Artículos

 

PID Control Law for Trajectory Tracking Error Using Time-Delay Adaptive Neural Networks for Chaos Synchronization

 

Joel Pérez P. y José P. Pérez

 

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

Corresponding author is Joel Pérez P.

 

Article received on 12/11/2013.
Accepted on 01/09/2014.

 

Abstract

This paper presents an application of Time-Delay adaptive neural networks based on a dynamic neural network for trajectory tracking of unknown nonlinear plants. Our approach is based on two main methodologies: the first one employs Time-Delay neural networks and Lyapunov-Krasovskii functions and the second one is Proportional-Integral-Derivative (PID) control for nonlinear systems. The proposed controller structure is composed of a neural identifier and a control law defined by using the PID 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 the tracking of the nonlinear system.

Keywords: Lyapunov-Krasovskii function stability, chaos synchronization, trajectory tracking, time-delay adaptive neural networks, PID control.

 

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Acknowledgements

The authors appreciate the support from CONACYT and the Dynamical Systems Group of the Faculty of Physical and Mathematical Sciences of the Autonomous University of Nuevo León, México.

 

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