Feedback compensation control on chaotic system with uncertainty based on radial basis function neural network

Abstract

For the problem of controlling uncertain chaotic systems, a method of feedback compensation control based on the radial basis function neural network (RBFNN) is studied. In the proposed method, dynamic properties of chaotic system is first trained by RBFNN, and then feedback compensation control for chaotic system is implemented using trained good RBFNN model. The characteristics of this method is that this method can quickly track any given reference signal with on requirement for any mathematic model of controlled chaos system. The numerical simulation results show that the proposed control method not only has the fast response speed, high control accuracy, but also has a stronger ability to suppress parameter perturbation and to resist interference to chaos system.