Name
Title | ||
|---|---|---|
Adaptive Neural Discrete-Time Fractional-Order Control for a UAV System With Prescribed Performance Using Disturbance Observer |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TSMC.2018.2882153 | IEEE Transactions on Systems, Man, and Cybernetics: Systems |
Keywords | DocType | Volume |
Backstepping control,discrete-time (DT) nonlinear systems,disturbance observer,neural network (NN),unmanned aerial vehicle (UAV) | Journal | 51 |
Issue | ISSN | Citations |
2 | 2168-2216 | 4 |
PageRank | References | Authors |
0.39 | 12 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuyi Shao | 1 | 56 | 4.12 |
| Mou Chen | 2 | 1251 | 59.31 |