Skip Navigation Links
Journal of Vibration Testing and System Dynamics

C. Steve Suh (editor), Pawel Olejnik (editor),

Xianguo Tuo (editor)

Pawel Olejnik (editor)

Lodz University of Technology, Poland


C. Steve Suh (editor)

Texas A&M University, USA


Xiangguo Tuo (editor)

Sichuan University of Science and Engineering, China


The Trajectory Tracking Control of fixed-wing UAV with Self-organizing Fuzzy Neural Network to Identify and Compensate the Modelling Uncertainties

Journal of Vcibration Testing and System Dynamics 3(1) (2019) 91--107 | DOI:10.5890/JVTSD.2019.03.007

Yu Tang$^{1}$,$^{2}$,$^{3}$, Lijia Cao$^{1}$,$^{2}$,$^{3}$, Da Lin$^{1}$,$^{2}$

$^{1}$ Artificial Intelligence Key Laboratory of Sichuan Province, Zigong, 643000, P.R. China

$^{2}$ Sichuan University of Science & Engineering, Zigong, 643000, P.R. China

$^{3}$ Sichuan Key Provincial Research Base of Intelligent Tourism, Zigong, 643000, P.R. China

Download Full Text PDF



Firstly, the dynamics model of fixed-wing UAV is given in the paper and an adaptive controller of the mode is designed based on dynamic inversion method, then a compensation strategy called SFNN-RC based on the self-organizing fuzzy neural network techniques and a robust controller is designed to online identify and compensate the modeling uncertainty part of fixed-wing UAV system in trajectory tracking control process. The robust controller in SFNN-RC is presented to optimize the convergence performance of the self-organizing fuzzy neural network. The stability of the SFNN-RC method can be proved based on Lyapunov theory. To demonstrate the effectiveness of the proposed method, some simulation results are illustrated in this paper.


This project is supported by the National Natural Science Foundation of China (Grants 61640223 and 11705122), the National Aerospace Science Foundation of China (Grant 201605U8002), the Open Project Program of the State Key Laboratory of Management and Control for Complex Systems (Grant 20160106), Sichuan Science and Technology Program (Grants 19ZDZX0037, 2018JY0512, 2016JY0179), the Innovation Group Build Plan for the Universities of Sichuan Province (Grant 15TD0024), the Key Project of Artificial Intelligence Key Laboratory of Sichuan Province (Grants 2016RYJ02 and 2015RYJ02), the Natural Science Foundation of Sichuan University of Science & Engineering (Grant 2018RCL18) and Sichuan Key Provincial Research Base of Intelligent Tourism Foundation (Grant ZHZJ18-01).


