  Networked control systems are closed-loop feedback control systems containing
system components that may be distributed geographically in different locations
and interconnected via a communication network such as the Internet. The
quality of network communication is a crucial factor that significantly affects
the performance of remote control. This is due to the fact that network
uncertainties can occur in the transmission of packets in the forward and
backward channels of the system. The two most significant among these
uncertainties are network time delay and packet loss. To overcome these
challenges, the networked predictive control system has been proposed to
provide improved performance and robustness using predictive controllers and
compensation strategies. In particular, the model predictive control method is
well-suited as an advanced approach compared to conventional methods. In this
paper, a networked model predictive control system consisting of a model
predictive control method and compensation strategies is implemented to control
and stabilize a robot arm as a physical system. In particular, this work aims
to analyze the performance of the system under the influence of network time
delay and packet loss. Using appropriate performance and robustness metrics, an
in-depth investigation of the impacts of these network uncertainties is
performed. Furthermore, the forward and backward channels of the network are
examined in detail in this study.
