Communication-Control Codesign for Large-Scale Wireless Networked Control Systems
Gaoyang Pang, Wanchun Liu, Dusit Niyato, Branka Vucetic, Yonghui Li

TL;DR
This paper introduces a deep reinforcement learning-based approach for the integrated design of large-scale wireless networked control systems, addressing complex interdependencies and resource constraints to improve industrial automation.
Contribution
It presents a novel WNCS model capturing correlated dynamics and proposes a DRL algorithm for joint scheduling and control optimization in large-scale systems.
Findings
DRL approach outperforms benchmarks in simulations
Scalable solution for large-scale WNCSs
Effective handling of communication-control correlations
Abstract
Wireless Networked Control Systems (WNCSs) are essential to Industry 4.0, enabling flexible control in applications, such as drone swarms and autonomous robots. The interdependence between communication and control requires integrated design, but traditional methods treat them separately, leading to inefficiencies. Current codesign approaches often rely on simplified models, focusing on single-loop or independent multi-loop systems. However, large-scale WNCSs face unique challenges, including coupled control loops, time-correlated wireless channels, trade-offs between sensing and control transmissions, and significant computational complexity. To address these challenges, we propose a practical WNCS model that captures correlated dynamics among multiple control loops with spatially distributed sensors and actuators sharing limited wireless resources over multi-state Markov block-fading…
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Taxonomy
TopicsNetwork Time Synchronization Technologies
