DOI: https://doi.org/10.14710/jtsiskom.8.2.2020.164-170

Sistem kendali kestabilan attitude quadrotor dengan metode self-tuning Fuzzy-PD

Attitude stabilization control for quadrotor using self-tuning fuzzy-PD

Department of Electrical Engineering, Universitas Diponegoro, Indonesia

Received: 11 Dec 2019; Revised: 11 Mar 2020; Accepted: 2 Apr 2020; Available online: 6 Apr 2020; Published: 30 Apr 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer under http://creativecommons.org/licenses/by-sa/4.0.

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Abstract
This research aims to develop a quadrotor control system for maintaining its position and balance from disturbance while hovering. A fast and reliable control technique is required to respond to high maneuverability and high non-linearity of six degrees of freedom system. Hence, this research focuses on designing a Self-Tuning Fuzzy-PD control system for quadrotor’s attitude. The designed control system utilizes input data from the Inertial Navigation System (INS). Then the quadrotor’s attitude is controlled by passing the PWM signal to the flight controller APM 2.6. The result shows that the average absolute error for the roll, pitch, and yaw angles are relatively small, as mentioned consecutively 2.079o, 2.266o, and 1.528o, while the maximum absolute errors are 6.314o, 6.722o, and 3.82o.
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Keywords: quadrotor; attitude control; self-tuning fuzzy-PD
Funding: Universitas Diponegoro, Indonesia

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Section: Original Research Articles
Language : ID
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  1. J. M. B. Domingues, "Quadrotor prototype," MS Thesis, University of Lisbon, Lisbon, Portugal, 2009
  2. M. Prabha, R. Thottungal, and S. Kaliappan, "Modeling and simulation of x-quadcopter control," International Journal for Research in Applied Science & Engineering Technology, vol. 4, no. 4, pp. 282-287, 2016
  3. P. Wang, Z. Man, Z. Cao, J. Zheng, and Y. Zhao, "Dynamics modelling and linear control of quadcopter," in 2016 International Conference on Advanced Mechatronic Systems, Melbourne, Australia, Dec. 2016, pp. 498-503,. doi: 10.1109/ICAMechS.2016.7813499
  4. A. Dharmawan and I. F. Arismawan, "Sistem kendali penerbangan quadrotor pada keadaan melayang dengan metode LQR dan Kalman filter," IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), vol. 7, no. 1, pp. 49-60, 2017. doi: 10.22146/ijeis.15262
  5. H. Arrosida, "Perancangan metode kontrol LQR (linear quadratic regulator) sebagai solusi optimal pengendalian gerak quadrotor," in Seminar Nasional MASTER: Maritim, Sains, dan Teknologi Terapan, Surabaya, Indonesia, Dec. 2016, pp. 109-122
  6. I. Sa and P. Corke, "System identification, estimation and control for a cost effective open-source quadcopter," in IEEE International Conference on Robotics and Automation, Saint Paul, USA, May. 2012, pp. 2202-2209. doi: 10.1109/ICRA.2012.6224896
  7. B. Erginer and E. Altuğ, "Design and implementation of a hybrid fuzzy logic controller for a quadrotor VTOL vehicle," International Journal of Control, Automation and Systems, vol. 10, pp. 61-70, 2012. doi: 10.1007/s12555-012-0107-0
  8. A. Noordin, M. A. M. Basri, Z. Mohamed, and A. F. Z. Abidin, "Modelling and PSO fine-tuned PID control of quadrotor UAV," International Journal on Advanced Science Engineering Information Technology, vol. 7, no. 4, pp. 1367-1373, 2017. doi: 10.18517/ijaseit.7.4.3141
  9. V. Mohammadi, S. Ghaemi, and H. Kharrati, "PSO tuned FLC for full autopilot control of quadrotor to tackle wind disturbance using bond graph approach," Applied Soft Computing, vol. 65, pp. 184-195, 2018. doi: 10.1016/j.asoc.2018.01.015
  10. T. K. Priyambodo, A. Dharmawan, O. A. Dhewa, and N. A. S. Putro, "Optimizing control based on fine tune PID using ant colony logic for vertical moving control of UAV system," AIP Conference Proceedings, vol. 1755, no. 1, 170011, pp. 1-6, 2016. doi: 10.1063/1.4958613
  11. H. Noshahri and H. Kharrati, "PID controller design for unmanned aerial vehicle using genetic algorithm," in 23rd International Symposium on Industrial Electronics, Istanbul, Turkey, Jun. 2014. doi: 10.1109/ISIE.2014.6864613
  12. M. S. Sulila, M. A. Riyadi and others, "Particle swarm optimization (PSO)-based self tuning proportional, integral, derivative (PID) for bearing navigation control system on quadcopter," in 4th International Conference on Information Technology, Computer, and Electrical Engineering, Semarang, Indonesia, Oct. 2017, pp. 181-186. doi: 10.1109/ICITACEE.2017.8257699
  13. P. A. Kusuma and A. Dharmawan, "Pengendalian kestabilan ketinggian pada penerbangan quadrotor dengan metode PID fuzzy," IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), vol. 7, no. 1, pp. 61-70, 2017. doi: 10.22146/ijeis.15456
  14. A. Faizal, E. Ismaredah and I. F. Ridho, "Perancangan pengendalian hover quadcopter menggunakan pengendali hybrid fuzzy dan proportional integral derivative (PID)," Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering), vol. 5, pp. 19-28, 2018. doi: 10.33019/ecotipe.v5i2.647
  15. K. Ogata and Y. Yang, Modern control engineering, vol. 5. Upper Saddle River, NJ: Prentice Hall, 2010

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