RISE-Based Integrated Motion Control of Autonomous Ground Vehicles with Asymptotic Prescribed Performance

Chuan Hu, Hongbo Gao, Jinghua Guo, Hamid Taghavifar, Yechen Qin*, Jing Na, Chongfeng Wei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

This article investigates the integrated lane-keeping and roll control for autonomous ground vehicles (AGVs) considering the transient performance and system disturbances. The robust integral of the sign of error (RISE) control strategy is proposed to achieve the lane-keeping control purpose with rollover prevention, by guaranteeing the asymptotic stability of the closed-loop system, attenuating systematic disturbances, and maintaining the controlled states within the prescribed performance boundaries. Three contributions have been made in this article: 1) a new prescribed performance function (PPF) that does not require accurate initial errors is proposed to guarantee the tracking errors restricted within the predefined asymptotic boundaries; 2) a modified neural network (NN) estimator which requires fewer adaptively updated parameters is proposed to approximate the unknown vertical dynamics; and 3) the improved RISE control based on PPF is proposed to achieve the integrated control objective, which analytically guarantees both the controller continuity and closed-loop system asymptotic stability by integrating the signum error function. The overall system stability is proved with the Lyapunov function. The controller effectiveness and robustness are finally verified by comparative simulations using two representative driving maneuvers, based on the high-fidelity CarSim-Simulink simulation.

Original languageEnglish
Pages (from-to)5336-5348
Number of pages13
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume51
Issue number9
Early online date12 Nov 2019
DOIs
Publication statusPublished - Sep 2021
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received June 14, 2019; accepted October 17, 2019. Date of publication November 12, 2019; date of current version August 18, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 51805028 and Grant 61873115, and in part by the China Post-Doctoral Science Foundation under Grant BX201600017. This article was recommended by Associate Editor Y.-J. Liu. (Corresponding author: Yechen Qin.) C. Hu is with the Department of Mechanical Engineering, University of Texas at Austin, Austin, TX 78712 USA.

Publisher Copyright:
© 2013 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Autonomous ground vehicles (AGVs)
  • lane keeping
  • neural network (NN)
  • prescribed performance control (PPC)
  • robust integral of the sign of the error (RISE)
  • roll control

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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