Adaptive Asymptotic Tracking Control Without Singularity for a Class of Uncertain Quadrotors With Thrust Saturation

被引:2
作者
Ji, Yunfeng [1 ]
Wang, Gang [1 ]
Li, Wei [2 ]
Li, Qingdu [1 ]
Zhang, Jianwei [3 ]
机构
[1] Univ Shanghai Sci & Technol, Inst Machine Intelligence, Shanghai 200093, Peoples R China
[2] Shanghai Lixin Univ Accounting & Finance, Sch Finance, Shanghai 201209, Peoples R China
[3] Univ Hamburg, Dept Informat, D-20146 Hamburg, Germany
来源
IEEE ACCESS | 2021年 / 9卷
基金
中国国家自然科学基金;
关键词
Attitude control; Trajectory; Tracking loops; Position control; Adaptive control; Trajectory tracking; Control design; Adaptive tracking control; uncertain dynamics; backstepping control; COMMAND-FILTERED COMPENSATION; SWITCHED NONLINEAR-SYSTEMS; CONTROL DESIGN; CONTROL SCHEME; STABILIZATION; VEHICLE;
D O I
10.1109/ACCESS.2021.3100101
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A novel adaptive tracking control strategy without singularity is proposed for quadrotor systems subject to uncertain dynamic parameters. Based on the design technique of bounded control, a new differentiable saturated function is proposed and subsequently adopted to ensure that the developed translational thrust remains within the preassigned range in the position control loop. A technical lemma is established to analyze the relationship between the convergence of the attitude tracking error and the position tracking error in the presence of thrust saturation. Then, by the compensation term in the form of an attitude error function, a singularity-free torque controller, which can avoid analytic calculations involving the desired attitude derivative, is presented in the attitude control loop. Finally, we prove that the proposed method can achieve asymptotic tracking without singularity. Simulation results illustrating the effectiveness of the proposed strategy are exhibited.
引用
收藏
页码:104612 / 104625
页数:14
相关论文
共 41 条
[1]  
[Anonymous], 2019, ASIAN J CONTROL
[2]   Design and Implementation of Deep Neural Network-Based Control for Automatic Parking Maneuver Process [J].
Chai, Runqi ;
Tsourdos, Antonios ;
Savvaris, Al ;
Chai, Senchun ;
Xia, Yuanqing ;
Chen, C. L. Philip .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (04) :1400-1413
[3]   Real-Time Reentry Trajectory Planning of Hypersonic Vehicles: A Two-Step Strategy Incorporating Fuzzy Multiobjective Transcription and Deep Neural Network [J].
Chai, Runqi ;
Tsourdos, Antonios ;
Savvaris, Al ;
Xia, Yuanqing ;
Chai, Senchun .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2020, 67 (08) :6904-6915
[4]   Multi-objective trajectory optimization of Space Manoeuvre Vehicle using adaptive differential evolution and modified game theory [J].
Chai, Runqi ;
Savvaris, Al ;
Tsourdos, Antonios ;
Chai, Senchun .
ACTA ASTRONAUTICA, 2017, 136 :273-280
[5]  
Chang X.-H., 2014, ROBUST OUTPUT FEEDBA, V7
[6]   Observer-Based Adaptive Finite-Time Tracking Control for a Class of Switched Nonlinear Systems With Unmodeled Dynamics [J].
Chang, Yi ;
Zhang, Shuo ;
Alotaibi, N. D. ;
Alkhateeb, A. F. .
IEEE ACCESS, 2020, 8 :204782-204790
[7]   Nonlinear Control of Quadrotor for Point Tracking: Actual Implementation and Experimental Tests [J].
Choi, Young-Cheol ;
Ahn, Hyo-Sung .
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2015, 20 (03) :1179-1192
[8]   On consensus algorithms of multiple uncertain mechanical systems with a reference trajectory [J].
Dong, Wenjie .
AUTOMATICA, 2011, 47 (09) :2023-2028
[9]   Design of a secure communication system between base transmitter station and mobile equipment based on finite-time chaos synchronisation [J].
Hashemi, Somayeh ;
Pourmina, Mohammad Ali ;
Mobayen, Saleh ;
Alagheband, Mahdi R. .
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2020, 51 (11) :1969-1986
[10]   Immersion and invariance based command-filtered adaptive backstepping control of VTOL vehicles [J].
Hu, Jinchang ;
Zhang, Honghua .
AUTOMATICA, 2013, 49 (07) :2160-2167