Observer-based adaptive event-triggered control for human-machine cooperation autonomous vehicles

The path tracking problem is studied in this paper for a human-machine cooperative steering control system, which involves the collaboration of driver engagement and vehicle autonomous driving. The proposed control scheme effectively counteracts unknown disturbances and demonstrates excellent tracking performance. Firstly, an adaptive steering control scheme with a disturbance observer is proposed, which can effectively reduce the influence of external disturbances and the uncertainties from drivers and vehicles. Secondly, to reduce the communication burden, a dynamic event-triggered mechanism is designed, while ensuring the system’s tracking performance remains unaffected. Then, through rigorous theoretical analysis, it is proven that the tracking error of this vehicle ultimately converges to a small neighborhood of the origin, and the triggering mechanism can avoid Zeno behavior. Finally, through comparative simulations on the CarSim platform, the effectiveness and superiority of the proposed control scheme are demonstrated.