Name
Abstract | ||
|---|---|---|
This work draws inspiration from prescribed performance control, where a prescribed performance finite-time control framework is developed. A new finite-time form of performance function is defined to constrain the trajectory tracking errors. The proposed scheme provides a less-complex finite-time convergence guarantee for the closed-loop system. Besides, some required transient performances and steady-state precision can be preseted. Furthermore, the proposed framework combined with quaternion-based backstepping is employed to address the tracking problem of disturbed quadrotors. To amplify the robustness, composite learning approach which combines adaptive neural controller with nonlinear disturbance observer, is conducted to counteract the adverse effects from parametric uncertainties and time-varying external perturbations. Some comparative simulation results illustrate the superiority of the proposed flight controller. Additionally, the flight experiments are implemented to further demonstrate the effectiveness of the prescribed performance scheme. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.jfranklin.2020.03.021 | Journal of the Franklin Institute |
DocType | Volume | Issue |
Journal | 357 | 10 |
ISSN | Citations | PageRank |
0016-0032 | 1 | 0.35 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Jiang | 1 | 211 | 44.26 |
| Jiangshuai Huang | 2 | 386 | 18.80 |
| Bin Li | 3 | 10 | 3.26 |