Paolo Frasca – författare

Hybrid and Networked Dynamical Systems treats a class of systems that is ubiquitous in everyday life. From energy grids to fleets of robots or vehicles to social networks to biological networks, the same scenario arises: dynamical units interact locally through a connection graph to achieve a global task. The book shows how analysis and design tools can be adapted for control applications that combine the effects of network-induced interactions and hybrid dynamics with complex results.

Following a scene-setting introduction, the remaining 12 chapters of the book are divided into three parts and provide a unique opportunity to describe the big picture that is the culmination of years of recent research activity. The contributing authors expand on their ideas at greater length than is possible in an archival research paper and use in-depth examples to illustrate their theoretical work.

The widespread importance of hybrid and networked systems means that the book is of significant interest to academic researchers working in applied mathematics, control, and electrical, mechanical and chemical engineering and to their industrial counterparts.

This book deals with averaging dynamics, a paradigmatic example of network based dynamics in multi-agent systems. The book presents all the fundamental results on linear averaging dynamics, proposing a unified and updated viewpoint of many models and convergence results scattered in the literature.

Starting from the classical evolution of the powers of a fixed stochastic matrix, the text then considers more general evolutions of products of a sequence of stochastic matrices, either deterministic or randomized. The theory needed for a full understanding of the models is constructed without assuming any knowledge of Markov chains or Perron–Frobenius theory. Jointly with their analysis of the convergence of averaging dynamics, the authors derive the properties of stochastic matrices. These properties are related to the topological structure of the associated graph, which, in the book’s perspective, represents the communication between agents. Special attention is paid to how these properties scale as the network grows in size.

Finally, the understanding of stochastic matrices is applied to the study of other problems in multi-agent coordination: averaging with stubborn agents and estimation from relative measurements. The dynamics described in the book find application in the study of opinion dynamics in social networks, of information fusion in sensor networks, and of the collective motion of animal groups and teams of unmanned vehicles. Introduction to Averaging Dynamics over Networks will be of material interest to researchers in systems and control studying coordinated or distributed control, networked systems or multiagent systems and to graduate students pursuing courses in these areas.