ISSN 0253-2778

CN 34-1054/N

open
Free to Publish. Free to Read.
Open AccessOpen Access JUSTC Original Paper

Design of quad-rotor general controller based on ensemble modeling method

  • 1.

    University of Electronic Science and Technology of China,Chengdu 611731

  • 2.

    China Aerodynamics Research and Development Center,Mianyang 621000

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2020.08.007
  • Received Date: 20 June 2020
  • Accepted Date: 07 July 2020
  • Rev Recd Date: 07 July 2020
  • Publish Date: 31 August 2020
    Abstract

  • Abstract

    To solve the problem of quad-rotor UAV general controller approximation, a method is proposed based on ensemble modeling to learn and construct the general form of controller. Quadrotor hover and forward flight tasks are designed and simulated on Matlab/Simulink to obtain training and test data sets. Then the state variables and process variables of the quadrotor flight are taken as inputs, and the lift forces of the rotors are taken as outputs to build an ensemble model to approximate the general controller. A single fixed size least squares support vector machines model and a deep belief networks model are compared with the ensemble modeling method. The experimental results show that the ensemble modeling method can get better results, and it is feasible to construct a general controller for quadrotor certain type of mission.

    Abstract

    To solve the problem of quad-rotor UAV general controller approximation, a method is proposed based on ensemble modeling to learn and construct the general form of controller. Quadrotor hover and forward flight tasks are designed and simulated on Matlab/Simulink to obtain training and test data sets. Then the state variables and process variables of the quadrotor flight are taken as inputs, and the lift forces of the rotors are taken as outputs to build an ensemble model to approximate the general controller. A single fixed size least squares support vector machines model and a deep belief networks model are compared with the ensemble modeling method. The experimental results show that the ensemble modeling method can get better results, and it is feasible to construct a general controller for quadrotor certain type of mission.
    • FullText(HTML)
  • loading
    • References(35)
  • [1]
    ZHAO J, LI Y, HU D, et al. Design on altitude control system of quad rotor based on laser radar[C]// IEEE International Conference on Aircraft Utility Systems. Beijing: IEEE, 2016: 105-109.
    [2]
    WU C, SU J B. Trajectory tracking of quadrotor based on disturbance rejection control[J]. Control Theory and Applications, 2016, 33(11): 1422-1430.
    [3]
    CARRILLO L R G, FLORES COLUNGA G R, SANAHUJA G, et al. Quad rotorcraft switching control: An application for the task of path following[J]. IEEE Transactions on Control Systems Technology, 2014, 22(4): 1255-1267.
    [4]
    TAYEBI A, MCGILVRAY S. Attitude stabilization of a VTOL quadrotor aircraft[J]. IEEE Transactions on Control Systems Technology, 2006, 14(3): 562-571.
    [5]
    KHATOON S, SHAHID M, IBRAHEEM C H. Dynamic modeling and stabilization of quadrotor using PID controller[C]// International Conference on Advances in Computing. Delhi, India: IEEE, 2014: 746-750.
    [6]
    MILHIM A, ZHANG Y, RABBATH C A. Gain scheduling based PID controller for fault tolerant control of quad-rotor UAV[C]// AIAA Infotech. Atlanta, USA: AIAA, 2010: No.3530.
    [7]
    BOUABDALLAH S, NOTH A, SIEGWART R. PID vs LQ control techniques applied to an indoor micro quadrotor[C]// IEEE/RSJ International Conference on Intelligent Robots and Systems. Sendi, Japan: IEEE, 2004: 2451-2456.
    [8]
    ARGENTIM L M, REZENDE W C, SANTOS P E, et al. PID, LQR and LQR-PID on a quadcopter platform[C]// International Conference on Informatics. Dhaka, Bangladesh: IEEE Computer Society, 2013:1-6.
    [9]
    PENA M, VIVAS E,RODRIGUEZ C. Simulation of the quadrotor controlled with LQR with integral effect. In ABCM Symposium Series in Mechatronics[J]. 2012, 5(1): 390-399.
    [10]
    RAFFO G V, ORTEGA M G, RUBIO F R. Backstepping/nonlinear H∞ control for path tracking of a quadrotor unmanned aerial vehicle[C]// American Control Conference. Seattle, USA: IEEE, 2008: 3356-3361.
    [11]
