RBF Neural Network Fusion Disturbance Estimation for Robust Synchronization of Chaotic Systems

This paper addresses the problem of complete synchronization of chaotic systems subject to external disturbances and parameter uncertainties, proposing a robust control strategy based on radial basis function (RBF) neural network fusion with a disturbance estimator (DE)-based control method. Firstly, a dynamic feedback controller is designed to stabilize the nominal error system. Subsequently, a set of appropriate filters are constructed, and based on these filters, a disturbance estimator that can asymptotically track external disturbances is developed, thereby realizing asymptotic cancelation of external disturbance effects on the synchronization process. Then, an RBF-based compensator is designed to approximate the unmodeled uncertainties of the system with high precision, effectively suppressing the adverse impacts of uncertainties. By integrating the aforementioned dynamic feedback controller, disturbance estimator, and RBF-based compensator, robust complete synchronization between the master and slave chaotic systems is successfully achieved. Finally, a numerical simulation example is presented to validate the feasibility and effectiveness of the proposed control scheme.