single-au.php

IJAT Vol.15 No.1 pp. 109-122
doi: 10.20965/ijat.2021.p0109
(2021)

Paper:

Fault-Tolerant Aircraft Control Based on Self-Constructing Fuzzy Neural Network for Quadcopter

Dejie Li, Pu Yang, Zhangxi Liu, Zixin Wang, and Zhiqing Zhang

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics
29 Yudao Street, Nanjing, Jiangsu 211106, China

Corresponding author

Received:
May 17, 2020
Accepted:
September 9, 2020
Published:
January 5, 2021
Keywords:
actuator faults, quadcopter, fault-tolerant aircraft control, sliding-mode control, adaptive self-constructing fuzzy neural network
Abstract

This paper proposes a fault-tolerant aircraft control method based on a self-constructed fuzzy neural network for quadcopters with multiple actuator faults. We first introduce the actuator failure model and the model uncertainty. Subsequently, we establish a framework for a self-constructed fuzzy neural network observer with an adaptive rate to obtain the estimated value of the nonlinear term of the module uncertainty. We also design a multivariable sliding mode fault-tolerant controller to ensure the stability of the aircraft under this fault condition. Finally, we conduct experiments using the Pixhawk 4 flight controller installed on the QBall-X4 UAV experimental platform, such that the use of the flight controller’s fault coprocessor and redundant sensor design reduces the crash that occurs during the debugging of the control algorithm. Compared to the existing intelligent fault-tolerant control technology, our proposed method employs fewer fuzzy rules, and the number of these rules can be adaptively adjusted when the system model changes. In the experimental test, the aircraft was still able to fly stably under multi-actuator failure and interference conditions, thereby proving the stability of the proposed controller.

