Preview
Text
08675462.pdf - Accepted Version

Download (2MB) | Preview
Official URL: https://doi.org/10.1109/TVT.2019.2907696

Abstract

This paper investigates the path-tracking control issue for autonomous ground vehicles with the integral sliding mode control (ISMC) considering the transient performance improvement. The path-tracking control is converted into the yaw stabilization problem, where the sideslip-angle compensation is adopted to reduce the steady-state errors, and then the yaw-rate reference is generated for the path-tracking purpose. The lateral velocity and roll angle are estimated with the measurement of the yaw rate and roll rate. Three contributions have been made in this paper: first, to enhance the estimation accuracy for the vehicle states in the presence of the parametric uncertainties caused by the lateral and roll dynamics, a robust extended Kalman filter is proposed based on the minimum model error algorithm; second, an improved adaptive radial basis function neural network (RBFNN) considering the approximation error adaptation is developed to compensate for the uncertainties caused by the vertical motion; third, the RBFNN and composite nonlinear feedback (CNF) based ISMC is developed to achieve the yaw stabilization and enhance the transient tracking performance considering the input saturation of the front steering angle. The overall stability is proved with Lyapunov function. Finally, the superiority of the developed control strategy is verified by comparing with the traditional CNF with high-fidelity CarSim-MATLAB simulations.

Item Type: Article
Uncontrolled Keywords: Path tracking, autonomous vehicles, sliding-mode control, extended Kalman filter, neural network
Subjects: H100 General Engineering
H300 Mechanical Engineering
H900 Others in Engineering
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Rachel Branson
Date Deposited: 15 Apr 2020 09:19
Last Modified: 15 Apr 2020 09:30
URI: http://nrl.northumbria.ac.uk/id/eprint/42767

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics