Name
Abstract | ||
|---|---|---|
Most existing results do not take the effects of backlash hysteresis of actuators into account in a controller design of missile systems, but such hysteresis seems inevitable in practice. In this paper, a robust adaptive neural network (NN) control law for a missile system with unknown parameters and hysteresis input is proposed based on a backstepping technique. The controller is designed by introducing NN approximation, which can be adjusted by an adaptive law based on the backstepping approach. The developed NN controller does not require a priori knowledge of the unknown backlash hysteresis. In particular, unlike existing results on adaptive compensation for unknown backlash hysteresis, the sign of b is no longer needed. It is shown that the designed controller can ensure the stability and tracking performance of the closed-loop system. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ACCESS.2017.2726186 | IEEE ACCESS |
Keywords | Field | DocType |
Missle system,adaptive control,neural network,hysteresis,autopilot,aerodynamics | Backstepping,Control theory,Backlash,Missile,Control theory,Adaptive system,Computer science,A priori and a posteriori,Hysteresis,Artificial neural network | Journal |
Volume | ISSN | Citations |
5 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianping Cai | 1 | 212 | 21.57 |
| lantao xing | 2 | 302 | 11.66 |
| Meng Zhang | 3 | 8 | 1.51 |
| Lujuan Shen | 4 | 1 | 3.06 |