Event-triggered human-machine shared steering online learning control strategy to accommodate driver behavioral uncertainty

To address the stochasticity and uncertainty in driver behavior, an event-triggered shared control strategy is proposed, enabling real-time adaptation while ensuring system stability. The vehicle control system is modeled as a nonlinear time-varying (NTV) system, incorporating human-machine interaction, driver behavior, variable speeds, and uncertain tire dynamics. Sparse Gaussian process regression (GPR) is employed for online system identification, eliminating the need for an exact system model. An adaptive feedback linearization (AFL) controller is designed, with asymptotic stability proven using a common Lyapunov function (CLF). The event-triggered mechanism updates the model and controller only when GPR uncertainty exceeds an adaptive threshold. Simulations and driver-in-the-loop experiments assess the proposed algorithm, demonstrating its robustness and adaptability to different driver behaviors, while reducing communication overhead and ensuring precise steering control.