Adaptive Robust Formation Control Strategy for CAVs Merging at Highway On-Ramps

Highway on-ramps are typical bottlenecks for the deployment of connected and automated vehicles (CAVs). However, few studies have considered time-varying traffic volume and dynamic uncertainties in real-world scenarios. This paper proposes an adaptive robust formation control strategy (ARFC) for CAVs to address these challenges. The ARFC consists of two stages: 1) In the tactical stage, the constraint-oriented platoon formation algorithm classifies approaching CAVs into multiple local virtual platoons (LVP), assigns the collision-free merging sequences and driving modes, thereby enabling the strategy to adapt to time-varying traffic volume. Unified spatial-dependent constraints are formulated to construct this geometry topology, where the spatial dependence ensures collision avoidance in the merging conflict zone. 2) In the operational stage, an adaptive robust control scheme is designed based on the Lyapunov min-max approach, rendering uniform boundedness, uniform ultimate boundedness performance of tracking errors, and string stability of LVP, regardless of time-varying uncertainties. Comprehensive validations demonstrate that the proposed strategy performs effectively under different traffic demands, and improves ride comfort and fuel economy compared to baseline methods.