Path-tracking of autonomous vehicles using a novel adaptive robust exponential-like-sliding-mode fuzzy type-2 neural network controller

Hamid Taghavifar, Subhash Rakheja

Research output: Contribution to journalArticle

Abstract

Structured and unstructured uncertainties together with external disturbances may pose considerable challenges in realizing desired path-tacking and lane keeping of autonomous vehicles. This paper presents a novel robust adaptive indirect control method based on an exponential-like-sliding-mode fuzzy type-2 neural network approach for enhanced path-tracking performance of road autonomous vehicles subject to parametric uncertainties related to vehicle nominal cornering stiffness, road-tire adhesion coefficient, inertial parameters, and forward speed. A hierarchical controller is designed and the stability of the closed-loop system is ensured along with deriving the adaptation laws by employing the Lyapunov stability theorem. The conventional reaching law related to the sliding mode degrades from the system stability and introduces an inherent chattering of the controller input. The convergence law for the sliding surface is adjusted based on a variable exponential sliding manifold to eliminate possible chattering in the buffeting switch zone near the origin. The proposed exponential-like sliding surface guarantees the swift and smooth global asymptotic convergence of the tracking error toward zero. Furthermore, an adaptive look-ahead path-tracking error term is introduced as an auxiliary error criterion to improve the vehicle path-tracking performance. The effectiveness of the proposed controller is verified through Matlab/Simulink–CarSim co-simulations and comparisons with selected reported control methods for two different road maneuvers. The results suggested substantially improved tracking performance by the proposed controller with robustness to withstand against the perturbed parameters and external disturbances.
Original languageEnglish
Pages (from-to)41-55
Number of pages15
JournalMechanical Systems and Signal Processing
Volume130
Early online date8 May 2019
DOIs
Publication statusPublished - 1 Sep 2019

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Neural networks
Controllers
Buffeting
Robustness (control systems)
System stability
Closed loop systems
Tires
Adhesion
Switches
Stiffness
Uncertainty

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Mechanical Systems and Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Mechanical Systems and Signal Processing, 130, (2019) DOI: 10.1016/j.ymssp.2019.04.060

© 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • Autonomous vehicles
  • Fuzzy system
  • Lane keeping control
  • Robustness

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications

Cite this

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abstract = "Structured and unstructured uncertainties together with external disturbances may pose considerable challenges in realizing desired path-tacking and lane keeping of autonomous vehicles. This paper presents a novel robust adaptive indirect control method based on an exponential-like-sliding-mode fuzzy type-2 neural network approach for enhanced path-tracking performance of road autonomous vehicles subject to parametric uncertainties related to vehicle nominal cornering stiffness, road-tire adhesion coefficient, inertial parameters, and forward speed. A hierarchical controller is designed and the stability of the closed-loop system is ensured along with deriving the adaptation laws by employing the Lyapunov stability theorem. The conventional reaching law related to the sliding mode degrades from the system stability and introduces an inherent chattering of the controller input. The convergence law for the sliding surface is adjusted based on a variable exponential sliding manifold to eliminate possible chattering in the buffeting switch zone near the origin. The proposed exponential-like sliding surface guarantees the swift and smooth global asymptotic convergence of the tracking error toward zero. Furthermore, an adaptive look-ahead path-tracking error term is introduced as an auxiliary error criterion to improve the vehicle path-tracking performance. The effectiveness of the proposed controller is verified through Matlab/Simulink–CarSim co-simulations and comparisons with selected reported control methods for two different road maneuvers. The results suggested substantially improved tracking performance by the proposed controller with robustness to withstand against the perturbed parameters and external disturbances.",
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