Robust adaptive dynamical neural control for uncertain chaotic system

Abstract

Robust adaptive control of chaotic system with uncertainty was investigated in the presence of modeling error. The scheme of adaptation was based on identification estimates via dynamical neural networks. By using proposed nonlinear adaptive controller, the chaotic signal of the unknown system dynamics tends to be driven into a well controlled steady state. Moreover, the mathematical proof of stability properties of the system was guaranteed. Finally, simulation results have demonstrated the effectiveness of the proposed method through application on the Chen's chaotic system.