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3D CAD model for a quadrotor system modeling and control

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, the work focus on the advantages of several engineering software to present a case study for the quadrotor system by a used 3D CAD model. The CAD model initially generated using one of the CAD software and it can be accessed from MATLAB software and converted into a virtual physical model. Quadrotors are unmanned aerial vehicles capable of vertical takeoff, hovering, and landing. The quadrotor is distinguished by its small size, flexibility, and maneuverability. The small sensors and actuators used in these systems are effective enough in comparison with the larger systems. The CAD model for the quadrotor system in this study is used to show the capability of the 3D model implementation in modeling and control. These models echo on the real-time models to improve the behavior in the real world. The actual quadrotor device was converted from the real system to a 3D model. Also, the model is converted to SimMechanics for the sake of simulation. Two different control methods are used in this work to stable the motion of the quadrotor system. First the adaptive sliding mode controller with the adaptive controller. Second the adaptive sliding mode controller with the PID controller. The simulation results show the model works fine with the controllers and it preserves the desired position and attitude along the desired predefined path. The results shown when a comparison was made that the ratio of error for PID controller is better.
Czasopismo
Rocznik
Strony
art. no. 2022203
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
  • Department of Mechanical Engineering, University of Babylon, Iraq
  • Department of Mechanical Engineering, University of Babylon, Iraq
Bibliografia
  • 1. Elruby A, El-khatib M, El-Amary N, Hashad A. Dynamic modeling and control of quadrotor vehicle. Int. Conf. Appl. Mech. Mech. Eng. 2012;15:1–10. https://doi.org/10.21608/amme.2012.37092.
  • 2. Abbasi E, Mahjoob M. Controlling of quadrotor UAV using a fuzzy system for tuning the PID gains in hovering mode. 10th Int. Conf. Adv. Comput. Entertain. Technol. 2013:1-6. http://confscoop.org/ACE-2013/13_Reza2_ACE.pdf.
  • 3. Jatsun S, Lushnikov B, Emelyanova O, Leon ASM. Synthesis of simmechanics model of quadcopter using solidworks cad translator function. Smart Innov. Syst. Technol. 2021;187:125-137. https://doi.org/10.1007/978-981-15-5580-0_10.
  • 4. Cekus D, Posiadała B, Warys P. Integration of modeling in solidworks and matlab/simulink environments. Arch. Mech. Eng. 2014;61(1):57-74. https://doi.org/10.2478/meceng-2014-0003.
  • 5. Gordon RF, Kumar P, Ruff R. Simulating quadrotor dynamics using imported CAD data. AIAA Model. Simul. Technol. Conf. 2013:1-7. https://doi.org/10.2514/6.2013-4735.
  • 6. Grau A, Bolea Y, Sanfeliu A. CAD-based Approach for Identification of UAVs. MATEC Web Conf. 2019;291(9):1004. https://doi.org/10.1051/matecconf/201929101004.
  • 7. Elsamanty M, Khalifa A, Fanni M, Ramadan A, AboIsmail A. Methodology for identifying quadrotor parameters, attitude estimation and control. 2013 IEEE/ASME Int. Conf. Adv. Intell. Mechatronics Mechatronics Hum. Wellbeing. AIM. 2013:1343-1348. https://doi.org/10.1109/AIM.2013.6584281.
  • 8. Makkonen T, Nevala K, Heikkilä R. A 3D model based control of an excavator. Autom. Constr. 2006; 15(5):571-577. https://doi.org/10.1016/j.autcon.2005.07.009.
  • 9. Velasco O, Valente J, Alhama Blanco PJ, Abderrahim M. An open simulation strategy for rapid control design in aerial and maritime drone teams: A comprehensive tutorial. Drones. 2020;4(3):1-20. https://doi.org/10.3390/drones4030037.
  • 10. Shaqura M, Shamma JS. An automated quadcopter cad based design and modeling platform using solidworks API and smart dynamic assembly. ICINCO 2017 - Proc. 14th Int. Conf. Informatics Control. Autom. Robot. 2017;2:122-131. https://doi.org/10.5220/0006438601220131.
  • 11. Simscape Multibody - MATLAB & Simulink. https://www.mathworks.com/products/simscapemultibody.html.
  • 12. Simulink Documentation. https://www.mathworks.com/help/simulink.
  • 13. Gouasmi M, Ouali M, Fernini B, Meghatria M. Kinematic modelling and simulation of a 2-R robot using solidworks and verification by matlab/Simulink. Int. J. Adv. Robot. Syst. 2012;9:1-13. https://doi.org/10.5772/50203.
  • 14. Zátopek J, Urednícek Z, Machado J, Sousa J. Dynamic simulation of the CAD model in SimMechanics with multiple uses. Turkish J. Electr. Eng. Comput. Sci. 2018;26(3):1278–1290. https://doi.org/10.3906/elk-1712-217.
  • 15. De Simone MC, Russo S, Rivera ZB, Guida D. Multibody model of a UAV in Presence of wind fields. Proc. - 2017 Int. Conf. Control. Artif. Intell. Robot. Optim. ICCAIRO 2017. 2017:83–88. https://doi.org/10.1109/ICCAIRO.2017.26.
  • 16. Srinivasan V, Mandal E, Akleman E. Solidifying wireframes creating the solid wireframe. Renaissance Banff: Mathematics, Music, Art, Culture. 2005: 203-210.
  • 17. Mariappan SM, Veerabathiran A., Modelling and simulation of multi spindle drilling redundant SCARA robot using SolidWorks and MATLAB/SimMechanics. Rev. Fac. Ing. 2016;81:63-72. https://doi.org/10.17533/udea.redin.n81a06.
  • 18. Yun C, Li XM. Aerodynamic model analysis and flight simulation research of UAV based on Simulink. J. Softw. Eng. Appl. 2013;6(2):43-47. https://doi.org/10.4236/jsea.2013.62007.
  • 19. Schlotter M. Multibody system simulation with SimMechanics. Analysis. 2003:1-23.
  • 20. Emran BJ. Nonlinear Adaptive control of a Quadcopter. 2914:106.
  • 21. Benic Z, Piljek P, Kotarski D. Mathematical modelling of unmanned aerial vehicles with four rotors. Interdiscip. Descr. Complex Syst. 2016;14(1):88-100. https://doi.org/10.7906/indecs.14.1.9.
  • 22. Islam M, Okasha M, Idres MM. Dynamics and control of quadcopter using linear model predictive control approach. IOP Conf. Ser. Mater. Sci. Eng.. 2017;270(1):0-9. https://doi.org/10.1088/1757-899X/270/1/012007.
  • 23. Moshayedi AJ, Gheibollahi M, Liefa L. The quadrotor dynamic modeling and study of meta-heuristic algorithms performance on optimization of PID controller index to control angles and tracking the route. IAES International Journal of Robotics and Automation 9.4. 2020:256.
  • 24. Lee S, Ha C, Kim BS. Adaptive nonlinear control system design for helicopter robust command augmentation. Aerosp. Sci. Technol. 2005;9(3):241-251. https://doi.org/10.1016/j.ast.2004.12.007.
  • 25. Wang L, He C, Zhu P. Adaptive sliding mode control for quadrotor aerial robot with Ι type configuration. Int. J. Autom. Control Eng. 2014;3(1):20. https://doi.org/10.14355/ijace.2014.0301.03.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d82344d3-c303-43b2-8eab-5202005d00ac
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