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

WU Shichong; LIAO Fei; WU Wenhua; FU Zaiming

doi:10.3969/j.issn.0253-2778.2020.08.007

JUSTC > 2020 > 50(8): 1084. > DOI: 10.3969/j.issn.0253-2778.2020.08.007

PDF (6949 KB)

Open 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: JUSTC, 2020, 50(8): 1084-1092

https://doi.org/10.3969/j.issn.0253-2778.2020.08.007

More Information

Received Date: June 19, 2020
Revised Date: July 06, 2020
Accepted Date: July 06, 2020
Published Date: August 30, 2020

Full text PDF

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)

References (35)

References

[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.)

Cited By

Track Citations

Get Citation

{{if article.articleBusiness.pdfLink && article.articleBusiness.pdfLink != ''}} {{else}} {{/if}}PDF

XML

References

[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.)

[1]	Shuang Wang, Hanbing Xue, Lizheng Wang. An empirical study on the effect of user engagement on personalized free-content promotion based on a causal machine learning model[J]. JUSTC, 2024, 54(10): 1005. DOI: 10.52396/JUSTC-2023-0063
[2]	Jie Qian, Junfan Xia, Bin Jiang. Machine learning molecular dynamics simulations of liquid methanol[J]. JUSTC, 2024, 54(6): 0603. DOI: 10.52396/JUSTC-2024-0031
[3]	Jiatong Hu, Mingqing Liu, Hongqi Li, Jiayin Yue, Wei Wang, Ji Liu. Association study on bone metabolism in type 2 diabetes by using machine learning[J]. JUSTC, 2023, 53(12): 1205. DOI: 10.52396/JUSTC-2023-0089
[4]	Can Yang, Qi Li, Yan Liu, Ling Zhang, Jian Gao, Xu Steven Xu, Min Yuan. Machine-learning diet quality score and risk of cardiovascular disease[J]. JUSTC, 2023, 53(12): 1204. DOI: 10.52396/JUSTC-2023-0067
[5]	Jie Wu, Yumeng Wu. Machine learning in data envelopment analysis: A smart mechanism for indicator selection[J]. JUSTC, 2022, 52(12): 5-1-5-9. DOI: 10.52396/JUSTC-2022-0106
[6]	XIN Shouyu, ZHENG Ruirui, ZHOU Yu, LIU Wenpeng, HE Jianjun. A one-shot learning algorithm using support set information during training[J]. JUSTC, 2020, 50(8): 1187. DOI: 10.3969/j.issn.0253-2778.2020.08.020
[7]	LONG Fei, HUANG Kun, LI Feng. A novel mapping between machine learning and phase transition[J]. JUSTC, 2020, 50(1): 18-28. DOI: 10.3969/j.issn.0253-2778.2020.01.003
[8]	SUI Hongjian, SHANG Weiwei, LI Xiang, CONG Shuang. Robot control policy transfer based on progressive neural network[J]. JUSTC, 2019, 49(10): 812-819. DOI: 10.3969/j.issn.0253-2778.2019.10.006
[9]	YANG Ziwen, CHEN Lei, PU Jianyu. Recognizing emotions from abstract paintings using convolutional neural network with two-layer transfer learning scheme[J]. JUSTC, 2019, 49(1): 40-48. DOI: 10.3969/j.issn.0253-2778.2019.01.006
[10]	LIN Yupeng, XIE Zhige, XU Kai, CHEN Fei, LIU Ligang. Fast cancer diagnosis based on extreme learning machine[J]. JUSTC, 2018, 48(2): 154-160. DOI: 10.3969/j.issn.0253-2778.2018.02.010

TrendMD

Volume 50 Issue 8 PP. 1084-1092

Cover

Keywords

Article Metrics

Article views (107) PDF downloads (961)

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

Abstract

Abstract

References

Related Articles

Catalog

References

Related Articles

TrendMD

Article Metrics

Authors

Browse

Contact Us

About

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

Share

Tools

Abstract

Abstract

References

Related Articles

Catalog

References

Related Articles

TrendMD

Article Metrics

Authors

Browse

Contact Us

About

Export File

Citation

Format

Content