PD+SMC Quadrotor Control for Altitude and Crack Recognition Using Deep Learning

  • J. M. Vazquez-NicolasEmail author
  • Erik Zamora
  • Iván González-Hernández
  • Rogelio Lozano
  • Humberto Sossa


Building inspection is a vital task because infrastructure damage puts people at risk or causes economic losses. Thanks to the technological breakthroughs in regard to Unmanned Aerial Vehicles (UAVs) and intelligent systems, there is a real possibility to implement an inspection by means of these technologies. UAVs allow reaching difficult places and, depending on the hardware carried onboard, take data or compute algorithms to understand the environment. This paper proposes a real-time robust altitude control strategy for a quadrotor aircraft, also a convolutional neuronal network for crack recognition is developed. The main idea of this proposal is to lay the background for an autonomous system for the inspection of structures using a UAV. For the robust control, a combination of two control actions, one linear (PD) and another nonlinear (Sliding Mode) is used. The combination of these control actions allows increasing the system’s performance. To verify the satisfactory performance of proposed control law, simulations and experimental results with a quadrotor, in the presence of disturbances, are presented. For crack recognition in images, several experiments were carried out validating the proposed model. For CNN training, a database of cracks was built from images taken from the Internet.


Deep learning embedded control system inspection quadrotor aircraft robust altitude control UAV 


