Statistical inference and machine-learning algorithms have traditionally been developed for
data available at a single location. Unlike this centralized setting, modern data sets are …

This study investigates the robust fault detection filter design problem for a class of discrete‐
time conic‐type non‐linear Markov jump systems with jump fault signals. The conic‐type non …

This paper focuses on designing, via the minimization of a suitable cost function, an input
sequence to guarantee active fault diagnosis (AFD) of discrete-time linear parameter …

We address the problem of reaching resilient consensus among a set of agents in the
presence of faulty nodes (attacked or noisy). We propose general algorithms, ie, receiving …

In model-based fault detection and isolation (FDI) systems, fault indicating signals (FISs)
such as residuals and fault estimates are corrupted by various noises, uncertainties and …

This paper addresses the problem of having a multi-agent system converging to a
rendezvous location for networks of agents without any type of localization sensor. A central …

There has been a vast body of research on set-membership techniques in recent years.
These algorithms compute convex sets that contain the state of a dynamical system given …

Autonomous vehicles in GPS-denied areas or when cooperating in missions might have
access to bearing and range measurements corrupted by noise, rendering the reachable set …

This paper addresses the problem of having a multi-agent system converging to multiple
dynamic rendezvous areas for networks of agents with no localization sensors and limited …

In this paper, we tackle the problem of state estimation for uncertain linear systems when
bounds are known for the disturbances, noise and initial state. Practical systems often have …