    RAFFO G V, ORTEGA M G, RUBIO F R. Path tracking of a UAV via an underactuated control strategy[J]. European Journal of Control, 2011, 17(2):194-213.
    [12]
    RAFFO G V, ORTEGA M G, RUBIO F R. An integral predictive/nonlinear H∞ control structure for a quadrotor helicopter[J]. Automatica, 2010, 46(1): 29-39.
    [13]
    GHANDOUR J, ABERKANE S, PONSART J C. Feedback linearization approach for standard and fault tolerant control: application to a quadrotor UAV testbed[J]. Journal of Physics Conference, 2014, 570(8): 082003.
    [14]
    MADANI T, BENALLEGUE A. Backstepping control for a quadrotor helicopter[C]// IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing: IEEE, 2007: 3255-3260.
    [15]
    BENALLEGUE A, MOKHTARI A, FRIDMAN L. Feedback linearization and high order sliding mode observer for a quadrotor UAV[C]// International Workshop on Variable Structure Systems. Alghero, Italy: IEEE, 2006: 365-372.
    [16]
    SUMANTRI B, UCHIYAMA N, SANO S, et al. Robust tracking control of a quad-rotor helicopter utilizing sliding mode control with a nonlinear sliding surface[J]. JSDD, 2013, 7(2): 226-241.
    [17]
    DYDEK Z, ANNASWAMY A, LAVRETSKY E. Adaptive control of quadrotor UAVs in the presence of actuator uncertainties[C/OL]// AIAA Infotech. Atlanda, USA: AIAA, 2015. [2020-04-28]. https://www.enu.kz/repository/2010/AIAA-2010-3416.pdf.
    [18]
    ISLAM S, LIU P X, EL SADDIK A. Nonlinear adaptive control for quadrotor flying vehicle[J]. Nonlinear Dynamics, 2014, 78(1): 117-133.
    [19]
    RAFFO, G V, ORTEGA M G, RUBIO F R. MPC with nonlinear H∞ control for path tracking of a quad-rotor helicopter[J]. IFAC Proceedings Volumes, 2008, 41(2): 8564-8569.
    [20]
    BANGURA M, MAHONY R. Real-time model predictive control for quadrotors[J]. IFAC Proceedings Volumes, 2014, 47(3): 11773-11780.
    [21]
    KURNAZ S, CETIN O, KAYNAK O. Fuzzy logic based approach to design of flight control and navigation tasks for autonomous unmanned aerial vehicles[J]. Journal of Intelligent and Robotic Systems, 2009, 54(1-3): 229-244.
    [22]
    BABAEI A R, MORTAZAVI M, MORADI M H. Classical and fuzzy-genetic autopilot design for unmanned aerial vehicles[J]. Applied Soft Computing, 2011, 11(1): 365-372.
    [23]
    DIERKS T, JAGANNATHAN S. Neural network output feedback control of a quadrotor UAV[C]// Decision and Control. Cancun, Mexico: IEEE, 2008: 3633-3639.
    [24]
    MOHAJERIN N, WASLANDER S L. State initialization for recurrent neural network modeling of time-series data[C]// International Joint Conference on Neural Networks. Anchorage, USA: IEEE, 2017: 2330-2337.
    [25]
    HAN B, ZHOU Y, DEVEERASETTY K K, et al. A review of control algorithms for quadrotor[C]// International Conference on Information and Automation. Wuyishan, China: IEEE, 2018: 951-956.
    [26]
    DIETTERICH T G. Machine learning research: Four current directions AI magazine[J]. AI Magazine, 1997, 8(4): 97-136.
    [27]
    POLIKAR R. Essemble based systems in decision making[J]. IEEE Circuits and Systems Magazine, 2006, 6(3): 21-45.
    [28]
    HUANG F J, ZHOU Z, ZHANG H J, et al. Pose invariant face recognition[C]// International Conference on Automatic Face and Gesture Recognition. Grenoble, France: ACM, 2000: 245-251.
    [29]
    FEILHAUER H, ASNER G, MARTIN R. Multi-method ensemble selection of spectral bands related to leaf biochemistry[J]. Remote Sensing of Environment: An Interdisciplinary Journal, 2015, 164: 57-65.
    [30]
    Orrell D. Ensemble forecasting in a system with model error[J]. Journal of the Atmospheric Sciences, 2005, 62(5): 1652-1659.
    [31]
    ROKACH L. 模式分类的集成方法[M]. 北京：国防工业出版社, 2015: 14-69.
    [32]
    SUYKENS JOHAN A K, VAN GESTEL T, DE BRABANTER J, et al. Least Squares Support Vector Machines[M]. Singapore: World Scientific, 2002: 178-195.
    [33]
    周志华. 机器学习[M]. 北京：清华大学出版社, 2016: 172.
    [34]
    WILLIAMS C K I, SEEGER M. Using the Nystrom method to speed up kernel machines[C]// Advances in Neural Information Processing Systems. Denver, USA: MIT Press, 2001: 682-688.
    [35]
    PERRONE M P, COOPER L N. When Networds Disagree: Ensemble Methods for Hybrid Neural Networds[A]. Mammone R L, ed., Chapman and Hall, 1993: 126-142.)
    • Supplements(0)
    • Related Articles
    • Track Citations
  • 加载中