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  1. [1]
    J. M. Vazquez-Nicolas, E. Zamora, I. Gonzalez-Hernandez, R. Lozano, and H. Sossa, “Towards automatic inspection: crack recognition based on Quadrotor UAVtaken images,” Proc. of the International Conference on Unmanned Aircraft Systems (ICUAS), pp. 654–659, 2018.Google Scholar
  2. [2]
    A. P. Sandiwan, A. Cahyadi, and S. Herdjunanto, “Robust proportional-derivative control on SO(3) with disturbance compensation for quadrotor UAV,” International Journal of Control, Automation and Systems, vol. 15, no. 5, pp. 2329–2342. October 2017.CrossRefGoogle Scholar
  3. [3]
    H. Liu, Y. Bai, G. Lu, Z Shi, and Y. Zhong, “Robust tracking control of a quadrotor helicopter,” Journal of Intelligent & Robotic Systems, vol. 75, no. 3–4, pp. 595–608, September 2014.CrossRefGoogle Scholar
  4. [4]
    M. Elfeky, M. Elshafei, A. A. Saif, and M. F. Al-Malki, “Modeling and simulation of quadrotor UAV with tilting rotors,” International Journal of Control, Automation and Systems, vol. 14, no. 4, pp. 1047–1055, August 2016.CrossRefGoogle Scholar
  5. [5]
    R. Rafifandi, D. L. Asri, E. Ekawati, and E. M. Budi, “Leader-follower formation control of two quadrotor UAVs,” SN Applied Sciences, vol. 1, no. 6, pp. 539, June 2019.Google Scholar
  6. [6]
    S. Jeong and S. Jung, “Cartesian space control of a quadrotor system based on low cost localization under a vision system,” International Journal of Control, Automation and Systems, vol. 14, no. 2, pp. 549–559, April 2016.CrossRefGoogle Scholar
  7. [7]
    Z. Zuo, “Trajectory tracking control design with commandfiltered compensation for a quadrotor,” IET Control Theory & Applications, vol. 4, no. 11, pp. 2343–2355, November 2010.MathSciNetCrossRefGoogle Scholar
  8. [8]
    S. Barghandan, M. A. Badamchizadeh, and M. R. Jahed-Motlagh, “Improved adaptive fuzzy sliding mode controller for robust fault tolerance of a quadrotor,” International Journal of Control, Automation and Systems, vol. 15, no. 1, pp. 427–41, February 2017.CrossRefGoogle Scholar
  9. [9]
    R. López-Gutiérrez, A. E. Rodriguez-Mata, and S. Salazar, “Robust quadrotor control: attitude and altitude real-time results,” Journal of Intelligent & Robotic Systems, vol. 88, no. 2–4, pp. 299–312, December 2017.CrossRefGoogle Scholar
  10. [10]
    A. Aboudonia, R. Rashad, and A. El-Badawy, “Composite hierarchical anti-disturbance control of a quadrotor UAV in the presence of matched and mismatched disturbances,” Journal of Intelligent & Robotic Systems, vol. 90, no. 1–2, pp. 201–216, May 2018.CrossRefGoogle Scholar
  11. [11]
    J. Escareño, S. Salazar, and H. Romero, “Trajectory control of a quadrotor subject to 2d wind disturbances,” Journal of Intelligent & Robotic Systems, vol. 70, no. 1–4, pp. 51–63, April 2013.CrossRefGoogle Scholar
  12. [12]
    Z. Liu, X. Liu, J. Chen, and C. Fang, “Altitude control for variable load quadrotor via learning rate based robust sliding mode controller,” IEEE Access, vol. 7, pp. 9736–9744, January 2019.CrossRefGoogle Scholar
  13. [13]
    A. L’Afflitto, R. B. Anderson, and K. Mohammadi, “An introduction to nonlinear robust control for unmanned quadrotor aircraft: how to design control algorithms for quadrotors using sliding mode control and adaptive control techniques [Focus on Education],” IEEE Control Systems Magazine, vol. 38, no. 3, pp. 102–121, June 2018.CrossRefGoogle Scholar
  14. [14]
    L. R. Salinas, D. Santiago, E. Slawiñski, V. A. Mut, D. Chavez, P. Leica, and O. Camacho, “P+d plus sliding mode control for bilateral teleoperation of a mobile robot,” International Journal of Control, Automation and Systems, vol. 16, no. 4, pp. 1927–1937, August 2018.CrossRefGoogle Scholar
  15. [15]
    P. R. Ouyang, J. Acob, and V. Pano, “PD with sliding mode control for trajectory tracking of robotic system,” Robotics and Computer-Integrated Manufacturing, vol. 30, no. 2, pp. 189–200, April 2014.CrossRefGoogle Scholar
  16. [16]
    J. Tang, P. R. Ouyang, W. H. Yue, and H. M. Kang, “Nonlinear PD sliding mode control for robotic manipulator,” Proc. of the IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 1004–1008, 2017.Google Scholar
  17. [17]
    P. R. Ouyang, J. Tang, W. H. Yue, and S. Jayasinghe, “Adaptive PD plus sliding mode control for robotic manipulator,” Proc. of the IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 930–934, 2016.Google Scholar
  18. [18]
    Newspaper “La Razon”, “Damaged building in the earthquake in Mexico City,” La Razon, 12 July, 2018., Accessed: September 1, 2018.Google Scholar
