A simple Adaptive Neural Fuzzy Design for Hyper-Chaos Control under External Disturbance

In this paper, an adaptive neural fuzzy control method is proposed to control hyper-chaotic dynamic systems. Due to the inherent complexity of hyper-chaotic systems, the neural fuzzy network training architecture is described, which includes the type and number of training inputs. The training data for the adaptive neural fuzzy network is based on nonlinear control. In other words, the data of a nonlinear controller, which includes error vectors and control input vectors, is used for neural fuzzy training. The numerical simulation results show that the proposed method is suitable for controlling hyper-chaotic systems. After the controller design, an external disturbance is added to the model to evaluate the performance of the ANFIS controller. The time to reach zero error and the behavior of the control signal are discussed, this can be an important issue in real-world implementation and manufacturing.