Prof. Giuseppe Franzè
I was born in Italy in 1968. I received the Laurea degree in Computer Engineering in 1994 and the Ph.D. degree in Systems Engineering in 1999 from the University of Calabria, Italy. From 2002 to 2014, I was an Assistant Professor with the DEIS department of the University of Calabria. From 2015 to 202, I was an Associate Professor at the University of Calabria with the DIMES department.
From 2022 I am a Full Professor at the University of Calabria with the DIMEG department.
From 2018 to 2022, I was the Chair of the Degree Course Council of the Master Degree in Automation Engineering at the DIMES department.
My current research interests include constrained predictive control, nonlinear systems, networked control systems, control under constraints and control reconfiguration for fault tolerant systems.
UPCOMING EVENTS
MY LATEST RESEARCH
We propose a distributed model predictive control strategy for a team of vehicles moving in an unknown obstacle scenario. In particular, we combine receding horizon control arguments with leader-follower formations in order to design a flexible architecture where the vehicle topology, if needed, can be rearranged to better cope with the presence of obstacles. Each vehicle is equipped with an ad-hoc dual-mode controller in charge of tracking along the leader-follower chain the predecessor vehicle (namely the target for the leader) and ensuring absence of collisions. Moreover, whenever the leader recognizes that the obstacle scenario obstructs its navigation, the current formation is properly reconfigured by preserving recursive feasibility and obstacle avoidance properties.
A resilient control strategy against replay attacks is developed for discrete-time linear systems subject to state and input constraints, bounded disturbances and measurement noises. In particular operating scenarios, where adversaries act on the communication network by maliciously repeating data transmitted from the sensor to the controller, are investigated. The idea is to customize basic model predictive control schemes for detection attack and resilient control action purposes by exploiting set-theoretic and feasibility arguments proper of the receding control horizon philosophy.