A Review on Wireless Networked Control System: The Communication Perspective

Wang, Ying; Wu, Shaohua; Lei, Chengjia; Jiao, Jian; Zhang, Qinyu

doi:10.1109/jiot.2023.3342032

Cited by 4 publications

(1 citation statement)

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“…According to Industry 4.0, wireless networked control systems (WNCSs) have been applied to industrial networks for various services, such as industrial automation, smart manufacturing, and unmanned robot control [ 1 , 2 ]. WNCSs have been considered as a prominent solution in industrial networks to provide real-time and reliable actuation [ 3 , 4 ]. Compared to conventional NCSs based on wired networks, since WNCSs are spatially distributed wireless control systems, they have been researched with respect to the enhancement of energy-harvesting capabilities [ 5 ], wireless resource scheduling [ 6 ], energy-aware performance optimization [ 7 ], and wireless attacks [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Priority-Aware Actuation Update Scheme in Heterogeneous Industrial Networks

Kyung,

Sung,

et al. 2024

Sensors

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In heterogeneous wireless networked control systems (WNCSs), the age of information (AoI) of the actuation update and actuation update cost are important performance metrics. To reduce the monetary cost, the control system can wait for the availability of a WiFi network for the actuator and then conduct the update using a WiFi network in an opportunistic manner, but this leads to an increased AoI of the actuation update. In addition, since there are different AoI requirements according to the control priorities (i.e., robustness of AoI of the actuation update), these need to be considered when delivering the actuation update. To jointly consider the monetary cost and AoI with priority, this paper proposes a priority-aware actuation update scheme (PAUS) where the control system decides whether to deliver or delay the actuation update to the actuator. For the optimal decision, we formulate a Markov decision process model and derive the optimal policy based on Q-learning, which aims to maximize the average reward that implies the balance between the monetary cost and AoI with priority. Simulation results demonstrate that the PAUS outperforms the comparison schemes in terms of the average reward under various settings.

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