This paper investigates the stability and stabilization of semilinear single-track vehicle models with distributed tire friction dynamics, modeled as interconnections of ordinary differential equations (ODEs) and hyperbolic partial differential equations (PDEs). Motivated by the long-standing practice of neglecting transient tire dynamics in vehicle modeling and control, a rigorous justification is provided for such simplifications using singular perturbation theory. A perturbation parameter, defined as the ratio between a characteristic rolling contact length and the vehicle's longitudinal speed, is introduced to formalize the time-scale separation between rigid-body motion and tire dynamics. For sufficiently small values of this parameter, it is demonstrated that standard finite-dimensional techniques can be applied to analyze the local stability of equilibria and to design stabilizing controllers. Whilst the proposed controllers build on classical approaches, the novelty of this work lies in establishing the first singular perturbation framework for ODE-PDE vehicle models with distributed tire dynamics, providing a theoretical justification for their quasi-static reduction and for the use of finite-dimensional tools for analysis and control design.
Lateral tracking control of all-wheel steering vehicles with intelligent tires
Department of Electrical Engineering, Linköping University（林奈大学电气工程系）；Department of Engineering Cybernetics, NTNU（挪威技术大学工程 cybernetics 系）；IEEE Publication Technology Department（IEEE出版技术部）
Dual-Envelope Constrained Nonlinear MPC for Distributed Drive Electric Vehicles Drifting Under Bounded Steering and Direct Yaw-Moment Control
State Key Laboratory of Automotive Simulation and Control, Jilin University（吉林大学汽车动态模拟与控制国家重点实验室）；Center of Research for Cyber Security and Network (CSNET), Faculty of Computer Science and Information Technology, Universiti Malaya（马来亚大学计算机科学与信息技术学院网络安全与网络研究中心）；Department of Electrical and Electronic Engineering, University of Hong Kong（香港大学电子电气工程系）；School of Engineering, Edith Cowan University（伊迪斯科文大学工程学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling
Toyota Research Institute（丰田研究院）；Department of Mechanical Engineering, Rice University（机械工程系， Rice大学）；MANE, Rensselaer Polytechnic Institute（MANE，Rensselaer理工学院）
针对极限动态驾驶中在线学习滞后易致失控的问题，提出结合贝叶斯最后一层元学习与不确定性引导主动数据收集的车辆动力学辨识框架，并在 Toyota Supra 漂移实验中验证其可靠控制效果。
