Abstract
A decentralized PID neural network (PIDNN) control scheme was proposed to a quadrotor helicopter subjected to wind disturbance. First, the dynamic model that considered the effect of wind disturbance was established via Newton-Euler formalism. For quadrotor helicopter flying at low altitude in actual situation, it was more susceptible to be influenced by the turbulent wind field. Therefore, the turbulent wind field was generated according to Dryden model and taken into consideration as the disturbance source of quadrotor helicopter. Then, a nested loop control approach was proposed for the stabilization and navigation problems of the quadrotor subjected to wind disturbance. A decentralized PIDNN controller was designed for the inner loop to stabilize the attitude angle. A conventional PID controller was used for the outer loop in order to generate the reference path to inner loop. Moreover, the connective weights of the PIDNN were trained on-line by error back-propagation method. Furthermore, the initial connective weights were identified according to the principle of PID control theory and the appropriate learning rate was selected by discrete Lyapunov theory in order to ensure the stability. Finally, the simulation results demonstrate that the controller can effectively resist external wind disturbances, and presents good stability, maneuverability and robustness.
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ABHIJIT D, FRANK L, KAMESH S. Backstepping approach for controlling a quadrotor using Lagrange form dynamics [J]. Journal of Intelligent & Robotic Systems, 2009, 56(1/2): 127–151.
MOREL Y, LEONESSA A. Direct adaptive tracking control of quadrotor aerial vehicles [C]// ASME International Mechanical Engineering Congress and Exposition. Chicago, 2006: 1–6.
RASHID M I, AKHTAR S. Adaptive control of a quadrotor with unknown model parameters [C]// Proceedings of 9th International Bhurban Conference on Applied Sciences and Technology. Islamabad, 2012: 8–14.
MOHAMMADI M, SHAHRI A M. Adaptive nonlinear stabilization control for a quadrotor UAV: Theory, simulation and experimentation [J]. Journal of Intelligent and Robotic Systems, 2013, 71(1): 1–18.
ASHFAQ A M, WANG Dao-bo. Modeling and backstepping-based nonlinear control strategy for a 6 DOF quadrotor helicopter [J]. Chinese Journal of Aeronautics, 2008, 21(3): 261–268.
RAFFO G V, ORTEGA M G, RUBIO F R. Backstepping & nonlinear ∞ control for path tracking of a quadrotor unmanned aerial vehicle [C]// American Control Conference. Seattle, 2008: 3356–3361.
BOUCHOUCHA M, SEGHOUR S, H. OSMANI M B. Integral backstepping for attitude tracking of a quadrotor system [J]. Electronics and Electrical Engineering, 2011, 116(10): 75–80.
RAFFO G, ORTEGA M, RUBIO F. An integral predictive nonlinear H(infinity) control structure for a quadrotor helicopter [J]. Automatic, 2010, 46(1): 29–39.
NICOL C, MACNAB C J B, RAMIREZ-SERRANO A. Robust adaptive control of a quadrotor helicopter [J]. Mechatronics, 2011, 21(5): 927–938.
DAEWON L, JIN K H, SHANKAR S. Feedback linearization vs. adaptive sliding mode control for a quadrotor helicopter [J]. International Journal of Control Automation And Systems, 2009, 7(3): 419–428.
PATEL A R, PATEL M A, VYAS D R. Modeling and analysis of quadrotor using sliding mode control [C]// Proceedings of the Annual Southeastern Symposium on System Theory. FL, 2012: 111–114.
BESNARDA L, SHTESSELB Y B, LANDRUM B. Quadrotor vehicle control via sliding mode controller driven by sliding mode disturbance observer [J]. Journal of the Franklin Institute, 2012, 349(2): 658–684.
DERAFA L, BENALLEGUE A, FRIDMAN L. Super twisting control algorithm for the attitude tracking of a four rotors UAV [J]. Journal of the Franklin Institute, 2012, 349(2): 685–699.
YANG Biao, PENG Jin-hui, GUO Sheng-hui, ZHANG Shi-min, LI Wei, HE Tao. Acid-pickling plates and strips speed control system by microwave heating based on self-adaptive fuzzy PID algorithm [J]. Journal of Central South University, 2012, 19(8): 2179–2186.
BOUABDALLAH S, NOTH A, SIEGWART R. PID vs LQ control techniques applied to an indoor micro quadrotor [C]//Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Sendai, 2004: 2451–2456.
SU Jing-ya, FAN Peng-hui, CAI Kai-yuan. Attitude control of quadrotor aircraft via nonlinear PID [J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(9): 1054–1058. (in Chinese)
DIERKS T, JAGANNATHAN S. Neural network output feedback control of a quadrotor UAV [C]// 47th IEEE Conference on Decision and Control. Cancun, 2008: 3633–3639.
NICOL C, MACNAB C J B, RAMIREZ-SERRANO A. Robust neural network control of a quadrotor helicopter [C]// IEEE Canadian Conference on Electrical and Computer Engineering. Canada, 2008: 1233–1237.
CHEN J, HUANG T. Applying neural networks to on-line updated PID controllers for nonlinear process control [J]. Journal of Process Control, 2004, 14(2): 211–230.
THANH T D C, AH K K. Nonlinear PID control to improve the control performance of 2 axes pneumatic artificial muscle manipulator using neural network [J]. Mechatronics, 2006, 16: 577–587.
CONG S, LIANG Y. PID-like neural network nonlinear adaptive control for uncertain multivariable motion control systems [J]. Industrial Electronics, 2009, 56(10): 3872–3879.
SHU H, PI Y. PID neural networks for time-delay systems [J]. Computers & Chemical Engineering, 2000, 24(2): 859–862.
MARABA V A, KUZUCUOGLU A E. PID neural network based speed control of asynchronous motor using programmable logic controller [J]. Advances in Electrical and Computer Engineering, 2011, 11(4): 23–28.
XIAO Ye-lun, JIN Chang-jiang. Flight theory in atmospheric disturbance [M]. Beijing: National Defense Industry Press, 1993: 73–74. (in Chinese)
BEAL T R. Digital simulation of atmospheric turbulence [J]. Journal of Guidance, Control and Dynamics, 1993, 16(1): 132–138.
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Foundation item: Project(2011ZA51001) supported by National Aerospace Science Foundation of China
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Chen, Ym., He, Yl. & Zhou, Mf. Decentralized PID neural network control for a quadrotor helicopter subjected to wind disturbance. J. Cent. South Univ. 22, 168–179 (2015). https://doi.org/10.1007/s11771-015-2507-9
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DOI: https://doi.org/10.1007/s11771-015-2507-9