  We propose an integrated control architecture to address the gap that
currently exists for efficient real-time implementation of MPC-based control
approaches for highly nonlinear systems with fast dynamics and a large number
of control constraints. The proposed architecture contains two types of
controllers: base controllers that are tuned or optimized offline, and parallel
controllers that solve an optimization-based control problem online. The
control inputs computed by the base controllers provide starting points for the
optimization problem of the parallel controllers, which operate in parallel
within a limited time budget that does not exceed the control sampling time.
The resulting control system is very flexible and its architecture can easily
be modified or changed online, e.g., by adding or eliminating controllers, for
online improvement of the performance of the controlled system. In a case
study, the proposed control architecture is implemented for highway traffic,
which is characterized by nonlinear, fast dynamics with multiple control
constraints, to minimize the overall travel time of the vehicles, while
increasing their total traveled distance within the fixed simulation time
window. The results of the simulation show the excellent real-time (i.e.,
within the given time budget) performance of the proposed control architecture,
with the least realized value of the overall cost function. Moreover, among the
online control approaches considered for the case study, the average cost per
vehicle for the base-parallel control approach is the closest to the online
MPC-based controllers, which have excellent performance but may involve
computation times that exceed the given time budget.