  1. [1]  Rabbath, C.A. and Lechevin, N. (2010), Safety and Reliability in Cooperating Unmanned Aerial Systems, WORLD SCIENTIFIC.
  2. [2]  Invernizzi, D. and Lovera, M. (2018), Trajectory tracking control of thrust-vectoring UAVs, Automatica, 95, 180-186.
  3. [3]  Beard, R.W. and McLain, T.W. (2012), Small unmanned aircraft: Theory and practice, Princeton university press.
  4. [4]  Feng, G. (1995), A compensating scheme for robot tracking based on neural networks, Robotics & Autonomous Systems, 15, 199-206.
  5. [5]  Drouot, A., Richard, E., and Boutayeb, M. (2014), Hierarchical backstepping-based control of a gun launched MAV in crosswinds: Theory and experiment, Control Engineering Practice, 25, 16-25.
  6. [6]  Boukattaya,M., Mezghani, N., andDamak, T. (2018), Adaptive nonsingular fast terminal sliding-mode control for the tracking problem of uncertain dynamical systems, Isa Transactions, 77, 1-19.
  7. [7]  Song, Z. and Sun, K. (2017), Adaptive compensation control for attitude adjustment of quad-rotor unmanned aerial vehicle, Isa Transactions, 69, 242-255.
  8. [8]  Yang, X., Cui,J., Lao, D., Li, D., and Chen, J. (2016), Input Shaping Enhanced Active Disturbance Rejection Control For A Twin Rotor Multi-Input Multi-Output System (TRMS), Isa Transactions, 62, 287-298.
  9. [9]  Arifianto, O. and Farhood, M. (2015), Optimal control of a small fixed-wing UAV about concatenated trajectories, Control Engineering Practice, 40, 113-132.
  10. [10]  Lu, P., Kampen, E.J.V., Visser, C.D., and Chu, Q. (2016), Aircraft fault-tolerant trajectory control using incremental nonlinear dynamic inversion, Control Engineering Practice, 57, 126-141.
  11. [11]  Vanek, B., Balas, G.J., and Arndt, R.E.A. (2010), Linear, parameter-varying control of a supercavitating vehicle, Control Engineering Practice, 18, 1003-1012.
  12. [12]  Cui, Y., Xu, L., Fei, M., and Shen, Y. (2017), Observer based robust integral sliding mode load frequency control for wind power systems, Control Engineering Practice, 65, 1-10.
  13. [13]  Liu, Z., Chen, C., and Zhang, Y. (2015), Decentralized robust fuzzy adaptive control of humanoid robot manipulation with unknown actuator backlash, IEEE Transactions on Fuzzy Systems, 23, 605-616.
  14. [14]  Coyle, D., Prasad, G., and Mcginnity, T.M. (2009), Faster self-organizing fuzzy neural network training and a hyperparameter analysis for a brain-computer interface, IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, 39, 1458-1471.
  15. [15]  Han, H., Lin, Z., and Qiao, J. (2017), Modeling of nonlinear systems using the self-organizing fuzzy neural network with adaptive gradient algorithm, Neurocomputing, 266, 566-578.
  16. [16]  Li, C., Lee, C.Y., and Cheng, K.H. (2004), Pseudoerror-based self-organizing neuro-fuzzy system, IEEE Transactions on Fuzzy Systems, 12, 812-819.
  17. [17]  Lin, D. andWang, X. (2011), Self-organizing adaptive fuzzy neural control for the synchronization of uncertain chaotic systems with random-varying parameters, Neurocomputing, 74, 2241-2249.
  18. [18]  Hsu, C.F., Lin, C.M., and Lee, T.T. (2006), Wavelet adaptive backstepping control for a class of nonlinear systems, IEEE Transactions on Neural Networks, 17, 1175.
  19. [19]  Ducard, G.J. (2009), Fault-tolerant flight control and guidance systems: Practical methods for small unmanned aerial vehicles, Springer Science & Business Media.
  20. [20]  Saha, A. and Das, S. (2018), On the unification of possibilistic fuzzy clustering: Axiomatic development and convergence analysis, Fuzzy Sets and Systems, 340, 73-90.
  21. [21]  Zarinbal, M., Fazel Zarandi, M.H., and Turksen, I.B. (2015), Relative entropy collaborative fuzzy clustering method, Pattern Recognition, 48, 933-940.
  22. [22]  Carvalho, F.D.A.T., Tenório, C.P., and Cavalcanti Junior, N.L. (2006), Partitional fuzzy clustering methods based on adaptive quadratic distances, Fuzzy Sets and Systems, 157, 2833-2857.
  23. [23]  Lee, T., Wang, P., Hsiao, C., and Hsu, C. (2017), Design of self-constructing fuzzy wavelet neural control system, Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS), 2017 Joint 17th World Congress of International, IEEE, 2017, pp. 1-5.
  24. [24]  Chen, Y., Wang, D., and Tong, S. (2016), Forecasting studies by designing Mamdani interval type-2 fuzzy logic systems: With the combination of BP algorithms and KM algorithms, Neurocomputing, 174, 1133-1146.
  25. [25]  Miao, B. and Li, T. (2015), A novel neural network-based adaptive control for a class of uncertain nonlinear systems in strict-feedback form, Nonlinear Dynamics, 79, 1005-1013.
  26. [26]  Park, S. (2013), Modeling with vortex lattice method and frequency sweep flight test for a fixed-wing UAV, Control Engineering Practice, 21, 1767-1775.
  27. [27]  Austin, R. (2011), Unmanned aircraft systems: UAVS design, development and deployment, John Wiley & Sons.
  28. [28]  Raffo, G.V., Ortega, M.G., and Rubio, F.R. (2010), An integral predictive/nonlinear H∞ control structure for a quadrotor helicopter, Automatica, 46, 29-39.
  29. [29]  Ambati, P.R. and Padhi, R. (2017), Robust auto-landing of fixed-wing UAVs using neuro-adaptive design, Control Engineering Practice, 60, 218-232.
  30. [30]  Kownacki, C. and Ambroziak, L. (2017), Local and asymmetrical potential field approach to leader tracking problem in rigid formations of fixed-wing UAVs, Aerospace Science and Technology, 68, 465-474.