Data-driven control of network systems: Accounting for communication adaptivity and security
Gang Wang, Wenjie Liu, Yifei Li, Xin Wang, Jian Sun, Jie Chen
TL;DR
This paper surveys data-driven control for networked systems under four core challenges: communication delay, aperiodic transmission, network security, and distributed configurations. It centers on Willems' fundamental lemma-based representations, contrasting QMI-formed and polytopic noise models to derive stability and performance guarantees across delays, ETC/STC, resilience to DoS/FDI, and distributed multi-agent coordination. The contributions include data-driven LMIs and optimization-based schemes for robust stabilization, triggering rule design, and output synchronization, enabling resource-efficient and secure operation without explicit plant models. The work highlights the practical impact of data-driven control for scalable, resilient networked systems and points to future directions in nonlinear extensions, cybersecurity integration, distributed learning, and real-world validation.
Abstract
Over the past decades, network systems have surged in significance, driven by merging technological advancements. These systems play pivotal roles in diverse applications ranging from autonomous driving to smart grids, yet they confront complexities arising from network imperfections and intricate interconnections, which challenge system identification, controller design, as well as stability and performance analysis. This survey provides an in-depth exploration of network systems from most recent data-driven perspective, across four key issues: communication delay, aperiodic sampling, network security, and distributed configurations. By doing so, this survey enhances our comprehension of the challenges and theoretical innovations within the realm of network systems.