Adaptive resilient event-triggered control design of autonomous vehicles with an iterative single critic learning framework

Abstract

This paper investigates the adaptive resilient event- triggered control for rear wheel drive autonomous (RWDA) vehicles based on an iterative single critic learning framework, which can effectively balance the frequency/changes in adjusting the vehicle’s control during the running process. According to the kinematic equation of RWDA vehicles and desired trajectory, the tracking error system during autonomous driving process is firstly built, where the denial-of-service (DoS) attacking signals are injected in the networked communication and transmission. Combining the event-triggered sampling mechanism and iterative single critic learning framework, a new event-triggered condition is developed for the adaptive resilient control algorithm, and the novel utility function design are considered for driving the autonomous vehicle, where the control input can be guaranteed into an applicable saturated bound. Finally, we apply the new adaptive resilient control scheme to a case study of driving the RWDA vehicles, and the simulation results illustrate the effectiveness and practicality successfully.