Reference:
S. Roshany-Yamchi,
M. Cychowski,
R.R. Negenborn,
B. De Schutter,
K. Delaney, and
J. Connell,
"Kalman filter-based distributed predictive control of large-scale
multi-rate systems: Application to power networks," IEEE
Transactions on Control Systems Technology, vol. 21, no. 1, pp.
27-39, Jan. 2013.
Abstract:
In this paper, a novel distributed Kalman Filter (KF) algorithm along
with a distributed Model Predictive Control (MPC) scheme for
large-scale multi-rate systems is proposed. The decomposed multi-rate
system consists of smaller subsystems with linear dynamics that are
coupled via states. These subsystems are multi-rate systems in the
sense that either output measurements or input updates are not
available at certain sampling times. Such systems can arise, e.g.,
when the number of sensors is smaller than the number of variables to
be controlled, or when measurements of outputs cannot be completed
simultaneously because of practical limitations. The multi-rate nature
gives rise to lack of information, which will cause uncertainty in the
system's performance. To circumvent this problem, we propose a
distributed KF-based MPC scheme, in which multiple control and
estimation agents each determine actions for their own parts of the
system. Via communication, the agents can in a cooperative way take
one another's actions into account. The main task of the proposed
distributed KF is to compensate for the information loss due to the
multi-rate nature of the systems by providing optimal estimation of
the missing information. A demanding two-area power network example is
used to demonstrate the effectiveness of the proposed method.