In the realm of Intelligent Transportation Systems (ITSs), ensuring the safety and stability of connected automated vehicles (CAVs) is of paramount importance due to their susceptibility to vulnerabilities in interactions. The potential for system-wide disruption stemming from a cyber-attack on the leader underscores this need. Therefore, this talk introduces a nonlinear neural-Lyapunov-based adaptive resilient cruise control approach aimed at ensuring that all vehicles maintain safe tracking of the leader's profile, even in the presence of cyber-attacks and external disturbances. To achieve this, we employ an adaptive neural network to estimate the system's nonlinear characteristics. Subsequently, the control procedure is proposed, utilizing a virtual disturbance observer and Lyapunov theorem for stability analysis and adaptive laws to deal with nonlinearity, external disturbances, deception attacks, and singular control gain. Notably, our proposed approach eliminates the need for restrictive assumptions such as Lipschitz conditions on the nonlinear component and avoids the requirement for additional algorithms to switch between controllers in the event of a cyber-attack. It provides compelling evidence of system stability and the achievement of control objectives. Additionally, simulation and comparative results validate the theoretical analysis, highlighting the efficacy of the proposed methodology.

报告人简介:刘新芝现任加拿大滑铁卢大学应用数学系教授。1988年在美国德克萨斯大学阿灵顿分校获博士学位。于1990年受聘滑铁卢大学并于1994年被聘为该校终身教授。刘教授的研究方向包括:混合动力系统,非线性系统控制及稳定性,复杂网络的同步,多智能体系统跟踪及编队控制,传染病模型等。已发表学术论文400余篇，学术专著6部,学术编著20余部。