Exponentially stable observer-based controller for VTOL-UAVs without velocity measurements

被引:14
作者
Hashim, Hashim A. [1 ]
机构
[1] Carleton Univ, Dept Mech & Aerosp Engn, Ottawa, ON K1S 5B6, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Unmanned aerial vehicle; nonlinear filter algorithm; autonomous navigation; tracking control; localisation; asymptotic stability; EXTENDED KALMAN FILTER; ATTITUDE DETERMINATION; CONVERGENCE; NAVIGATION;
D O I
10.1080/00207179.2022.2079004
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
There is a great demand for vision-based robotics solutions that can operate using Global Positioning Systems (GPS), but are also robust against GPS signal loss and gyroscope failure. This paper investigates the estimation and tracking control in application to a Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) in six degrees of freedom (6 DoF). A full state observer for the estimation of VTOL-UAV motion parameters (attitude, angular velocity, position, and linear velocity) is proposed on the Lie Group of SE2 (3) x R-3 = SO (3) x R-9 with almost globally exponentially stable closed-loop error signals. Thereafter, a full state observer-based controller for the VTOL-UAV motion parameters is proposed on the Lie Group with a guaranteed almost global exponential stability. The proposed approach produces good results without the need for angular and linear velocity measurements (without a gyroscope and GPS signals) utilising only a set of known landmarks obtained by a vision-aided unit (monocular or stereo camera). The equivalent quaternion representation on S-3 x R-9 is provided in the Appendix. The observer-based controller is presented in a continuous form while its discrete version is tested using a VTOL-UAV simulation that incorporates large initial error and uncertain measurements. The proposed observer is additionally tested experimentally on a real-world UAV flight dataset.
引用
收藏
页码:1946 / 1960
页数:15
相关论文
共 34 条
[1]   Robust Relative Navigation by Integration of ICP and Adaptive Kalman Filter Using Laser Scanner and IMU [J].
Aghili, Farhad ;
Su, Chun-Yi .
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2016, 21 (04) :2015-2026
[2]  
[Anonymous], 2014, IEEE INTCONF COMPUT
[3]  
[Anonymous], MOTION COORDINATION, P85104
[4]   The Invariant Extended Kalman Filter as a Stable Observer [J].
Barrau, Axel ;
Bonnabel, Silvere .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2017, 62 (04) :1797-1812
[5]   Quadrotor vehicle control via sliding mode controller driven by sliding mode disturbance observer [J].
Besnard, Lenaick ;
Shtessel, Yuri B. ;
Landrum, Brian .
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2012, 349 (02) :658-684
[6]   The EuRoC micro aerial vehicle datasets [J].
Burri, Michael ;
Nikolic, Janosch ;
Gohl, Pascal ;
Schneider, Thomas ;
Rehder, Joern ;
Omari, Sammy ;
Achtelik, Markus W. ;
Siegwart, Roland .
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 2016, 35 (10) :1157-1163
[7]   Indirect Kalman Filtering Based Attitude Estimation for Low-Cost Attitude and Heading Reference Systems [J].
Chang, Lubin ;
Zha, Feng ;
Qin, Fangjun .
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2017, 22 (04) :1850-1858
[8]   Image based fixed time visual servoing control for the quadrotor UAV [J].
Chen, Jiannan ;
Hua, Changchun ;
Guan, Xinping .
IET CONTROL THEORY AND APPLICATIONS, 2019, 13 (18) :3117-3123
[9]   Hierarchical backstepping-based control of a Gun Launched MAV in crosswinds: Theory and experiment [J].
Drouot, A. ;
Richard, E. ;
Boutayeb, M. .
CONTROL ENGINEERING PRACTICE, 2014, 25 :16-25
[10]   Geometric stochastic filter with guaranteed performance for autonomous navigation based on IMU and feature sensor fusion [J].
Hashim, Hashim A. ;
Abouheaf, Mohammed ;
Abido, Mohammad A. .
CONTROL ENGINEERING PRACTICE, 2021, 116