PID Based Quadrotor Altitude Retaining Model with Backward Propagation Artificial Neural Network
Abstract
A quadrotor is a type of Unmanned Aerial Vehicle (UAV) or an unmanned flying vehicle flying remotely or using automatic control. In carrying out its mission, a quadrotor requires a good control system. One of the control systems in the quadrotor system is the altitude control system. Altitude control will control the quadrotor according to the desired altitude, whether there are interference and the quadrotor load. The widely used control method is the PID control. Unfortunately, the PID control produces a poor response because the PID constant is fixed, whereas the interference when the quadrotor flies will fluctuate. Therefore, this study offers control that can make a self-adjustment when exposed to specific interference. The method offered in this study is a PID control with Artificial Neural Networks (ANN). The ANN system will tune the PID components in real-time according to the occurring interference. The use of the PID with ANN results in a faster rise time response of 0.0594 seconds, a decrease in overshoot of 7.58%, a decrease in the steady-state error of ± 0.0672, and a decrease in settling time of 1.031 seconds compared to conventional PID. It shows that the PID with ANN results in better control than the PID alone.
References
J. Gómez-Avila, C. López-Franco, A.Y. Alanis, dan N. Arana-Daniel, “Control of Quadrotor using a Neural Network based PID,” 2018 IEEE Lat. Am. Conf. on Comput. Intel. (LA-CCI), Nov. 2018, hal. 1–6.
H. Mansoor, I.U.H. Shaikh, dan S. Habib, “Genetic Algorithm Based LQR Control of Hovercraft,” 2016 Int. Conf. on Intel. Sys. Eng. (ICISE), 2016, hal. 335–339.
X. Wang, C. Sun, X. Lin, dan Y. Yu, “Adaptive Neural Network Control of a Quadrotor with Input Delay,” Proc. 2018 Chinese Autom. Congr. (CAC 2018), 2018, hal. 4095–4100.
Z. Qing, M. Zhu, dan Z. Wu, “Adaptive Neural Network Control for a Quadrotor Landing on a Moving Vehicle,” Proc. 30th Chinese Control Decis. Conf. CCDC 2018, 2018, hal. 28–33.
Y.F. Teng, B. Hu, Z.W. Liu, J. Huang, dan Z.H. Guan, “Adaptive Neural Network Control for Quadrotor Unmanned Aerial Vehicles,” 2017 Asian Control Conf. ASCC 2017, 2018, hal. 988–992.
T. Bodrumlu, A. Bek, M.A. Akbulut, dan F. Caliskan, “Design and Control of Artificial Neural Network Based Low-Cost Autonomous Quadrotor System,” 2018 6th Int. Conf. Control Eng. Inf. Technol. CEIT 2018, 2018, hal. 25–27.
S. Liao, X. Lei, dan Y. Xiao, “The Compound Control Method for Pesticide Spraying Quadrotor UAVs,” Proc. 2019 IEEE 3rd Inf. Technol. Networking, Electron. Autom. Control Conf. (ITNEC 2019), 2019, hal. 1022–1027.
Y.M. Chen, Y.L. He, dan M.F. Zhou, “Decentralized PID Neural Network Control for a Quadrotor Helicopter Subjected to Wind Disturbance,” J. Cent. South Univ., Vol. 22, No. 1, hal. 168–179, 2015.
F.F. Rahani dan T.K. Priyambodo, “Implementasi Full State Feedback LQR dengan JST pada Kendali Ketinggian Quadrotor,” J. Nas. Tek. Elektro dan Teknol. Inf., Vol. 8, No. 4, hal. 357-363, Nov. 2019.
T.K. Priyambodo, A.E. Putra, dan A. Dharmawan, “Optimizing Control Based on Ant Colony Logic for Quadrotor Stabilization,” 2015 IEEE Int. Conf. on Aero. Electron. and Remote Sens. Technol. (ICARES), 2015, hal. 1–4.
T.K. Priyambodo, A. Dharmawan, dan A.E. Putra, “PID Self Tuning Control Based on Mamdani Fuzzy Logic Control for Quadrotor Stabilization,” AIP Conf. Proc., 2016, Vol. 1705, No. 1, hal. 020013.
F.F. Rahani dan T.K. Priyambodo, “Penalaan Mandiri Full State Feedback dengan LQR dan JST pada Kendali Quadrotor,” Indonesian J. Electron. Instrum. Syst. (IJEIS), Vol. 9, No. 1, hal. 21-32, Apr. 2019.
C. Sun, T. Lu, dan K. Yuan, “Balance Control of Two-Wheeled Self-Balancing Robot Based on Linear Quadratic Regulator and Neural Network,” 2013 Fourth Int. Conf. Intell. Control Inf. Process.(ICICIP), 2013, hal. 862–867.
P. Gautam, “Optimal Control of Inverted Pendulum System Using ADALINE Artificial Neural Network with LQR,” 2016 Int. Conf. Recent Adv. Innov. Eng., 2016, hal. 1–6.
A.Z. Azfar dan D. Hazry, “A Simple Approach on Implementing IMU Sensor Fusion in PID Controller for Stabilizing Quadrotor Flight Control,” Proc. 2011 IEEE 7th Int. Colloq. on Signal Proces. and Its Appl, (CSPA 2011), 2011, hal. 28–32.
S. Gupte dan J.M. Conrad, “A Survey of Quadrotor Unmanned Aerial Vehicles,” 2012 Proc. IEEE Southeastcon, 2012, hal. 1–6.
L.R. García Carrillo, A.E. Dzul López, R. Lozano, dan C. Pégard, Quad Rotorcraft Control, Vol. 1, London, UK: Springer London, 2013.
P. Pounds, R. Mahony, dan P. Corke, “Modelling and Control of a Quad-Rotor Robot,” Proc. of the 2006 Australas. Conf. on Robot. and Automat., 2006, pp. 1-10.
A. Ollero, “Aerial Robotic Manipulators,” dalam Encyclopedia of Robotics, M.H. Ang, O. Khatib, dan B. Siciliano, Eds. Heidelberg, Germany: Springer Berlin Heidelberg, 2019, hal. 1–8.
Z. Tahir, “State Space System Modeling of a Quad Copter UAV,” Indian J. Sci. Technol., Vol. 8, No. 1, hal. 1–5, Jan. 2015.
K. Ogata, Modern Control Engineering, New York, USA: Prentice Hall, 2010.
A.B. Zakaria dan A. Dharmawan, “Sistem Kendali Penghindar Rintangan pada Quadrotor Menggunakan Konsep Linear Quadratic,” Indones. J. Electron. Instrum. Syst., Vol. 7, No. 2, hal. 219–230, 2017.
A. O’Dwyer, “PI and PID Controller Tuning Rules: An Overview and Personal Perspective,” Proc. IET Irish Signals Syst. Conf., 2006, hal. 161–166.
T.K. Priyambodo, A. Dharmawan, O.A. Dhewa, dan N.A.S. Putro, “Optimizing Control Based on Fine Tune PID Using Ant Colony Logic for Vertical Moving Control of UAV System,” AIP Conf. Proc., Vol. 1755, No. 1, hal. 170011.1-7, 2016.
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