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

Joel Perez Padron, Jose P Perez, Francisco Rodríguez, Angel Flores


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.


Dynamic neural networks; chaos production; chaos synchronization; trajectory tracking; Lyapunov function stability; PI control

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