Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

被引：72
|
作者：
Ouyang, Yuncheng [1 ,2 ]
Dong, Lu [1 ,2 ]
Xue, Lei [1 ,2 ]
Sun, Changyin [1 ,2 ]
机构：
[1] Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China
[2] Southeast Univ, Minist Educ, Key Lab Measurement & Control Complex Syst Engn, Nanjing 210096, Jiangsu, Peoples R China
来源：
IEEE-CAA JOURNAL OF AUTOMATICA SINICA | 2019年 / 6卷 / 03期
基金：
中国国家自然科学基金;
关键词：
2-degree of freedom (DOF) helicopter; adaptive control; input deadzone; integral barrier Lyapunov function; neural networks; output constraints; NONLINEAR-SYSTEMS; LEARNING CONTROL;
D O I：
10.1109/JAS.2019.1911495
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a study of control for an uncertain 2-degree of freedom (DOF) helicopter system is given. The 2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function (IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.
引用
收藏
页码：807 / 815
页数：9
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