Adaptive neural network control for unified chaotic systems with dead-zone input

An adaptive control scheme is studied for unified chaotic systems with unknown function and dead-zone input. Because uncertain nonlinear property is included in the considered unified chaotic systems, the neural networks are used to approximate the uncertainties. An adaptive technique is employed to construct the neural controllers and compensate for the dead-zone parameters. By using the scheme, the chaotic phenomena for unified chaotic systems are overcome. It is proven that the proposed algorithm can guarantee that all the signals in the closed-loop system are bounded and the system states can converge to a neighborhood of zero based on the Lyapunov analysis method. The simulation example for a unified chaotic system is provided to demonstrate the effectiveness of the proposed method.