  This paper presents a robust hierarchical MPC (H-MPC) for dynamic systems
with slow states subject to demand forecast uncertainty. The H-MPC has two
layers: (i) the scheduling MPC at the upper layer with a relatively long
prediction/planning horizon and slow update rate, and (ii) the piloting MPC at
the lower layer over a shorter prediction horizon with a faster update rate.
The scheduling layer MPC calculates the optimal slow states, which will be
tracked by the piloting MPC, while enforcing the system constraints according
to a long-range and approximate prediction of the future demand/load, e.g.,
traction power demand for driving a vehicle. In this paper, to enhance the
H-MPC robustness against the long-term demand forecast uncertainty, we propose
to use the high-quality preview information enabled by the connectivity
technology over the short horizon to modify the planned trajectories via a
constraint tightening approach at the scheduling layer. Simulation results are
presented for a simplified vehicle model to confirm the effectiveness of the
proposed robust H-MPC framework in handling demand forecast uncertainty.
