Motion control of autonomous vehicles presents unique challenges in terms of stability and obstacle avoidance. Especially in critical driving conditions such as μ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu $$\end{document}-low, autonomous vehicles may reach the limits of stability and require the use of all available lateral friction to maintain control and stably follow a reference path, while also taking into consideration collision avoidance. Overactuated vehicles can mitigate the issue of limited stability and controllability by optimal use of the lateral friction potential by using additional actuators. To address these challenges, this paper introduces a novel control architecture that combines a linear model predictive controller for tracking with model following and nonlinear control allocation technique. The control allocation method uses torque vectoring and rear wheel steering to extend the linear understeer characteristics and increase the maximum achievable lateral acceleration. Simulations demonstrate that this method reduces the path tracking error and enhances stable maneuvering in critical driving conditions by increasing the vehicle’s lateral capabilities.


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    Title :

    Enhancing Controllability and Path Tracking with Overactuated Autonomous Vehicles


    Additional title:

    Lect.Notes Mechanical Engineering


    Contributors:

    Conference:

    The IAVSD International Symposium on Dynamics of Vehicles on Roads and Tracks ; 2023 ; Ottawa, ON, Canada August 21, 2023 - August 25, 2023



    Publication date :

    2024-10-13


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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