Automatic landing drone using the anticipatory controller method

Authors

1 MSc, Faculty of Aerospace Engineering, Malek Ashtar University of Technology, Iran

2 Assistant Professor, Faculty of Aerospace Engineering, Malik Ashtar University of Technology

3 Assist. Prof., Faculty of Aerospace Engineering, Malek Ashtar University of Technology, Iran

Abstract

Due to the drastic change in aerodynamic parameters, the problem of controlling the landing mode will be much more complicated than the high altitude flight. On the other hand, the presence of obstacles in the way of the bird's landing is another condition that should be considered in the design of the controller. Thus, according to the above, it is necessary to use the predictive controller to solve the problem. This controller inherently has a high resistance to model change. Also, if this controller is used in a restricted manner, it can be used to bypass obstacles in the path of movement during landing. The aim of this paper is to control the system for automatic landing by means of model-based pre-interlinear control. The reason for using the pre-interlinear controller is the presence of obstacles in the path of movement and the fulfillment of the constraints in the environment to remove the obstacles. As a final result, this controller is designed in such a way that the effect of external disturbances on the bird is minimized and the stability of the system is not jeopardized by the emergence of model uncertainties. Also, in this method, the effect caused by the delay of the external navigation system is taken into account in the closed loop system and the stability of the system is guaranteed. Finally, the proposed controller design is calculated for a real bird model and its performance simulation in the presence of obstacles, lateral and longitudinal wind is presented.

Keywords

Main Subjects

References

[1]. Michel, N., et al., (2019) Design and flight experiments of a Tube-Based Model Predictive Controller for the AR. Drone 2.0 quadrotor. IFAC-PapersOnLine, 52: p. 112-117.
[2].Greatwood, C. and A.G. Richards, (2019) Reinforcement learning and model predictive control for robust embedded quadrotor guidance and control. Auton. Robots, 2019. 43: p. 1681-1693.
[3].Koo, S., S. Kim, and J. Suk, (2015) Model predictive control for UAV automatic landing on moving carrier deck with heave motion. IFAC-PapersOnLine, 48: p. 59-64.
[4].Huh, S. and D.H. Shim, (2010) A vision-based automatic landing method for fixed-wing UAVs. J. Intel.Robot. Sys.,,  (1) 57, p.217-231.
[5].  Kim, D., et al., A glidepath tracking algorithm for autolanding of a UAV, in Infotech@ Aerospace. 2005. p. 6979.
[6].  Blenkhorn, K. and S. O'Hara. (2007) Towards an inexpensive computer-vision based automated landing system for small unmanned aerial vehicles. in Enhanced and Synthetic Vision 2007. SPIE.
[7].  Najafi, M., S. Rahmanian, and B. (2018) Shirani, Design of an H∞-PID Controller for an UAV Auto-Landing System Based on External Navigation. Modares Mechanical Engineering,. 17:(11) p. 89-96.
[8].  Cho, A., et al., (2008) Fully automatic taxiing, takeoff and landing of a UAV based on a single-antenna GNSS receiver. IFAC Proceedings Volumes. 41(2): p. 4719-4724.
[9].  Lange, S., N. Sunderhauf, and P. Protzel. (2009) A vision based onboard approach for landing and position control of an autonomous multirotor UAV in GPS-denied environments. in 2009 International Conference on Advanced Robotics. IEEE.
[10]. Kim, H.J., et al., (2013) Fully autonomous vision-based net-recovery landing system for a fixed-wing UAV. IEEE/ASME Transactions On Mechatronics, 18(4): p. 1320-1333.
[11]. Joo, S., et al. (2008) Vision aided inertial navigation with measurement delay for fixed-wing unmanned aerial vehicle landing. in 2008 IEEE Aerospace Conference. IEEE.
[12]. Kong, W., et al. (2013) Autonomous landing of an UAV with a ground-based actuated infrared stereo vision system. in 2013 IEEE/RSJ international conference on intelligent robots and systems. IEEE.
[13]. Masri, M.A., S. Dbeis, and M. Al Saba, (2017) Autolanding a power-off uav using on-line optimization and slip maneuvers. J. Intel. Robotic Sys., 86(2): p. 255-276.
[14]. Lee, C.-L. and J.-G. Juang. (2011) Aircraft landing control in wind shear condition. in 2011 International Conference on Machine Learning and Cybernetics. IEEE.
[15]. Etkin, B., (2012) Dynamics of atmospheric flight.: Courier Corporation.
[16]. Kabiri, M., M.B. Menhaj, and H. (2017) Atrainfar, Trajectory tracking of a VTOL aircraft with uncertainty and disturbances. Modares Mechanical Engineering, 17(8): p. 68-74.
[17]. Malekzadeh, M., B. Shahbazi, and H.R. Koofigar, (2015) Robust Control of spacecraft formation flying via virtual structure. Modares Mechanical Engineering, 15(8): p. 379-385.
[18]. Wagner, T. and J. Valasek. (2006) Digital autoland control laws using direct digital design and quantitative feedback theory. in AIAA Guidance, Navigation, and Control Conference and Exhibit.
[19]. Gui, Y., et al., (2013) Airborne vision-based navigation method for UAV accuracy landing using infrared lamps. J. Intel. Rob. Sys., 72(2): p. 197-218.
[20]. Yakimenko, O.A., et al., (2002) Unmanned aircraft navigation for shipboard landing using infrared vision. IEEE Transactions on Aerospace and Electronic Systems, 38(4): p. 1181-1200.
[21]. Wang, J., et al., (2008) Integration of GPS/INS/vision sensors to navigate unmanned aerial vehicles. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(part B1): p. 963-969.
[22]. Shakernia, O., et al (1999)., Landing an unmanned air vehicle: Vision based motion estimation and nonlinear control. Asian J. cont., 1(3): p. 128-145.
[23]. Sharp, C.S., O. Shakernia, and S.S. Sastry. (2001) A vision system for landing an unmanned aerial vehicle. in Proceedings ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164). Ieee.
[24]. Weiss, S., D. Scaramuzza, and R. Siegwart, (2011) Monocular‐SLAM–based navigation for autonomous micro helicopters in GPS‐denied environments. J. Field Robot., 28(6): p. 854-874.
[25]. Shue, S.-P. and R.K. Agarwal, (1999) Design of automatic landing systems using mixed h/h control. J. Guidance, Control, and Dynamics, 22(1): p. 103-114.
[26]. Allgöwer, F. and A. Zheng, (2012) Nonlinear model predictive control. Vol. 26. Birkhäuser.
[27]. Camacho, E.F. and C.B. Alba, (2013) Model predictive control. Springer science & business media.
[28]. Feng, Y., et al., (2018) Autonomous landing of a UAV on a moving platform using model predictive control. Drones, 2(4): p. 34.
[29]. Macés-Hernández, J.A., F. Defaÿ, and C. Chauffaut. (2017) Autonomous landing of an UAV on a moving platform using Model Predictive Control. in 2017 11th Asian Control Conference (ASCC).. IEEE.
[30]. امینی، سمانه و اکبری، علی اکبر،1394،مدل سازی دینامیکی و کنترل غیرخطی دینامیک طولی پهپاد بال ثابت توسط کنترلگر PID با استفاده از الگوریتم ژنتیک،همایش یافته های نوین در هوافضا ،تهران
Journal of Solid and Fluid Mechanics
Volume 14, Issue 5
November and December 2024
Pages 137-153

Files

Share

How to cite

Statistics