mlsanityData to 14 Oct 2026

Communication-Control Codesign for Large-Scale Wireless Networked Control Systems

arXiv 2410.11316 ↗First posted 15 Oct 2024eess.SY cs.IT cs.LGcited by 0

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Abstract (arXiv)

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 channels. We formulate the codesign problem as a sequential decision-making task that jointly optimizes scheduling and control inputs across estimation, control, and communication domains. To solve this problem, we develop a Deep Reinforcement Learning (DRL) algorithm that efficiently handles the hybrid action space, captures communication-control correlations, and ensures robust training despite sparse cross-domain variables and floating control inputs. Extensive simulations show that the proposed DRL approach outperforms benchmarks and solves the large-scale WNCS codesign problem, providing a scalable solution for industrial automation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 74 references that are in the map. First posted on arXiv 15 Oct 2024.

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19 of 74 references.

References · 19

  1. 2016Layer Normalizationcited by 5,669
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  3. 2018Deep Reinforcement Learning for Wireless Sensor Scheduling in Cyber-Physical Systemscited by 23
  4. 2018Deep Reinforcement Learning for Event-Triggered Controlcited by 16
  5. 2020Learning Event-triggered Control from Data through Joint Optimizationcited by 9
  6. 2022DRL-based Resource Allocation in Remote State Estimationcited by 7
  7. 2022Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approachcited by 7
  8. 2020Model-Free Design of Control Systems over Wireless Fading Channelscited by 6
  9. 2018LQG Control and Sensing Co-Designcited by 5
  10. 2020On the Latency, Rate and Reliability Tradeoff in Wireless Networked Control Systems for IIoTcited by 5
  11. 2021Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transportcited by 5
  12. 2019Deep reinforcement learning for scheduling in large-scale networked control systemscited by 3
7 more
  1. 2022Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transportcited by 3
  2. 2021A Jointly Optimal Design of Control and Scheduling in Networked Systems under Denial-of-Service Attackscited by 2
  3. 2022A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocationcited by 2
  4. 2023Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Controlcited by 2
  5. 2024Remote UGV Control via Practical Wireless Channels: A Model Predictive Control Approachcited by 2
  6. 2015Co-design of controller and routing redundancy over a wireless networkcited by 1
  7. 2019Scheduling and control over networks using MPC with time-varying terminal ingredients: extended versioncited by 1

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