  The design of cooperative adaptive cruise control is critical in mixed
traffic flow, where connected and automated vehicles (CAVs) and human-driven
vehicles (HDVs) coexist. Compared with pure CAVs, the major challenge is how to
handle the prediction uncertainty of HDVs, which can cause significant state
deviation of CAVs from planned trajectories. In most existing studies, model
predictive control (MPC) is utilized to replan CAVs' trajectories to mitigate
the deviation at each time step. However, as the replan process is usually
conducted by solving an optimization problem with information through
inter-vehicular communication, MPC methods suffer from heavy computational and
communicational burdens. To address this limitation, a robust platoon control
framework is proposed based on tube MPC in this paper. The prediction
uncertainty is dynamically mitigated by the feedback control and restricted
inside a set with a high probability. When the uncertainty exceeds the set or
additional external disturbance emerges, the feedforward control is triggered
to plan a ``tube'' (a sequence of the set), which can bound CAVs' actual
trajectories. As the replan process is usually not required, the proposed
method is much more efficient regarding computation and communication, compared
with the MPC method. Comprehensive simulations are provided to validate the
effectiveness of the proposed framework.
