Control and Synchronization of a Class of Chaotic Systems by Using a Lyapunov Based Model Reference Rough Network Controller

This study intends to investigate a new control str ucture using a model reference radial basis function neural network controller wit h feedback error learning to control of a class of nonlinear systems subject to unknown bounded uncertainty. The proposed controller is hybrid form and includes the classic controller and rough network controller. Due to use of the classic contr oller with the neural network controller, it is expected that the transient response is bounded. The weights of the output layer of the neural network controller are interval varia and also according to the output of the classic con troller and based on stability, the stable adaptation laws for these weights are derived. The simulation results which are applied to Duffing Oscillator and Gen approach. In addition, the suggested manner is comp ared with the simple model reference radial basis function neural network controller whi ch demonstrates the superiority and robustness of the controller, synchronization of the mentioned chaoti c systems is performed. The results verified the high accuracy and effectiveness of the proposed controller. This study intends to investigate a new control str ucture using a model reference radial basis function neural network controller wit h feedback error learning to control of a class of nonlinear systems subject to unknown bounded uncertainty. The proposed controller is hybrid form and includes the classic controller and rough - radi network controller. Due to use of the classic contr oller with the neural network controller, it is expected that the transient response is bounded. The weights of the output layer of the neural network controller are interval varia bles. Using an appropriate Lyapunov function and also according to the output of the classic con troller and based on stability, the stable adaptation laws for these weights are derived. The simulation results which are applied to Duffing Oscillator and Gen esio- Tesi systems verify the efficacy of the presented c ontrol approach. In addition, the suggested manner is comp ared with the simple model reference radial basis function neural network controller whi ch demonstrates the superiority and robustness of the proposed method in presence of uncertainty. Also, u sing the proposed controller, synchronization of the mentioned chaoti c systems is performed. The results verified the high accuracy and effectiveness of the proposed controller.