Catalog

    |
    Get Citation
    PDF
    XML
    [1]
    ZHAO J, LI Y, HU D, et al. Design on altitude control system of quad rotor based on laser radar[C]// IEEE International Conference on Aircraft Utility Systems. Beijing: IEEE, 2016: 105-109.
    [2]
    WU C, SU J B. Trajectory tracking of quadrotor based on disturbance rejection control[J]. Control Theory and Applications, 2016, 33(11): 1422-1430.
    [3]
    CARRILLO L R G, FLORES COLUNGA G R, SANAHUJA G, et al. Quad rotorcraft switching control: An application for the task of path following[J]. IEEE Transactions on Control Systems Technology, 2014, 22(4): 1255-1267.
    [4]
    TAYEBI A, MCGILVRAY S. Attitude stabilization of a VTOL quadrotor aircraft[J]. IEEE Transactions on Control Systems Technology, 2006, 14(3): 562-571.
    [5]
    KHATOON S, SHAHID M, IBRAHEEM C H. Dynamic modeling and stabilization of quadrotor using PID controller[C]// International Conference on Advances in Computing. Delhi, India: IEEE, 2014: 746-750.
    [6]
    MILHIM A, ZHANG Y, RABBATH C A. Gain scheduling based PID controller for fault tolerant control of quad-rotor UAV[C]// AIAA Infotech. Atlanta, USA: AIAA, 2010: No.3530.
    [7]
    BOUABDALLAH S, NOTH A, SIEGWART R. PID vs LQ control techniques applied to an indoor micro quadrotor[C]// IEEE/RSJ International Conference on Intelligent Robots and Systems. Sendi, Japan: IEEE, 2004: 2451-2456.
    [8]
    ARGENTIM L M, REZENDE W C, SANTOS P E, et al. PID, LQR and LQR-PID on a quadcopter platform[C]// International Conference on Informatics. Dhaka, Bangladesh: IEEE Computer Society, 2013:1-6.
    [9]
    PENA M, VIVAS E,RODRIGUEZ C. Simulation of the quadrotor controlled with LQR with integral effect. In ABCM Symposium Series in Mechatronics[J]. 2012, 5(1): 390-399.
    [10]
    RAFFO G V, ORTEGA M G, RUBIO F R. Backstepping/nonlinear H∞ control for path tracking of a quadrotor unmanned aerial vehicle[C]// American Control Conference. Seattle, USA: IEEE, 2008: 3356-3361.
    [11]
    RAFFO G V, ORTEGA M G, RUBIO F R. Path tracking of a UAV via an underactuated control strategy[J]. European Journal of Control, 2011, 17(2):194-213.
    [12]
    RAFFO G V, ORTEGA M G, RUBIO F R. An integral predictive/nonlinear H∞ control structure for a quadrotor helicopter[J]. Automatica, 2010, 46(1): 29-39.
    [13]
    GHANDOUR J, ABERKANE S, PONSART J C. Feedback linearization approach for standard and fault tolerant control: application to a quadrotor UAV testbed[J]. Journal of Physics Conference, 2014, 570(8): 082003.
    [14]
    MADANI T, BENALLEGUE A. Backstepping control for a quadrotor helicopter[C]// IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing: IEEE, 2007: 3255-3260.
    [15]
    BENALLEGUE A, MOKHTARI A, FRIDMAN L. Feedback linearization and high order sliding mode observer for a quadrotor UAV[C]// International Workshop on Variable Structure Systems. Alghero, Italy: IEEE, 2006: 365-372.
    [16]
    SUMANTRI B, UCHIYAMA N, SANO S, et al. Robust tracking control of a quad-rotor helicopter utilizing sliding mode control with a nonlinear sliding surface[J]. JSDD, 2013, 7(2): 226-241.
    [17]
    DYDEK Z, ANNASWAMY A, LAVRETSKY E. Adaptive control of quadrotor UAVs in the presence of actuator uncertainties[C/OL]// AIAA Infotech. Atlanda, USA: AIAA, 2015. [2020-04-28]. https://www.enu.kz/repository/2010/AIAA-2010-3416.pdf.
