Asynchronous Shared Steering Control Via Dynamic Event-Triggering with Data-Driven Guarantees

Shared steering control enables human–machine cooperation in vehicle lateral control. This article develops an asynchronous shared steering control framework with dynamic event triggering and data-driven guarantees. The human–machine steering system is modeled as an asynchronous closed-loop system, where the driver steering action evolves continuously while the automated steering command is updated only at triggered instants. Without explicit model identification, a data-driven synthesis method is established directly from input-state trajectory data via consistency-set characterization, enabling the codesign of the state-feedback controller and the dynamic triggering mechanism under actuator saturation. Sufficient linear matrix inequality conditions are derived to ensure regional stability and discrete-time $H_\infty$ performance under the stated admissibility conditions for all data-consistent system realizations, while the resulting invariant ellipsoids provide certified positively invariant regions in the disturbance-free case. Simulation results verify the effectiveness and robustness of the proposed method under both disturbance-free and disturbed conditions.