  Distributed switched large-scale systems are composed by dynamically coupled
subsystems, in which interactions among subsystems vary over time according to
an exogenous input signal named switching signal. In this paper, we present a
distributed robust switched model-based predictive control (DSwMPC) to control
such systems that are subject to local state and input constraints. The
proposed method guarantees stabilizing the origin of the whole closed-loop
system and ensures the constraints satisfaction in the presence of an unknown
switching signal. In the distributed model-based predictive control (DMPC) used
in this work, by considering the interactions among subsystems as an additive
disturbance, the effect of the switch is reflected on the dynamic equation,
local, and consistency constraint sets of the nominal subsystems. In the
DSwMPC, to compensate the effect of switching signal which creates a
time-varying network topology, a robust tube-based switched model-based
predictive control (RSwMPC) with switched robust control invariant (switchRCI)
set as the target set robust to unknown mode switching is used as local
controller. The scheme performance is assessed using three typical examples. In
the first example, the switching times are unknown in prior, but the next
neighborhood sets are assumed to be known in prior. In the second and third
cases, both of them are supposed to be unknown in prior. The simulation results
show that the input and state constraints are satisfied by the proposed DSwMPC
at all times. They also validate that the closed-loop system converges to the
origin. Also, a comparison of the DSwMPC with a centralized SwMPC (CSwMPC) and
a decentralized SwMPC (DeSwMPC) shows that the DSwMPC outperforms the DecSwMPC
and also the shapes of response curves under the CSwMPC are very similar to
those obtained by the DSwMPC.
