Adaptive Sliding mode Control Based on RBF Neural Network Approximation for Quadrotor

被引：4
|
作者：
Alqaisi, Walid Kh. [1 ]
Brahmi, Brahim [1 ]
Ghommam, Jawhar [2 ]
Saad, Maarouf [1 ]
Nerguizian, Vahe [1 ]
机构：
[1] Univ Quebec, ETS, Elect Engn Dept, Montreal, PQ H3C 1K3, Canada
[2] Sultan Quaboos Univ, Dept Elect & Comp Engn, Muscat 123, Oman
来源：
2019 IEEE INTERNATIONAL SYMPOSIUM ON ROBOTIC AND SENSORS ENVIRONMENTS (ROSE 2019) | 2019年
关键词：
Adaptive control; quadrotor; RBF neural network; sliding-mode; UAV; TRACKING;
D O I：
10.1109/rose.2019.8790423
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper addresses the design of a robust adaptive sliding mode tracking control approach utilizing a Radial Basis Function Neural Network RBF NN for quadrotor. The proposed system has great advantages in dealing with nonlinearities and it has the ability to approximate uncertainties. The output of the neural network is used as a compensator parameter in order to eliminate system uncertainties. Consequently, fast error convergence in the closed loop control system can be achieved. A preliminary study to apply the system in an agricultural application using visual sensing is introduced and tested. The proposed system stability is proved by Lyapunov analysis, simulation and experimental implementation.
引用
收藏
页码：77 / 83
页数：7
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