Cite this article as:
Dejie Li, Pu Yang, Zhangxi Liu, Zixin Wang, and Zhiqing Zhang, “Fault-Tolerant Aircraft Control Based on Self-Constructing Fuzzy Neural Network for Quadcopter,” Int. J. Automation Technol., Vol.15, No.1, pp. 109-122, 2021.
Data files:
References
  1. [1] T. Li, Y. Zhang, and B. W. Gordon, “Nonlinear Fault-Tolerant Control of a Quadrotor UAV Based on Sliding Mode Control Technique,” IFAC Proc. Volumes, Vol.45, Issue 20, pp. 1317-1322, 2012.
  2. [2] J. Wang, Z. Zhao, and Y. Zheng, “NFTSM-based Fault Tolerant Control for Quadrotor Unmanned Aerial Vehicle with Finite-Time Convergence,” IFAC-PapersOnLine, Vol.51, Issue 24, pp. 441-446, 2018.
  3. [3] M. H. Amoozgar, A. Chamseddine, and Y. Zhang, “Fault-Tolerant Fuzzy Gain-Scheduled PID for a Quadrotor Helicopter Testbed in the Presence of Actuator Faults,” IFAC Proc. Volumes, Vol.45, Issue 3, pp. 282-2870, 2012.
  4. [4] H. Başak and E. Prempain, “Switched fault tolerant control for a quadrotor UAV,” IFAC-PapersOnLine, Vol.50, Issue 1, pp. 10363-10368, 2017.
  5. [5] I. Sadeghzadeh, A. Chamseddine, Y. Zhang, and D. Theilliol, “Control Allocation and Re-allocation for a Modified Quadrotor Helicopter against Actuator Faults,” IFAC Proc. Volumes, Vol.45, Issue 20, pp. 247-252, 2012.
  6. [6] A. Boche, J.-L. Farges, and H. De Plinval, “Reconfiguration control method for multiple actuator faults on UAV,” IFAC-PapersOnLine, Vol.50, Issue 1, pp. 12691-12697, 2017.
  7. [7] D. Nguyen, D. Saussié, and L. Saydy, “Robust Self-Scheduled Fault-Tolerant Control of a Quadrotor UAV,” IFAC-PapersOnLine, Vol.50, Issue 1, pp. 5761-5767, 2017.
  8. [8] P. Yang, Z. Liu, Y. Wang, and D. Li, “Adaptive Sliding Mode Fault-Tolerant Control for Uncertain Systems with Time Delay,” Int. J. Automation Technol., Vol.14, No.2, pp. 337-345, 2020.
  9. [9] S. Y. Vural, J. Dasdemir, and C. Hajiyev, “Passive Fault Tolerant Lateral Controller Design For an UAV,” IFAC-PapersOnLine, Vol.51, Issue 30, pp. 446-451, 2018.
  10. [10] Y. Yang, D. Iwakura, A. Namiki, K. Nonami, and W. Wang, “Autonomous Flight of Hexacopter Under Propulsion System Failure,” J. Robot. Mechatron., Vol.28, No.6, pp. 899-910, 2016.
  11. [11] M. Saied, B. Lussier, I. Fantoni, H. Shraim, and C. Franci, “Fault Diagnosis and Fault-Tolerant Control of an Octorotor UAV using motors speeds measurements,” IFAC-PapersOnLine, Vol.50, Issue 1, pp. 5263-5268, 2017.
  12. [12] C. Lijia, T. Yu, and Z. Guo, “Adaptive observer-based fault detection and active tolerant control for unmanned aerial vehicles attitude system,” IFAC-PapersOnLine, Vol.50, Issue 1, pp. 5263-5268, 2017.
  13. [13] B. Wang, X. Yu, L. Mu, and Y. Zhang, “Disturbance observer-based adaptive fault-tolerant control for a quadrotor helicopter subject to parametric uncertainties and external disturbances,” Mechanical Systems and Signal Processing, Vol.120, pp. 727-743, 2019.
  14. [14] M. Farahani and S. Ganjefar, “An online trained fuzzy neural network controller to improve stability of power systems,” Neurocomputing, Vol.162, No.25, pp. 245-255, 2015.
  15. [15] Y. C. Liu, S. Y. Liu, and N. Wang, “Fully-tuned fuzzy neural network based robust adaptive tracking control of unmanned underwater vehicle with thruster dynamics,” Neurocomputing, Vol.196, No.5, pp. 1-13, 2016.
  16. [16] A. Rubaai and P. Young, “Hardware/software implementation of fuzzy-neuralnetwork self-learning control methods for brushless DC motor drives,” IEEE Trans. on Industry Applications, Vol.52, No.1, pp. 414-424, 2016.
  17. [17] R. J. Wai, Y. F. Lin, and Y. K. Liu, “Design of adaptive fuzzy-neural-network control for a single-stage boost inverter,” IEEE Trans. on Power Electronics, Vol.30, No.12, pp. 7282-7298, 2015.
  18. [18] S. Tong and Y. Li, “Adaptive fuzzy output feedback tracking backstepping control of strict-feedback nonlinear systems with unknown dead zones,” IEEE Trans. Fuzzy Systems, Vol.20, No.1, pp. 160-180, 2012.
  19. [19] Y. Li, S. Tong, L. Liu, and G. Feng, “Adaptive output-feedback control design with prescribed performance for switched nonlinear systems,” Automatica, Vol.80, pp. 225-231, 2017.
  20. [20] H. Lu, M. Chang, and C. Tsai, “Adaptive self-constructing fuzzy neural network controller for hardware implementation of an inverted pendulum system,” Applied Soft Computing, Vol.11, Issue 5, pp. 3962-3975, 2011.
  21. [21] M. J. Er, T. P. Tan, and S. Y. Loh, “Control of a mobile robot using generalized dynamic fuzzy neural networks,” Microprocessors and Microsystems, Vol. 28, Issue 9, pp. 491-498, 2004.
  22. [22] H. Han, Z. Chen, H. Liu, and J. Qiao, “A self-organizing interval Type-2 fuzzy-neural-network for modeling nonlinear systems,” Neurocomputing, Vol.290, No.17, pp. 196-207, 2018.
  23. [23] S. Wen, M. Z. Q. Chen, Z. Zeng, T. Huang, and C. Li, “Adaptive Neural-Fuzzy Sliding-Mode Fault-Tolerant Control for Uncertain Nonlinear Systems,” IEEE Trans. on Systems, Man, and Cybernetics: Systems, Vol.47, No.8, pp. 2268-2278, 2017.
  24. [24] X. Yu, Y. Fu, P. Li, and Y. Zhang, “Fault-Tolerant Aircraft Control Based on Self-Constructing Fuzzy Neural Networks and Multivariable SMC Under Actuator Faults,” IEEE Trans. on Fuzzy Systems, Vol.26, No.4, pp. 2324-2335, 2018.
  25. [25] H. Bou-Ammar, H. Voos, and W. Ertel, “Controller design for quadrotor UAVs using reinforcement learning,” Proc. of 2010 IEEE Int. Conf. on Control Applications, pp. 2130-2135, 2010.
  26. [26] Y. Yu, S. Yang, M. Wang, C. Li, and Z. Li, “High Performance Full Attitude Control of a Quadrotor on SO(3),” Proc. of 2015 IEEE Int. Conf. on Robotics and Automation, pp. 1698-1703, 2015.
  27. [27] J. Diebel, “Representing Attitude: Euler Angles, Unit Quaternions, and Rotation Vectors,” Stanford University, 2006.
  28. [28] S. Bouabdallah and R. Siegwart, “Backstepping and sliding-mode techniques applied to an indoor micro quadrotor,” Proc. of Int. Conf. on Robotics and Automation, pp. 2247-2252, 2005.
  29. [29] E. H. Zheng, J. J. Xiong, and J. L. Luo, “Second order sliding mode control for a quadrotor UAV,” ISA Trans., Vol.53, Issue 4, pp. 1350-1356, 2014.
  30. [30] J. Xiong and G. Zhang, “Global fast dynamic terminal sliding mode control for a quadrotor UAV,” ISA Trans., Vol.66, pp. 233-240, 2017.
  31. [31] A. Sharifian, M. J. Ghadi, S. Ghavidel, L. Li, and J. Zhang, “A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data,” Renewable Energy, Vol.120, pp. 220-230, 2018.
  32. [32] H. Taghavifar and S. Rakheja, “Path-tracking of autonomous vehicles using a novel adaptive robust exponential-like-sliding-mode fuzzy type-2 neural network controller,” Mechanical Systems and Signal Processing, Vol.130, No.1, pp. 41-55, 2019.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 05, 2021