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.

翻译：网络化控制系统是一种闭环反馈控制系统，其系统组件可能分布在不同地理位置，并通过通信网络（如互联网）相互连接。网络通信质量是显著影响远程控制性能的关键因素，这是因为在系统前向和后向通道的数据包传输过程中可能出现网络不确定性。其中最重要的两种不确定性是网络时延和数据包丢失。为克服这些挑战，已提出网络化预测控制系统，通过采用预测控制器和补偿策略来提升性能与鲁棒性。特别地，与传统方法相比，模型预测控制方法作为先进方法具有显著优势。本文实现了一种由模型预测控制方法和补偿策略组成的网络化模型预测控制系统，用于控制并稳定作为物理系统的机器人手臂。本研究旨在分析系统在网络时延和数据包丢失影响下的性能，通过合适的性能与鲁棒性指标，深入探究这些网络不确定性的影响。此外，本文还对网络的前向和后向通道进行了详细分析。