Data-Learning Game Output Regulation Approach for Human-Machine Cooperative Driving Toward Varied Drivers and Vehicles

For personalized human-machine cooperative (HMC) control, traditional model-driven approaches, which rely on predefined driver-vehicle-road (DVR) models, often struggle to adapt to individual driver differences. To address this, a data-learning shared control strategy based on game output regulation and adaptive dynamic programming (ADP) is presented. Firstly, considering the differences in driver's characteristics, vehicle-road dynamics and human-machine interaction, an uncertain DVR system is established. Subsequently, robust output regulation (ROR) is utilized to handle road curvature perturbations and ensure closed-loop system stability. Subsequently, a dynamic game framework between the front-wheel steering system (AFS) and the active rear-wheel steering system (ARS) is further developed to ensure both vehicle stability and path-tracking accuracy in complex environments. Finally, the AFS-ARS optimal control strategies are iteratively learned and updated by ADP, using online DVR system data, without requiring prior knowledge of specific drivers or vehicles. Through driver-in-the-loop experiments, it is demonstrated that the presented method exhibits good adaptability to different drivers.