Memory-Event-Triggered Tracking Control for Intelligent Vehicle Transportation Systems: A Leader-Following Approach

This paper addresses the problem of adaptive memory-event-triggered control for intelligent vehicle transportation systems (IVTSs). Unlike the existing methods for modeling IVTSs, the acceleration information is introduced in the desired distance to achieve better distance control performance of IVTSs. The information between vehicles interacts over a wireless network. In order to further reduce the amount of data transmission, a novel adaptive memory-event-triggered mechanism (METM) is developed, in which historical information is utilized. In addition, the proposed METM threshold is designed to vary with the system state of each autonomous vehicle to adjust the data-releasing rate adaptively. Under the adaptive METM, a distributed tracking controller with historical information is put forward to guarantee uniformly ultimately bounded (UUB) stability using the Lyapunov stability theory and linear matrix inequality (LMI) technique. Finally, simulation results are given to verify the superiority of the proposed method.