Adaptive Neural Discrete-Time Fractional-Order Control for a UAV System With Prescribed Performance Using Disturbance Observer

In this paper, an adaptive neural discrete-time (ANDT) fractional-order tracking control scheme is proposed for an unmanned aerial vehicle system with prescribed performance in the presence of system uncertainties and unknown bounded disturbances based on a discrete-time disturbance observer (DTDO). The system uncertainties are handled using neural network (NN) approximation. To compensate for the adverse effects of unknown disturbances, an NN-based DTDO is designed. On the basis of the NN, the designed DTDO and the backstepping technology, an ANDT fractional-order control scheme with prescribed performance is developed. Then, the tracking errors are convergent under the proposed control scheme. Finally, the effectiveness of the proposed discrete-time control scheme is demonstrated by numerical simulation results.

Automated summary

This paper proposes an adaptive neural discrete-time fractional-order tracking control scheme for an unmanned aerial vehicle system, which can handle system uncertainties and unknown disturbances while ensuring convergent tracking errors under prescribed performance. The effectiveness of the proposed control scheme is demonstrated through numerical simulation results.