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

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 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.