This brief is concerned with the robust H 2 consensus problem of linear multi-agent systems
in the presence of parametric uncertainties and noise signals. The robust H 2 consensus …

This article investigates the optimal linear filter (OLF) over Gilbert–Elliott channels where
packet losses follow a two-state Markov chain. For a system with packet losses, according to …

We address the problem of sparse selection of visual features for localizing a team of robots
navigating in an unknown environment, where robots can exchange relative position …

Recurrent Neural networks (RNN) have shown promising potential for learning dynamic
features of sequential data. However, artificial neural networks are known to exhibit poor …

Recent advances in model predictive control (MPC) leverage local communication
constraints to produce localized MPC algorithms whose complexities scale independently of …

The power grid, the internet, a city of autonomous cars, neural networks in humans and
intelligent systems, and the microbiome are just a few examples of large-scale distributed …

This paper investigates the robustness of linear consensus networks which are designed
under a hierarchical scheme based on Cartesian product. For robustness analysis …

This dissertation explores a diverse set of problems in dynamical systems, control,
estimation, and learning theory. Part I studies nonlinear systems using operator theory …

In this paper, we focus on localized conditions to verify exponential stability of spatially
distributed linear systems whose interconnection structures are defined using a geodesic on …

In engineering, control theory plays a crucial role in the design and analysis of robust and
efficient systems—including robots, spacecraft, and power grids. In biology, control theory …