Mini Drone Linear and Nonlinear Controller System Design and Analyzing

Esraa Hadi Kadhim; Ahmad T. Abdulsadda

doi:10.18196/jrc.v3i2.14180

Authors

Esraa Hadi Kadhim Department of Communication Engineering, Engineering Technical Collage / Al-Najaf, Al-Furat Al-Awsat Technical University (ATU), Najaf, Iraq https://orcid.org/0000-0001-6589-2562
Ahmad T. Abdulsadda

DOI:

https://doi.org/10.18196/jrc.v3i2.14180

Keywords:

PD controller, quadcopter, Matlab-Simulink, Altitude controller.

Abstract

Choosing the mini-drone for a specific payload for designing purposes is one of the most challenging for both cost and design purposes. It is important to develop and analyze the flight control systems of the quadcopter-type Parrot mini drone and how to make the drones more tolerant of adverse weather conditions. The main problem with any quadcopter is that it loses its balance when exposed to any external influence, even if that influence is weak. Where the controller is the most important part of the drone, six plane controllers cover the six degrees of freedom (6dof) in the movement of the drone. In our research, we have improved the height controller in the drone, thus improving the altitude controller by using (PD) and increasing the values of (Kp and Kd) in the altitude controller of the Parrot Mini Drone Mambo to make it more bearable to external influence and to maintain its altitude. We assumed that the aircraft was exposed to bad weather conditions, such as snowfall and dust, which led to an increase in the speed at which the drone fell. We also increased the free fall constant of the object in the simulation design of the drone from (-9.81 m/s2 to -12.81 m/s2) and used Matlab R2021a Simulink to undertake the tuning of the (Kp and Kd) values. This study yielded good results, as illustrated in the results section. Therefore, this research paper suggests adopting the PD controller in the altitude controller and the new values of Kp and Kd to make the drone more tolerant of weather conditions. We tested these results in practice and got good results.

Author Biography

Esraa Hadi Kadhim, Department of Communication Engineering, Engineering Technical Collage / Al-Najaf, Al-Furat Al-Awsat Technical University (ATU), Najaf, Iraq

Engineering of technical communication

References

B. P. Amiruddin, E. Iskandar, A. Fatoni, and A. Santoso, “Deep Learning based System Identification of Quadcopter Unmanned Aerial Vehicle,” 2020 3rd International Conference on Information and Communications Technology (ICOIACT), 2021, pp. 165-169.

P. Ceppi, “Model-based Design of a Line-tracking Algorithm for a Low-cost Mini Drone through Vision-based Control,” Diss. University of Illinois at Chicago, 2020.

A. Kumar, K. Sharma, H. Singh, and S. Gupta, “A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic,” Futur. Gener. Comput. Syst., vol. 115, pp. 1–19, 2021, doi: 10.1016/j.future.2020.08.046.

S. DeBock “Guidance, Navigation, And Control of a Quadrotor Drone with PID Controls,” Diss. Monterey, CA; Naval Postgraduate School, 2020.

S. Waitman, H. Alwi, and C. Edwards, “Flight evaluation of simultaneous actuator/sensor fault reconstruction on a quadrotor minidrone,” IET Control Theory Appl., vol. 15, no. 16, pp. 2095–2110, 2021, doi: 10.1049/cth2.12180.

J. Chaoraingern, V. Tipsuwanporn, and A. Numsomran, “Mini-drone quadrotor altitude control using characteristic ratio assignment PD tuning approach,” Lect. Notes Eng. Comput. Sci., vol. 2019, pp. 337–341, 2019.

C. C. Veedhi, “Estimation of altitude using ultrasonic and pressure sensors,” Thesis of Blekinge Institute of Technology, 2020.

A. Talaeizadeh, D. Antunes, H. N. Pishkenari, and A. Alasty, “Optimal-time quadcopter descent trajectories avoiding the vortex ring and autorotation states,” Mechatronics, vol. 68, p. 102362, 2020, doi: 10.1016/j.mechatronics.2020.102362.

P. J. Bristeau, F. Callou, D. Vissière, and N. Petit, “The Navigation and Control technology inside the AR.Drone micro UAV,” IFAC Proc. Vol., vol. 44, no. 1, pp. 1477–1484, 2011, doi: 10.3182/20110828-6-IT-1002.02327.

A. Shamshirgaran, H. Javidi and D. Simon, "Evolutionary Algorithms for Multi-Objective optimization of Drone Controller Parameters," 2021 IEEE Conference on Control Technology and Applications (CCTA), 2021, pp. 1049-1055, doi: 10.1109/CCTA48906.2021.9658828.

W. M. Thet, M. Myint, and E. E. Khin, “Modelling and Control of Quadrotor Control System using MATLAB/Simulink,” Int. J. Sci. Eng. Appl., vol. 7, no. 7, pp. 125–129, 2018, doi: 10.7753/ijsea0707.1002.

