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Path following of Nano quad-rotors using a novel disturbance observer-enhanced dynamic inversion approach

Published online by Cambridge University Press:  17 June 2019

Yuan Wang  and
Xiangming Zheng
Yuan Wang
Affiliation:
Nanjing University of Aeronautics and Astronautics Key Laboratory of Fundamental Science for National Defence-Advanced Design Technology of Flight Vehicle, Nanjing, China
Xiangming Zheng*
Affiliation:
Nanjing University of Aeronautics and Astronautics Key Laboratory of Fundamental Science for National Defence-Advanced Design Technology of Flight Vehicle, Nanjing, China
*
[email protected]
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Abstract

The model of Nano quad-rotors contains many uncertainties such as an external disturbance from a wind field, highly non-linear strong coupling between variables and body measurement errors. To deal with these uncertainties and control the Nano quad-rotors, a novel data-based disturbance observer (DO) is firstly proposed to observe disturbances from a wind field and perturbations from errors of parameter estimation. Then the DO is used to improve the conventional dynamic inversion (DI) method to obtain an enhanced dynamic inversion (EDI) method, which relies only on roughly estimated geometrical parameters, thus eliminating the largest flaw of conventional DI, namely depending on detailed plant information. Simulation results show that the method proposed achieved good trajectory tracking with only roughly estimated geometrical values under wind field; the DO proposed can accurately estimate disturbance from a wind field and perturbation from error of parameter estimation.

Keywords

Nano quad-rotorDisturbance observerDynamic inversionPath following
Type
Research Article
Information
The Aeronautical Journal , Volume 123 , Issue 1266 , August 2019 , pp. 1122 - 1134
Copyright
© Royal Aeronautical Society 2019 

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References

REFERENCES

Lin, Q., Cai, Z.H., Wang, Y.X., Yang, J.P. and Chen, L.F. Adaptive flight control design for quadrotor UAV based on dynamic inversion and neural networks, Third International Conference on Instrumentation, Measurement, Computer, Communication and Control, IEEE, 2014, Shenyang, China, pp 1461–1466. doi:10.1109/IMCCC.2013.326.CrossRefGoogle Scholar
Das, A., Subbarao, K. and Lewis, F. Dynamic inversion with zero-dynamics stabilisation for quadrotor control, IET Control Theory & Applications, 2009, 3, (3), pp 303–314. doi:10.1049/iet-cta:20080002.CrossRefGoogle Scholar
Sá, R.C., Araújo, A.L.C.D., Varela, A.T. and Barreto, G.D.A. Construction and PID control for stability of an unmanned aerial vehicle of the type quadrotor, Robotics Symposium and Competition, IEEE, 2013, 10, Arequipa, Peru, pp 95–99. doi:10.1109/LARS.2013.64.CrossRefGoogle Scholar
Bouabdallah, S., Noth, A. and Siegwart, R. PID vs LQ control techniques applied to an indoor micro quadrotor, RSJ International Conference on Intelligent Robots and Systems, IEEE, 2004, 3, Sendai, Japan, pp 2451–2456. doi:10.1109/IROS.2004.1389776.CrossRefGoogle Scholar
Islam, S., Faraz, M., Ashour, R.K., Dias, J. and Seneviratne, L.D. Robust adaptive control of quadrotor unmanned aerial vehicle with uncertainty, International Conference on Robotics and Automation, IEEE, 2015, Seattle, WA, USA, pp 1704– 1709. doi:10.1109/ICRA.2015.7139417.CrossRefGoogle Scholar
Fan, Y., Cao, Y. and Zhao, Y. Sliding mode control for nonlinear trajectory tracking of a quadrotor, Chinese Control Conference (CCC), IEEE, 2017, Dalian, China, pp 6676–6680. doi:10.23919/ChiCC.2017.8028413.CrossRefGoogle Scholar
Madani, T. and Benallegue, A. Control of a quadrotor mini-helicopter via full state backstepping technique, Proceedings of the IEEE Conference on Decision and Control, IEEE, 2007, San Diego, CA, USA, pp 1515–1520. doi:10.1109/CDC.2006.377548.CrossRefGoogle Scholar
Schumacher, C. and Khargonekar, P.P. Missile autopilot designs using H1 control with gain scheduling and dynamic inversion, J Guidance, Control, and Dynamics, 1998, 21, (2), pp 234–243. doi:10.2514/2.4248.CrossRefGoogle Scholar
Ye, B., Lan, W., Jin, H. and Huang, C. Linear active disturbance rejection control of quadrotor’s altitude and attitude, Automation, IEEE, 2017, pp 1188–1193. doi:10.1109/YAC.2017.7967593.CrossRefGoogle Scholar
Libo, Q., Wenya, Z., Long’en, L. and Wenhui, J. Active disturbance rejection control system design for quadrotor, Chinese Control Conference (CCC), IEEE, 2017, Dalian, China, pp 6530–6534. doi:10.23919/ChiCC.2017.8028413.CrossRefGoogle Scholar
Beard, R.W. and Mclain, T.W. Small unmanned aircraft: Theory and practice, Princeton University Press, 2012, New Jersey.10.1515/9781400840601CrossRefGoogle Scholar
Derafa, L., Ouldali, A., Madani, T. and Benallegue, A. Non-linear control algorithm for the four rotors UAV attitude tracking problem, Aeronautical J, 2011, 115, (1165), pp 175–185. doi:10.1017/S0001924000005571.CrossRefGoogle Scholar
Hou, Z., Chi, R. and Gao, H. An overview of dynamic-linearization-based data-driven control and applications, IEEE Transactions Industrial Electronics, 64, (5), 2017, pp 4076–4090. doi:10.1109/TIE.2016.2636126.CrossRefGoogle Scholar
Li, X. and Chen, M. Extended state observer-based nonlinear cascade proportional-integral–derivative control of the Nano quadrotor, Advances in Mechanical Engineering 2016, 8, (12), 1687814016680799. doi:10.1177/1687814016680799.CrossRefGoogle Scholar