Trajectory tracking error using fractional order time-delay recurrent neural networks using Krasovskii-Lur’e functional for Chua’s circuit via inverse optimal control | Zendy

Joel Pérez Padron | Zendy; CésarFernando MéndezBarrios | Zendy; Emilio J. González-Galván | Zendy

Open Access

Trajectory tracking error using fractional order time-delay recurrent neural networks using Krasovskii-Lur’e functional for Chua’s circuit via inverse optimal control

Author(s) -

Joel Pérez Padron,

CésarFernando MéndezBarrios,

Emilio J. González-Galván

Publication year - 2019

Publication title -

revista mexicana de física

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.181

H-Index - 25

eISSN - 2683-2224

pISSN - 0035-001X

DOI - 10.31349/revmexfis.66.98

Subject(s) - control theory (sociology) , trajectory , artificial neural network , tracking (education) , synchronization (alternating current) , tracking error , nonlinear system , fractional calculus , inverse , computer science , mathematics , stability (learning theory) , control (management) , topology (electrical circuits) , artificial intelligence , machine learning , psychology , pedagogy , physics , geometry , combinatorics , astronomy , quantum mechanics

This paper presents an application of a Fractional Order Time Delay Neural Networks to chaos synchronization. The two main methodologies, on which the approach is based, are fractional order time-delay recurrent neural networks and the fractional order inverse optimal control for nonlinear systems. The problem of trajectory tracking is studied, based on the fractional order Lyapunov-Krasovskii and Lur’e theory, that achieves the global asymptotic stability of the tracking error between a delayed recurrent neural network and a reference function is obtained. The method is illustrated for the synchronization, the analytic results we present a trajectory tracking simulation of a fractional order time-delay dynamical network and the Fractional Order Chua’s circuits

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