Author Description

Networked control systems, rooted in networked control theory, tackle collective behavior and coordination among interconnected entities. This concept, which is crucial in robotics, social networks, and distributed computing, aims to achieve consensus among diverse agents. Understanding and guiding such systems toward coherent states is pivotal for developing resilient and adaptive systems capable of autonomous decision-making and control. However, bridging the gap between theoretical constructs and real-world applications poses challenges, particularly in networked control systems. Incorporating factors like input noise and communication time-delay is essential for a nuanced understanding and effective deployment of control systems in uncertain environments.A significant focus lies on investigating cascading failures within networked control systems, triggered by the failure of a single component propagating through the entire network. Understanding and mitigating cascading effects are imperative for safeguarding critical infrastructure and enhancing system resilience. Through theoretical analyses and simulation studies, researchers aim to characterize the dynamics of cascading failures and develop robust frameworks to mitigate their impact, thereby enhancing the stability and reliability of networked control systems. In the next stage, the challenge will emerge when the statistics of the noise are not exactly known. As a result, the ambiguity of the uncertainty will be introduced into the existing system and make the behavior of the system even less predictable. To address this issue, we adopt the concept of distributionally robust risk measures and optimization to solve the robustified version of the original problem. These methodologies aim to enhance the robustness of the system against the ambiguity of uncertainty, providing a framework to make decisions that are resilient to variations in the underlying distribution of the noise.Lastly, instead of considering