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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.18 n.2 México Apr./Jun. 2014

http://dx.doi.org/10.13053/CyS-18-2-2014-040 

Artículos regulares

 

Trajectory Tracking for Chaos Synchronization via PI Control Law between Roosler-Chen

 

Seguimiento de trayectorias para sincronización de caos vía ley de control PI entre Roosler-Chen

 

Joel Perez Padron, Jose Paz Perez Padron, Francisco Rodriguez Ramirez, and Angel Flores Hernandez

 

Facultad de Ciencias Físico-Matemáticas, Universidad Autónoma de Nuevo León (UANL), Monterrey, Mexico. joelperezp@yahoo.com, josepazp@gmail.com, francisco_rdz_rmz@hotmail.com

 

Abstract

This paper presents an application of adaptive neural networks based on a dynamic neural network to trajectory tracking of unknown nonlinear plants. The main methodologies on which the approach is based are recurrent neural networks and Lyapunov function methodology and Proportional-Integral (PI) control for nonlinear systems. The proposed controller structure is composed of a neural identifier and a control law defined by using the PI approach. The new control scheme is applied via simulations to Chaos Synchronization. Experimental results have shown the usefulness of the proposed approach for Chaos Production. To verify the analytical results, an example of a dynamical network is simulated and a theorem is proposed to ensure tracking of the nonlinear system.

Keywords: Dynamic neural networks, chaos production, chaos synchronization, trajectory tracking, Lyapunov function stability, PI control.

 

Resumen

Este artículo presenta la aplicación de redes neuronales adaptables, basada sobre una red neuronal dinámica, para seguimiento de trayectorias de plantas no lineales desconocidas. La principal metodología, sobre el cual la aproximación es basada, son redes neuronales recurrentes, metodología de las funciones de Lyapunov y control Proporcional-Integral (PI) para sistemas no lineales. La estructura del controlador propuesto es compuesta de un identificador neuronal y una ley de control definida usando la aproximación PI. El nuevo esquema de control es aplicado vía simulación para sincronización de caos. Resultados experimentales han mostrado la utilidad del enfoque propuesto para la producción de caos. Para verificar el resultado analítico, un ejemplo de una red dinámica es simulado y un teorema es propuesto para asegurar el seguimiento del sistema no lineal.

Palabras clave: Red neuronal dinámica, producción de caos, sincronización de caos, seguimiento de trayectorias, estabilidad de funciones de Lyapunov, control PI.

 

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