Reference:
A. Jamshidnejad,
G. Gomes,
A.M. Bayen, and
B. De Schutter,
"Integrated offline and online predictive control system within a
base-parallel architecture," Tech. rep. 18-025, Delft Center for
Systems and Control, Delft University of Technology, Delft, The
Netherlands, 2018. Submitted for publication. See also https://arxiv.org/abs/1907.05464v1.
Abstract:
Optimization-based controllers minimize a specific performance index
within a finite or infinite prediction window, and find the
corresponding optimal control input. An estimator helps the controller
to determine the future states of the controlled system and the
external inputs such as disturbances, etc. Online application of
optimization-based controllers is still a challenge, especially since
the time required for investigating the search space by the
optimization solver usually exceeds the requirements of a real-time
procedure. In this paper, we propose a novel integrated control
architecture that benefits from the advantages of both offline and
online controllers within a predictive base-parallel structure. The
base block includes efficient parameterized controllers, which have
been optimized offline w.r.t. their parameters within an update
window, which is larger than the prediction window, and direct
controllers, which have been trained offline to produce a sequence of
control inputs within the prediction window. These controllers provide
good starting points for the optimization-based controllers that are
run in parallel in real time. We discuss different options for
designing the proposed base-parallel control architecture. Finally, we
implement the proposed architecture for a highway that is controlled
by ramp metering, and compare our results with the results of previous
efficient controllers, such as ALINEA and an artificial neural
network-based controller that has been trained by deep learning.