Observer-based adaptive neural network control design for projective synchronization of uncertain chaotic systems

This paper addresses the design of an observer-based adaptive neural network chaos synchronization scheme for a general class of uncertain chaotic systems. The controller consists of an adaptive neural network control law and an extended state observer. The parameterization of the designed extended observer and the sufficient stability conditions are derived in the light of the singular perturbation theory. The extended observer is incorporated into the controller to reconstruct the synchronization error vector as well as to estimate the error between an ideal control law and the actual control. These estimated error signals are utilized in the adaptation mechanism of the neural network weight vector. In the presented chaos synchronization method, the knowledge of the models of the master-slave systems is not required and the controller only needs the projective synchronization error for its implementation. Numerical simulations are performed along with a comparative study to demonstrate the efficiency and effectiveness of the suggested chaos synchronization approach.

Automated summary

This paper proposes an observer-based adaptive neural network chaos synchronization scheme for a general class of uncertain chaotic systems. The scheme uses an adaptive neural network control law and an extended state observer to achieve synchronization. The estimated error signals are utilized in the adaptation mechanism of the neural network weight vector. Compared to traditional methods, this scheme does not require knowledge of the models of the master-slave systems, but only requires the projective synchronization error.