    [18]
    ISLAM S, LIU P X, EL SADDIK A. Nonlinear adaptive control for quadrotor flying vehicle[J]. Nonlinear Dynamics, 2014, 78(1): 117-133.
    [19]
    RAFFO, G V, ORTEGA M G, RUBIO F R. MPC with nonlinear H∞ control for path tracking of a quad-rotor helicopter[J]. IFAC Proceedings Volumes, 2008, 41(2): 8564-8569.
    [20]
    BANGURA M, MAHONY R. Real-time model predictive control for quadrotors[J]. IFAC Proceedings Volumes, 2014, 47(3): 11773-11780.
    [21]
    KURNAZ S, CETIN O, KAYNAK O. Fuzzy logic based approach to design of flight control and navigation tasks for autonomous unmanned aerial vehicles[J]. Journal of Intelligent and Robotic Systems, 2009, 54(1-3): 229-244.
    [22]
    BABAEI A R, MORTAZAVI M, MORADI M H. Classical and fuzzy-genetic autopilot design for unmanned aerial vehicles[J]. Applied Soft Computing, 2011, 11(1): 365-372.
    [23]
    DIERKS T, JAGANNATHAN S. Neural network output feedback control of a quadrotor UAV[C]// Decision and Control. Cancun, Mexico: IEEE, 2008: 3633-3639.
    [24]
    MOHAJERIN N, WASLANDER S L. State initialization for recurrent neural network modeling of time-series data[C]// International Joint Conference on Neural Networks. Anchorage, USA: IEEE, 2017: 2330-2337.
    [25]
    HAN B, ZHOU Y, DEVEERASETTY K K, et al. A review of control algorithms for quadrotor[C]// International Conference on Information and Automation. Wuyishan, China: IEEE, 2018: 951-956.
    [26]
    DIETTERICH T G. Machine learning research: Four current directions AI magazine[J]. AI Magazine, 1997, 8(4): 97-136.
    [27]
    POLIKAR R. Essemble based systems in decision making[J]. IEEE Circuits and Systems Magazine, 2006, 6(3): 21-45.
    [28]
    HUANG F J, ZHOU Z, ZHANG H J, et al. Pose invariant face recognition[C]// International Conference on Automatic Face and Gesture Recognition. Grenoble, France: ACM, 2000: 245-251.
    [29]
    FEILHAUER H, ASNER G, MARTIN R. Multi-method ensemble selection of spectral bands related to leaf biochemistry[J]. Remote Sensing of Environment: An Interdisciplinary Journal, 2015, 164: 57-65.
    [30]
    Orrell D. Ensemble forecasting in a system with model error[J]. Journal of the Atmospheric Sciences, 2005, 62(5): 1652-1659.
    [31]
    ROKACH L. 模式分类的集成方法[M]. 北京：国防工业出版社, 2015: 14-69.
    [32]
    SUYKENS JOHAN A K, VAN GESTEL T, DE BRABANTER J, et al. Least Squares Support Vector Machines[M]. Singapore: World Scientific, 2002: 178-195.
    [33]
    周志华. 机器学习[M]. 北京：清华大学出版社, 2016: 172.
    [34]
    WILLIAMS C K I, SEEGER M. Using the Nystrom method to speed up kernel machines[C]// Advances in Neural Information Processing Systems. Denver, USA: MIT Press, 2001: 682-688.
    [35]
    PERRONE M P, COOPER L N. When Networds Disagree: Ensemble Methods for Hybrid Neural Networds[A]. Mammone R L, ed., Chapman and Hall, 1993: 126-142.)
    Recommended articles
    TrendMD
    Volume 50 Issue 8 page: 1084-1092
    Cover

    Article Metrics

    Article views (34) PDF downloads(171)
    Proportional views

    Copyright © Editorial Office of JUSTC, All Rights Reserved. 皖ICP备05002528号

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return