ABSTRACT
Networked control systems, address the collective behavior and coordination among interconnected entities, which is crucial in
fields like robotics, social networks, and distributed computing. However, applying theoretical constructs to realworld scenarios is challenging, especially in incorporating input noise and communication timedelay, which are vital for effective deployment in uncertain environments. A significant focus of ours is on investigating and mitigating cascading failures, where a single component’s failure can propagate through the network, threatening critical infrastructure. Additionally, we employ distributionally robust risk measures and optimization to enhance system robustness against ambiguous uncertainty when the noise statistics are unknown. Furthermore, we also explore realworld perception models to enhance robotic capabilities, including 3D scene understanding via Neural Radiance Fields (NeRF) models, traffic sign classification with riskaware decisionmaking, and map classification by communicating aerial robots, thereby examining the impact of uncertainties on the safety
and efficacy of autonomous systems.

PRESENTER
https://sites.google.com/lehigh.edu/g...
Guangyi Liu obtained his Ph.D. at the Autonomous and Intelligent Robotics (AIR) Laboratory, Department of Mechanical Engineering, Lehigh University, working under the advisory of Prof. Nader Motee. Before joining Lehigh, he obtained his B.E. degree at Beijing Institute of Technology. His research interests include risk and robustness analysis in networked control systems and perception systems. He has served as a workshop organizer in IEEE ACC 2023 and organized three invited sessions at ACC 2023 and ACC 2024.