Stochastic hybrid systems: An introduction on modeling and stability analysis

At 10:30 - 11:30 
Department of Industrial Engineering, Girasole room, via Sommarive 9, Povo - Trento

• Antonino Sferlazza, University of Palermo, Palermo, Italy

Abstract

Stochastic hybrid systems are dynamical systems that capture the interaction of continuous-time and discrete-time phenomena including also random effects. They represent a very important and general class of systems, and they constitute a starting point for many applications such as financial systems, air traffic management systems, communication networks and networked control systems, biological systems, and power systems.

In this talk, some important models present in the literature will be introduced, which include stochastic switched systems, Markov jump systems, impulsive stochastic systems, switching diffusions, stochastic impulsive systems driven by renewal processes, diffusions driven by Lévy processes, piecewise-deterministic Markov processes, general stochastic hybrid systems, and stochastic hybrid inclusions. During the presentation the common features and the differences among them will be emphasized, introducing a common structure for most models. Subsequently some of the stability concepts for stochastic hybrid systems will be introduced, including Lyapunov stability, Lagrange stability, and asymptotic stability. Some open problems will be finally discussed.

Biography

Antonino Sferlazza was born in Palermo, Italy, in November 1987. He received the Master’s degree in automation engineering and the Ph.D. degree in control system engineering from the University of Palermo, Palermo, Italy, in 2011 and 2015, respectively. Currently he is postdoctoral research fellow at University of Palermo. He is also research collaborator with the ISSIA CRN. He was also visiting PhD student at University of California at Santa Barbara, USA, and at LAAS CNRS, Toulouse, France.

His research interests include the development of feedback control algorithms for non- linear dynamical systems, estimation of stochastic dynamical systems, hybrid systems, and applications of control of electrical drives, power converters, and mechanical systems.