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

Chuan Hu, Zhenfeng Wang, Hamid Taghavifar, Jing Na, Yechen Qin*, Jinghua Guo, Chongfeng Wei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

60 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)5246-5259
Number of pages14
JournalIEEE Transactions on Vehicular Technology
Volume68
Issue number6
Early online date27 Mar 2019
DOIs
Publication statusPublished - Jun 2019
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received September 18, 2018; revised January 29, 2019; accepted March 19, 2019. Date of publication March 27, 2019; date of current version June 18, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 51805028, and in part by the China Postdoctoral Science Foundation under Grants 2016M600934 and BX201600017. The review of this paper was coordinated by Dr. A. Heydari. (Corresponding author: Yechen Qin.) C. Hu is with the Department of Mechanical Engineering, University of Texas at Austin, Austin, TX 78712 USA (e-mail:,chuan.hu.2013@gmail.com).

Publisher Copyright:
© 1967-2012 IEEE.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

  • autonomous vehicles
  • extended Kalman filter
  • neural network
  • Path tracking
  • sliding mode control

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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