Tracking control for uncertain fractional-order chaotic systems based on disturbance observer and neural network

This article proposes an adaptive neural tracking control scheme for uncertain fractional-order chaotic systems (FOCSs) subject to unknown disturbance and input saturation. To tackle the system uncertainty in the FOCS, the radial basis function neural network (RBFNN) is employed. Furthermore, the sliding mode fractional-order disturbance observer (SMFODO) is designed to estimate the unknown disturbance. Using the backstepping technique, an adaptive neural control is proposed for uncertain FOCSs by employing the RBFNN and the developed SMFODO. To avoid the tedious analytic computation in the backstepping method, a fractional-order differentiator is introduced. The stability is proved via fractional-order analysis method for the whole closed-loop system in the presence of the system uncertainty, the input saturation and the unknown external disturbance. Simulation results of the fractional-order chaotic electronic oscillator model are presented to illustrate the effectiveness of the proposed adaptive neural tracking control scheme.