Self-Tuning Sliding Mode Control for an Uncertain Coaxial Octorotor UAV

被引:28
作者
Xiong, Jing-Jing [1 ]
Guo, Nai-Huan [1 ]
Mao, Jun [1 ]
Wang, Hui-Di [2 ]
机构
[1] China Jiliang Univ, Coll Mech & Elect Engn, Hangzhou 310018, Peoples R China
[2] China Jiliang Univ, Coll Sci, Hangzhou 310018, Peoples R China
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2023年 / 53卷 / 02期
基金
中国国家自然科学基金;
关键词
Uncertainty; Autonomous aerial vehicles; Manifolds; Aerodynamics; Adaptation models; Sliding mode control; Rotors; Coaxial octorotor UAV (COUAV); radial basis function neural network (RBFNN); sliding mode control (SMC); trajectory tracking control; TRAJECTORY TRACKING; ATTITUDE-CONTROL; SYSTEMS;
D O I
10.1109/TSMC.2022.3193377
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Sliding mode control (SMC), which is well known to its insensitivity to parameter uncertainties and robustness against various disturbances, has been widely adopted for some types of underactuated systems, including multirotor unmanned aerial vehicles (UAVs). This article is absorbed in developing a self-tuning SMC strategy to perform the robust and adaptive tracking control for an uncertain coaxial octorotor UAV (COUAV) with the actuator faults. Such a strategy is composed of manifold coefficients, which are adjusted by employing gradient descent method. It is also composed of continuous controllers that are designed by utilizing the radial basis function neural network (RBFNN) to approximate the total disturbances, where the approximation errors of RBFNN are estimated by using the adaptive control method. Moreover, the developed SMC strategy allows tuning the adaptive coefficients of the constructed manifolds, eliminating the inherent chattering phenomenon, and addressing the robust and adaptive tracking control problems for an uncertain COUAV. Comparative simulation results are carried out to illustrate the effectiveness of the developed SMC strategy.
引用
收藏
页码:1160 / 1171
页数:12
相关论文
共 44 条
[1]   Simplified fuzzy-Pade controller for attitude control of quadrotor helicopters [J].
Abdollahi, Taleb ;
Salehfard, Sepideh ;
Xiong, Cai-Hua ;
Ying, Jiang-Feng .
IET CONTROL THEORY AND APPLICATIONS, 2018, 12 (02) :310-317
[2]   Trajectory Tracking Control of Planar Underactuated Vehicles [J].
Ashrafiuon, Hashem ;
Nersesov, Sergey ;
Clayton, Garrett .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2017, 62 (04) :1959-1965
[3]   Sliding mode control of underactuated multibody systems and its application to shape change control [J].
Ashrafiuon, Hashem ;
Erwin, R. Scott .
INTERNATIONAL JOURNAL OF CONTROL, 2008, 81 (12) :1849-1858
[4]   Robust Backstepping Sliding-Mode Control and Observer-Based Fault Estimation for a Quadrotor UAV [J].
Chen, Fuyang ;
Jiang, Rongqiang ;
Zhang, Kangkang ;
Jiang, Bin ;
Tao, Gang .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2016, 63 (08) :5044-5056
[5]   Modeling and robust backstepping sliding mode control with Adaptive RBFNN for a novel coaxial eight-rotor UAV [J].
Peng, Cheng ;
Bai, Yue ;
Gong, Xun ;
Gao, Qingjia ;
Zhao, Changjun ;
Tian, Yantao .
IEEE/CAA Journal of Automatica Sinica, 2015, 2 (01) :56-64
[6]   Higher Order Sliding Mode Control Using Discontinuous Integral Action [J].
Cruz-Zavala, Emmanuel ;
Moreno, Jaime A. .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2020, 65 (10) :4316-4323
[7]   Extended State Observer-Based Integral Sliding Mode Control for an Underwater Robot With Unknown Disturbances and Uncertain Nonlinearities [J].
Cui, Rongxin ;
Chen, Lepeng ;
Yang, Chenguang ;
Chen, Mou .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2017, 64 (08) :6785-6795
[8]   Second-order sliding mode controller design subject to mismatched term [J].
Ding, Shihong ;
Li, Shihua .
AUTOMATICA, 2017, 77 :388-392
[9]   Attractive Ellipsoid-Based Robust Control for Quadrotor Tracking [J].
Falcon, Romeo ;
Rios, Hector ;
Mera, Manuel ;
Dzul, Alejandro .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2020, 67 (09) :7851-7860
[10]   Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure [J].
Fei, Juntao ;
Lu, Cheng .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 29 (04) :1275-1286