Construction, modelling, and control of an autonomous unmanned aerial vehicle for target localization

2015 ◽  
Vol 61 (2) ◽  
pp. 23-35
Author(s):  
Amr Nagaty ◽  
Carl Thibault ◽  
Mae Seto ◽  
Michael Trentini ◽  
Howard Li
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Target Localization ◽  
Aerial Vehicle ◽  
And Control

Modeling and control of a quadrotor tail-sitter unmanned aerial vehicles

2021 ◽  
pp. 095965182110504
Author(s):  
Hongbo Xin ◽  
Yujie Wang ◽  
Xianzhong Gao ◽  
Qingyang Chen ◽  
Bingjie Zhu ◽  
...  
Keyword(s):  
Control System ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Euler Angle ◽  
Level Flight ◽  
Aerial Vehicles ◽  
Flight Mode ◽  
Aerial Vehicle ◽  
Hover Flight ◽  
And Control

The tail-sitter unmanned aerial vehicles have the advantages of multi-rotors and fixed-wing aircrafts, such as vertical takeoff and landing, long endurance and high-speed cruise. These make the tail-sitter unmanned aerial vehicle capable for special tasks in complex environments. In this article, we present the modeling and the control system design for a quadrotor tail-sitter unmanned aerial vehicle whose main structure consists of a traditional quadrotor with four wings fixed on the four rotor arms. The key point of the control system is the transition process between hover flight mode and level flight mode. However, the normal Euler angle representation cannot tackle both of the hover and level flight modes because of the singularity when pitch angle tends to [Formula: see text]. The dual-Euler method using two Euler-angle representations in two body-fixed coordinate frames is presented to couple with this problem, which gives continuous attitude representation throughout the whole flight envelope. The control system is divided into hover and level controllers to adapt to the two different flight modes. The nonlinear dynamic inverse method is employed to realize fuselage rotation and attitude stabilization. In guidance control, the vector field method is used in level flight guidance logic, and the quadrotor guidance method is used in hover flight mode. The framework of the whole system is established by MATLAB and Simulink, and the effectiveness of the guidance and control algorithms are verified by simulation. Finally, the flight test of the prototype shows the feasibility of the whole system.

scholarly journals Dynamic Stability and Control of a Manipulating Unmanned Aerial Vehicle

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yunping Liu ◽  
Xijie Huang ◽  
Yonghong Zhang ◽  
Yukang Zhou
Keyword(s):  
Stability Analysis ◽  
Lyapunov Exponent ◽  
Dynamic Stability ◽  
Unmanned Aerial Vehicle ◽  
Impedance Control ◽  
System Stability ◽  
Constructive Method ◽  
Aerial Vehicle ◽  
And Control ◽  
The Impact

This paper focuses on the dynamic stability analysis of a manipulator mounted on a quadrotor unmanned aerial vehicle, namely, a manipulating unmanned aerial vehicle (MUAV). Manipulator movements and environments interaction will extremely affect the dynamic stability of the MUAV system. So the dynamic stability analysis of the MUAV system is of paramount importance for safety and satisfactory performance. However, the applications of Lyapunov’s stability theory to the MUAV system have been extremely limited, due to the lack of a constructive method available for deriving a Lyapunov function. Thus, Lyapunov exponent method and impedance control are introduced, and the Lyapunov exponent method can establish the quantitative relationships between the manipulator movements and the dynamics stability, while impedance control can reduce the impact of environmental interaction on system stability. Numerical simulation results have demonstrated the effectiveness of the proposed method.

Development of Autonomous Quad-Tilt-Wing (QTW) Unmanned Aerial Vehicle: Design, Modeling, and Control

2010 ◽  
pp. 77-93 ◽  
Author(s):  
Kenzo Nonami ◽  
Farid Kendoul ◽  
Satoshi Suzuki ◽  
Wei Wang ◽  
Daisuke Nakazawa
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vehicle Design ◽  
Modeling And Control ◽  
Aerial Vehicle ◽  
And Control

Modeling and control of a quadrotor unmanned aerial vehicle using type-2 fuzzy systems

2021 ◽  
pp. 25-46
Author(s):  
Ayad Al-Mahturi ◽  
Fendy Santoso ◽  
Matthew A. Garratt ◽  
Sreenatha G. Anavatti
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Fuzzy Systems ◽  
Modeling And Control ◽  
Aerial Vehicle ◽  
And Control

scholarly journals 3D target localization based on multi–unmanned aerial vehicle cooperation

2020 ◽  
pp. 002029402092226
Author(s):  
Cheng Xu ◽  
Chanjuan Yin ◽  
Daqing Huang ◽  
Wei Han ◽  
Dongzhen Wang
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Target Localization ◽  
Weight Matrix ◽  
Target Point ◽  
Observation Data ◽  
Remote Sensing Images ◽  
Localization Method ◽  
Ground Target ◽  
Aerial Vehicle

Ground target three-dimensional positions measured from optical remote-sensing images taken by an unmanned aerial vehicle play an important role in related military and civil applications. The weakness of this system lies in its localization accuracy being unstable and its efficiency being low when using a single unmanned aerial vehicle. In this paper, a novel multi–unmanned aerial vehicle cooperative target localization measurement method is proposed to overcome these issues. In the target localization measurement stage, three or more unmanned aerial vehicles simultaneously observe the same ground target and acquire multiple remote-sensing images. According to the principle of perspective projection, the target point, its image point, and the camera’s optic center are collinear, and nonlinear observation equations are established. These equations are then converted to linear equations using a Taylor expansion. Robust weighted least-squares estimation is used to solve the equations with the objective function of minimizing the weighted square sum of re-projection errors from target points to multiple pairs of images, which can make the best use of the effective information and avoid interference from the observation data. An automatic calculation strategy using a weight matrix is designed, and the weight matrix and target-position coordinate value are updated in each iteration until the iteration stopping condition is satisfied. Compared with the stereo-image-pair cross-target localization method, the multi–unmanned aerial vehicle cooperative target localization method can use more observation information, which results in higher rendezvous accuracy and improved performance. Finally, the effectiveness and robustness of this method is verified by numerical simulation and flight testing. The results show that the proposed method can effectively improve the precision of the target’s localization and demonstrates great potential for providing more accurate target localization in engineering applications.

Sign in / Sign up

Export Citation Format

Share Document