Adaptive control

Operating system

Iterative learning control

Physics

Adaptive learning

Engineering

Astronomy

Control engineering

Mathematical analysis

Trajectory

Artificial intelligence

Control (management)

Event (particle physics)

Mathematics

Kinematics

Computer science

Control theory (sociology)

Quantum mechanics

Classical mechanics

Transmission (telecommunications)

Telecommunications

Scheme (mathematics)

Process (computing)

IEEE Transactions on Neural Networks and Learning Systems

This article 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 the desired trajectory, the tracking error system during the autonomous driving process is first built, where the denial-of-service (DoS) attacking signals are injected into 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 is 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 of driving the RWDA vehicles, and the simulation results illustrate the effectiveness and practicality successfully.

Adaptive Resilient Event-Triggered Control Design of Autonomous Vehicles With an Iterative Single Critic Learning Framework