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Adaptive neural stabilization control for unified chaotic systems with full state constraints

This paper presents an adaptive control strategy for a unified chaotic system with unknown functions and full state constraints. The unknown functions in the unified chaotic system are approximated by using the radial basis function neural networks. At present, a great many of the results for chaotic system neglect the situation of full state constraints. In the proposed scheme, we successfully utilize the barrier Lyapunov function approach to prevent the full state from violating constraint condition. In addition, when the full state constraints are considered, the computation online burden is very large in the previous works. In contrast with the existing results for unified chaotic system, the number of the adaptation laws has only two. The state feedback controllers and the adaptive laws for estimating the uncertainties were derived based on the Lyapunov stability theory. Finally, it is proved that all the signals in the unified chaotic system are bounded and the constraints are not violated. The performance of the proposed scheme was validated by using a simulation example.