A dual-input fuzzy parameter zeroing neural network for robust synchronization of chaotic systems

As a research hotspot in control and engineering, chaotic systems synchronization has attracted considerable attention. However, traditional control methods are difficult to ensure that chaotic systems realize synchronization within a predefined time, and they also lack robustness against external disturbances. Therefore, chaotic systems synchronizations based on zeroing neural network (ZNN) have achieved great development in recent years. A dual-input fuzzy parameter zeroing neural network (DIFPZNN) is proposed to realize fast synchronization and adaptive disturbance suppression for chaotic systems without measuring unknown bounded external disturbances. Rigorous theoretical analysis and comparative simulations verify its convergence and disturbance attenuation performance for disturbances imposed only on the master chaotic system, rather than general universal robustness against all types of perturbations. In addition, comparative simulation results of the proposed DIFPZNN model with the other three recently reported models for the synchronization of the Qi chaotic systems further validate the superior performance of the proposed DIFPZNN model. Finally, the hardware implementation is completed based on the field programmable gate array (FPGA), and the synchronization process of the Qi chaotic system controlled by the DIFPZNN model is demonstrated on an oscilloscope. Meanwhile, the DIFPZNN-based Qi chaotic synchronization method is applied to signal encryption, which verifies the practical application value of the proposed model. The proposed controller achieves synchronization without measuring external disturbances.