Abstract

PEREZ PADRON, Joel; PEREZ PADRON, Jose Paz; RODRIGUEZ RAMIREZ, Francisco  and  FLORES HERNANDEZ, Angel. Trajectory Tracking for Chaos Synchronization via PI Control Law between Roosler-Chen. Comp. y Sist. [online]. 2014, vol.18, n.2, pp.399-407. ISSN 2007-9737.  https://doi.org/10.13053/CyS-18-2-2014-040.

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.

        · abstract in Spanish     · text in English     · English ( pdf )