  This paper develops a sequencing-enabled hierarchical connected automated
vehicle (CAV) cooperative on-ramp merging control framework. The proposed
framework consists of a two-layer design: the upper level control sequences the
vehicles to harmonize the traffic density across mainline and on-ramp segments
while enhancing lower-level control efficiency through a mixed-integer linear
programming formulation. Subsequently, the lower-level control employs a
longitudinal distributed model predictive control (MPC) supplemented by a
virtual car-following (CF) concept to ensure asymptotic local stability, l_2
norm string stability, and safety. Proofs of asymptotic local stability and l_2
norm string stability are mathematically derived. Compared to other prevalent
asymptotic local-stable MPC controllers, the proposed distributed MPC
controller greatly expands the initial feasible set. Additionally, an auxiliary
lateral control is developed to maintain lane-keeping and merging smoothness
while accommodating ramp geometric curvature. To validate the proposed
framework, multiple numerical experiments are conducted. Results indicate a
notable outperformance of our upper-level controller against a distance-based
sequencing method. Furthermore, the lower-level control effectively ensures
smooth acceleration, safe merging with adequate spacing, adherence to proven
longitudinal local and string stability, and rapid regulation of lateral
deviations.