B. Alkhlidi, A. T. Abdulsadda, and A. Al Bakri, “Optimal robotic path planning using intelligent search algorithms,” J. Robot. Control, vol. 2, no. 6, pp. 519–526, 2021, doi: 10.18196/jrc.26132.

C. Ben Jabeur and H. Seddik, “Optimized Neural Networks-PID Controller with Wind Rejection Strategy for a Quad-Rotor,” J. Robot. Control, vol. 3, no. 1, pp. 62–72, 2022, doi: 10.18196/jrc.v3i1.11660.

A. E. M. Redha, R. B. Abduljabbar, and M. S. Naghmash, “Drone Altitude Control Using Proportional Integral Derivative Technique and Recycled Carbon Fiber Structure,” Lecture Notes in Networks and Systems, pp. 55–67, Aug. 2021.

H. Lim, J. Park, D. Lee, and H. J. Kim, “Build Your Own Quadrotor: Open-Source Projects on Unmanned Aerial Vehicles,” IEEE Robot. Autom. Mag., vol. 19, no. 3, pp. 33–45, 2012, doi: 10.1109/MRA.2012.2205629.

D. Lee and D. H. Shim, “Design and Validation of Low-cost Flight Control Computer for Multi-rotor UAVs,” J. Korean Soc. Aeronaut. Sp. Sci., vol. 45, no. 5, pp. 401–408, 2017, doi: 10.5139/jksas.2017.45.5.401.

D. Lee, H. Lee, J. Lee, and D. H. Shim, “Design, implementation, and flight tests of a feedback linearization controller for multirotor UAVs,” Int. J. Aeronaut. Sp. Sci., vol. 18, no. 4, pp. 740–756, 2017, doi: 10.5139/IJASS.2017.18.4.740.

L. Meier, P. Tanskanen, F. Fraundorfer, and M. Pollefeys, “PIXHAWK: A system for autonomous flight using onboard computer vision,” Proc. - IEEE Int. Conf. Robot. Autom., pp. 2992–2997, 2011, doi: 10.1109/ICRA.2011.5980229.

O. A. Ibrahim, S. Sciancalepore, and R. Di Pietro, “Noise2Weight: On detecting payload weight from drones acoustic emissions,” Future Generation Computer Systems, Apr. 2022.

Z. Uddin, M. Altaf, M. Bilal, L. Nkenyereye, and A. K. Bashir, “Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference,” Comput. Commun., vol. 154, pp. 236–245, 2020, doi: 10.1016/j.comcom.2020.02.065.

S. Sciancalepore, O. A. Ibrahim, G. Oligeri, and R. Di Pietro, “Detecting drones status via encrypted traffic analysis,” Proceedings of the ACM Workshop on Wireless Security and Machine Learning - WiseML 2019, pp. 67–72, 2019, doi: 10.1145/3324921.3328791.

U. Seidaliyeva, M. Alduraibi, L. Ilipbayeva, and A. Almagambetov, “Detection of loaded and unloaded UAV using deep neural network,” Proc. - 4th IEEE Int. Conf. Robot. Comput. IRC 2020, pp. 490–494, 2020, doi: 10.1109/IRC.2020.00093.

I. Djurek, A. Petosic, S. Grubesa, and M. Suhanek, “Analysis of a Quadcopter’s Acoustic Signature in Different Flight Regimes,” IEEE Access, vol. 8, pp. 10662–10670, 2020, doi: 10.1109/ACCESS.2020.2965177.

S. Madruga, A. Tavares, A. Brito and T. Nascimento, "A Project of an Embedded Control System for Autonomous Quadrotor UAVs," 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE), 2018, pp. 483-489, doi: 10.1109/LARS/SBR/WRE.2018.00091.

S. Bouabdallah, A. Noth and R. Siegwart, "PID vs LQ control techniques applied to an indoor micro quadrotor," 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 2004, pp. 2451-2456, vol. 3, doi: 10.1109/IROS.2004.1389776.

A. Benini, A. Mancini, and S. Longhi, “An IMU/UWB/vision-based extended kalman filter for mini-UAV localization in indoor environment using 802.15.4a wireless sensor network,” J. Intell. Robot. Syst. Theory Appl., vol. 70, no. 1–4, pp. 461–476, 2013, doi: 10.1007/s10846-012-9742-1.

“Simulink Support Package for Parrot Minidrones - File Exchange - MATLAB Central.” https://ch.mathworks.com/matlabcentral/fileexchange/63318-simulink-support-package-for-parrot-minidrones (accessed Feb. 19, 2022).

J. J. E. Slotine and W. Li, Applied Nonlinear Control, Englewood Cliffs, NJ: Prentice-Hall, 1991.

D. Xue, Modeling and Simulation with Simulink®: For Engineering and Information Systems, Walter de Gruyter GmbH & Co KG, 2022.

C. S. Veerappan, P. K. K. Loh, and R. J. Chennattu, “Smart Drone Controller Framework—Toward an Internet of Drones,” Studies in Computational Intelligence, pp. 1–14, 2022.