Eur. Phys. J. Plus (2018) 133: 14
https://doi.org/10.1140/epjp/i2018-11840-4

Regular Article

Adaptative synchronization in multi-output fractional-order complex dynamical networks and secure communications

¹ Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, Academia de Mecatrónica, Av. IPN 2580, 07340 D.F., Ciudad de México, Mexico
² CINVESTAV-IPN, Departamento de Biotecnología y Bioingeniería, Av. IPN 2508, 07360 D.F., Ciudad de México, Mexico

^* e-mail: jmatam@ipn.mx

Received: 28 August 2017
Accepted: 19 December 2017
Published online: 17 January 2018

Abstract

This work deals with the adaptative synchronization of complex dynamical networks with fractional-order nodes and its application in secure communications employing chaotic parameter modulation. The complex network is composed of multiple fractional-order systems with mismatch parameters and the coupling functions are given to realize the network synchronization. We introduce a fractional algebraic synchronizability condition (FASC) and a fractional algebraic identifiability condition (FAIC) which are used to know if the synchronization and parameters estimation problems can be solved. To overcome these problems, an adaptative synchronization methodology is designed; the strategy consists in proposing multiple receiver systems which tend to follow asymptotically the uncertain transmitters systems. The coupling functions and parameters of the receiver systems are adjusted continually according to a convenient sigmoid-like adaptative controller (SLAC), until the measurable output errors converge to zero, hence, synchronization between transmitter and receivers is achieved and message signals are recovered. Indeed, the stability analysis of the synchronization error is based on the fractional Lyapunov direct method. Finally, numerical results corroborate the satisfactory performance of the proposed scheme by means of the synchronization of a complex network consisting of several fractional-order unified chaotic systems.