BOOLEAN CONTROL NETWORKS

Collaborations with: ETTORE FORNASINI, HONGSHENG QI, GUODONG SHI, HE KONG.

TOPIC DESCRIPTION: Research   in  Boolean networks (BNs)  and Boolean control networks (BCNs) AND THEIT PROBABILISTIC VERSIONS has a  long tradition. The  renewed interest witnessed in recent times, however, must be mainly credited to two reasons: on the one hand, BNs and BCNs (AS WELL AS PBNS AND PBCNS)  have  proved to be  effective modeling tools for a number of rapidly evolving research topics,  like  genetic regulation networks  and consensus problems.

On the other hand, the  algebraic framework   developed by D. Cheng and co-authors has allowed   to  cast  both BNs and BCNs into the framework of linear state-space models (operating  on canonical vectors), thus benefitting of a  number of powerful algebraic tools, in addition to   traditional graph-based techniques.

PROBLEMS INVESTIGATED: FEEDBACK STABILIZATION, RECONSTRUCTABILITY, OBSERVABILITY OF PROBABILISTIC BOOLEAN NETWORKS, KOOPMAN THEORY FOR LOGICAL DYNAMICAL SYSTEMS.

MULTI-AGENT SYSTEMS, CONSENSUS PROBLEMS AND SOCIAL NETWORKS

Collaborations with: FRANCESCO BULLO, GIULIA DE PASQUALE, GIORGIA DISARO’ AND GIANFRANCO PARLANGELI.

TOPIC DESCRIPTION: MULTI-AGENT SYSTEMS AND CONSENSUS PROBLEMS Have been the subject of intensive research in the last 20 years. their success is highly motivated by the large number of problems that can be equivalently rephrased as consensus problems, namely as problems in which a group of agents share information at a local level and apply distributed control alGorIthms with the goal of converging to a common decision.

SOCIAL NETWORKS ARE SPECIAL KINDS OF MULTI-AGENT SYSTEMS. IN THIS CONTEXT OPINION DYNAMICS HAS RECEIVED LOT OF ATTENTION AND SEVERAL MODELS HAVE BEEN PROPOSED, HIGHLIGHTING DIFFERENT ATTITUDES OF HUMAN BEINGS, AND THE WAY THEY AFFECT THE EVOLUTION OF THEIR OPINIONS.

PROBLEMS INVESTIGATED: TRIPARTITE CONSENSUS, ALGORITHMS TO INCREASE THE CONVERGENCE SPEED TO CONSENSUS, THE MUTUAL EFFECT OF OPINIONS  AND APPRAISALS, MULTI-DIMENSIONAL BOUNDED CONFIDENCE MODELS, DISTRIBUTION MODLES FOR COLLABORATIVE TASKS, EXTENDED FRIEDKIN-JOHNSEN MODELS.

DATA-DRIVEN APPROACH TO UNKNOWN INPUT OBSERVERS DESIGN

Collaborations with: GIORGIA DISARO’, WENJIE LIU, JIAN SUN, GANG WANG, YUZHOU WEI.

TOPIC DESCRIPTION: THE PROBLEM OF ESTIMATING THE STATE OF An lti SYSTEM AFFECTED BY UKNOWN DISTURBANCES, BAsed on the available input and output measurements, has been the subject of intensive interest in the ninenties. in recent times, on the other hand, data-driven approaches have become pervasive as efficient means to achieve a desired goal without passing through the model identification.

PROBLEMS INVESTIGATED: data-driven UIOs, data-driven reduced order uios, distributed data-driven uios.