Designing full-order sliding mode controller based on neural network for a class of chaotic systems

In this study, Full-order sliding mode control based on neural network is proposed to control chaos in nonlinear systems. At the first, for chaotic systems, a full-order sliding mode controller is designed for chaotic system with uncertainty and disturbance. This method isn’t used for chaotic systems. The full-order sliding mode controller is a novel method to design controllers for nonlinear systems in canonical form, which can meet the problem of chattering and singularity existed in sliding mode control methods. Then with the assumption that a part of system dynamic is uncertain Radial basis function (RBF) network is applied for uncertain dynamic estimation of system based on input-output data and a new controller is proposed for chaotic systems. Stability of the proposed method is proved based on Lyapunov theorem. Simulation are carried on a chaotic systems named and the simulation results show appropriate performance of the proposed controller.