  19. [19]
    R. G. Lins and S. N. Givigi, “Automatic crack detection and measurement based on image analysis,” IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 3, pp. 583–590, March 2016.CrossRefGoogle Scholar
  20. [20]
    Y. Shi, L. Cui, Z. Qi, F. Meng, and Z. Chen, “Automatic road crack detection using random structured forests,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 12, pp. 3434–3445, Decemeber 2016.CrossRefGoogle Scholar
  21. [21]
    A. M. A. Talab, Z. Huang, F. Xi, and L. HaiMing, “Detection crack in image using Otsu method and multiple filtering in image processing techniques,” Optik, vol. 127, no. 3, pp. 1030–1033, February 2016.CrossRefGoogle Scholar
  22. [22]
    M. D. Phung, V. T. Hoang, T. H. Dinh, and Q. Ha, “Automatic crack detection in built infrastructure using unmanned aerial vehicles,” Proc. of the 34th International Symposium on Automation and Robotics in Construction, pp. 823–829, 2017.Google Scholar
  23. [23]
    L. Wang and Z. Zhang, “Automatic detection of wind turbine blade surface cracks based on UAV-taken images,” IEEE Transactions on Industrial Electronics, vol. 64, no. 9, pp. 7293–7303, Septemeber 2017.CrossRefGoogle Scholar
  24. [24]
    S. S. Choi and E. K. Kim, “Building crack inspection using small UAV,” Proc. of the 17th International Conference on Advanced Communication Technology (ICACT), pp. 235–238, 2015.Google Scholar
  25. [25]
    N. Sun, T. Yang, Y. Fang, Y. Wu, and H. Chen, “Transportation control of double-pendulum cranes with a nonlinear quasi-PID scheme: design and experiments,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 7, pp. 1408–1418, July 2019.CrossRefGoogle Scholar
  26. [26]
    R. Lozano, Unmanned Aerial Vehicles: Embedded Control, Wiley, Hoboken, NJ, 2013.CrossRefGoogle Scholar
  27. [27]
    P. Castillo, A. Dzul, and R. Lozano, “Real-time stabilization and tracking of a four-rotor mini rotorcraft,” IEEE Transactions on Control Systems Technology, vol. 12, no. 4, pp. 510–516, July 2004.CrossRefGoogle Scholar
  28. [28]
    I. Gonzalez, S. Salazar, and R. Lozano, “Chattering-free sliding mode altitude control for a quad-rotor aircraft: realtime application,” Journal of Intelligent & Robotic Systems, vol. 73, no. 1–4, pp.137–155, January 2014.CrossRefGoogle Scholar
  29. [29]
    L. Yang, B. Li, W. Li, Z. Liu, G. Yang, and J. Xiao, “Deep concrete inspection using unmanned aerial vehicle towards CSSC database,” Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.Google Scholar
  30. [30]
    C. Pan, X. Cao, and D. Wu, “Power line detection via background noise removal,” Proc. of the IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 871–875, 2016.Google Scholar
  31. [31]
    T. Tang, Z. Deng, S. Zhou, L. Lei, and H. Zou, “Fast vehicle detection in UAV images,” Proc. of the International Workshop on Remote Sensing with Intelligent Processing (RSIP), pp. 1–5, 2017.Google Scholar
  32. [32]
    J. S. Zhang, J. Cao, and B. Mao, “Application of deep learning and unmanned aerial vehicle technology in traffic flow monitoring,” Proc. of the International Conference on Machine Learning and Cybernetics (ICMLC), pp. 189–194, 2017.Google Scholar
  33. [33]
    Y. Ma, Y. Liu, R. Jin, X. Yuan, R. Sekha, S. Wilson, and R. Vaidyanathan, “Hand gesture recognition with convolutional neural networks for the multimodal UAV control,” Proc. of the Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS), pp. 198–203, 2017.Google Scholar
  34. [34]
    L. Zhang, F. Yang, Y. Daniel Zhang, and Y. J. Zhu, “Road crack detection using deep convolutional neural network,” Proc. of the IEEE International Conference on Image Processing (ICIP), pp. 3708–3712, 2016.Google Scholar
  35. [35]
    H. K. Khalil, Nonlinear Control, pp. 244, Pearson, 2014.Google Scholar
  36. [36]
    F. Chollet, Keras, 2015. GitHub,, Accessed: September 29, 2018.Google Scholar
  37. [37]
    F. Chollet, Keras: The Python Deep Learning library,, Accessed: September 29, 2018.Google Scholar

Copyright information

© ICROS, KIEE and Springer 2019

Authors and Affiliations

  • J. M. Vazquez-Nicolas
    • 1
    Email author
  • Erik Zamora
    • 2
  • Iván González-Hernández
    • 1
  • Rogelio Lozano
    • 3
  • Humberto Sossa
    • 2
    • 4
  1. 1.UMI-LAFMIA 3175 CNRS at CINVESTAV-IPNCiudad de MéxicoMéxico
  2. 2.Instituto Politécnico Nacional-CICCiudad de MéxicoMéxico
  3. 3.UTC-HEUDIASyCCentre de Recherches de RoyallieuCompiegneFrance
  4. 4.Tecnológico de MonterreyZapopanMéxico

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