Efficient Learning Control of Uncertain Fractional-Order Chaotic Systems With Disturbance

In this brief, the problem of synchronization control is investigated for a class of fractional-order chaotic systems with unknown dynamics and disturbance. The controller is constructed using neural approximation and disturbance estimation where the system uncertainty is modeled by neural network (NN) and the time-varying disturbance is handled using disturbance observer (DOB). To evaluate the estimation performance quantitatively, the serial-parallel estimation model is constructed based on the compound uncertainty estimation derived from NN and DOB. Then, the prediction error is constructed and employed to design the composite fractional-order updating law. The boundedness of the system signals is analyzed. The simulation results show that the proposed new design scheme can achieve higher synchronization accuracy and better estimation performance.

Automated summary

This paper investigates the synchronization control problem for a class of fractional-order chaotic systems with unknown dynamics and disturbance. A new design scheme is proposed to achieve higher synchronization accuracy and better estimation performance. The controller is constructed using neural approximation and disturbance estimation, and the simulation results demonstrate the effectiveness of the proposed approach.