Design of RBF Neural Robust Controller with Differencial Reconstruction and for a Class of Nonlinear Uncertain Chaotic Systems

An adaptive neural robust controller was designed by using adaptive backstepping method for a class of nonlinear uncertain chaotic systems which could be turned to "standard block control type", Furthermore, It is possible to make the network more stable and make the selection of simulation parameter more easy due to the introduction of differential reconstruction which increased the damp of the system. The known dynamics are used to design a feedback controller which ensure the stability of the system. A neural network-based adaptive compensator is designed for compensation of the system uncertainties. It was proved by constructing Lyapunov function step by step that all signals of the system are bounded and exponentially converge to the neighborhood of the origin globally