Adaptive Neural Network Control of an Uncertain 2-DOF Helicopter With Unknown Backlash-Like Hysteresis and Output Constraints

被引:31
|
作者
Zhao, Zhijia [1 ,2 ]
Zhang, Jian [1 ,2 ]
Liu, Zhijie [3 ,4 ]
Mu, Chaoxu [5 ]
Hong, Keum-Shik [6 ]
机构
[1] Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou 510006, Peoples R China
[2] Guangdong Lab Artificial Intelligence & Digital E, Shenzhen 518060, Peoples R China
[3] Univ Sci & Technol Beijing, Inst Artificial Intelligence, Beijing 100083, Peoples R China
[4] Univ Sci & Technol Beijing, State Key Lab Adv Met, Beijing 100083, Peoples R China
[5] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
[6] Pusan Natl Univ, Sch Mech Engn, Busan 46241, South Korea
基金
中国国家自然科学基金;
关键词
Helicopters; Hysteresis; Artificial neural networks; Nonlinear dynamical systems; Uncertainty; Adaptation models; MIMO communication; Adaptive neural network (NN) control; output constraint; two-degree of freedom (2-DOF) helicopter; unknown backlash-like hysteresis; TRACKING CONTROL; NONLINEAR-SYSTEMS; STATE CONSTRAINTS; LQR;
D O I
10.1109/TNNLS.2022.3163572
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
An adaptive neural network (NN) control is proposed for an unknown two-degree of freedom (2-DOF) helicopter system with unknown backlash-like hysteresis and output constraint in this study. A radial basis function NN is adopted to estimate the unknown dynamics model of the helicopter, adaptive variables are employed to eliminate the effect of unknown backlash-like hysteresis present in the system, and a barrier Lyapunov function is designed to deal with the output constraint. Through the Lyapunov stability analysis, the closed-loop system is proven to be semiglobally and uniformly bounded, and the asymptotic attitude adjustment and tracking of the desired set point and trajectory are achieved. Finally, numerical simulation and experiments on a Quanser's experimental platform verify that the control method is appropriate and effective.
引用
收藏
页码:10018 / 10027
页数:10
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