FisMat2017 - Submission - View

Abstract's title: Control of multidimensional systems on complex network
Submitting author: Giulia Cencetti
Affiliation: Università degli Studi di Firenze, Dipartimento di Ingegneria dell'Informazione and CSDC
Affiliation Address: via S. Marta, 3 50139 Firenze
Country: Italy
Oral presentation/Poster (Author's request): Oral presentation
Other authors and affiliations: Franco Bagnoli (Università degli Studi di Firenze, Dipartimento di Fisica e Astronomia), Giorgio Battistelli (Università degli Studi di Firenze, Dipartimento di Ingegneria dell'Informazione), Luigi Chisci (Università degli Studi di Firenze, Dipartimento di Ingegneria dell'Informazione), Duccio Fanelli (Università degli Studi di Firenze, Dipartimento di Fisica e Astronomia)
Abstract
Multidimensional systems coupled via complex networks are widespread in nature and thus frequently invoked for a large plethora of interesting applications.
From ecology to physics, individual entities in mutual interactions are grouped in families, homogeneous in kind. These latter interact selectively, through a sequence of self-consistently regulated steps, whose deeply rooted architecture is stored in the assigned matrix of connections. The asymptotic equilibrium eventually attained by the system, and its associated stability, can be assessed by employing standard nonlinear dynamics tools. For many practical applications, it is however important to externally drive the system towards a desired equilibrium, which is resilient, hence stable, to external perturbations. To this end we here consider 
a system made up of N interacting populations which evolve according to general rate equations, bearing attributes of universality. One species is added to the pool of interacting families and used as a dynamical controller to induce novel stable equilibria. Use can be made of the root locus method to shape the needed control, 
in terms of intrinsic reactivity and adopted protocol of injection.  The proposed method is tested on both synthetic and real data, thus enabling to demonstrate its robustness and  versatility.