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

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Abstract

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.

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References

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. Newspaper “La Razon”, “Damaged building in the earthquake in Mexico City,” La Razon, 12 July, 2018. www.razon.com.mx/, Accessed: September 1, 2018.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  26. R. Lozano, Unmanned Aerial Vehicles: Embedded Control, Wiley, Hoboken, NJ, 2013.

    Book  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. 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. 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. 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. 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. H. K. Khalil, Nonlinear Control, pp. 244, Pearson, 2014.

    Google Scholar 

  36. F. Chollet, Keras, 2015. GitHub, https://github.com/kerasteam/keras, Accessed: September 29, 2018.

    Google Scholar 

  37. F. Chollet, Keras: The Python Deep Learning library, https://github.com/keras-team/keras, Accessed: September 29, 2018.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. UMI-LAFMIA 3175 CNRS at CINVESTAV-IPN, Av. IPN 2508, San Pedro Zacatenco, 07360, Ciudad de México, México

    J. M. Vazquez-Nicolas & Iván González-Hernández

  2. Instituto Politécnico Nacional-CIC, Av. Juan de Dios Batiz S/N, Gustavo A. Madero, 07738, Ciudad de México, México

    Erik Zamora & Humberto Sossa

  3. UTC-HEUDIASyC, Centre de Recherches de Royallieu, 60205, Compiegne, France

    Rogelio Lozano

  4. Tecnológico de Monterrey, Campus Guadalajara, Av. Gral. Ramón Corona, 2514, Zapopan, Jalisco, 45138, México

    Humberto Sossa

Authors
  1. J. M. Vazquez-Nicolas
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  2. Erik Zamora
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  3. Iván González-Hernández
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  4. Rogelio Lozano
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  5. Humberto Sossa
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Corresponding author

Correspondence to J. M. Vazquez-Nicolas.

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Recommended by Associate Editor Ning Sun under the direction of Editor Chan Gook Park. This work was economically supported by SIP-IPN (grant numbers 20180180, 20180730, 20190007, and 20190166) and CONACYT (grant number 65). Iván González-Hernández and R. Lozano acknowledges UMI-LAFMIA 3175 CNRS, CINVESTAV-IPN, and the research project “Desarrollo de vehículos aéreos y submarinos para aplicaciones de inspección en plantas hidroeléctricas utilizando navegación relativa” which is funded under the 2018 call of the SEP-Cinvestav Fund. J. M. Vázquez-Nicolás acknowledges UMI-LAFMIA 3175 CNRS, CINVESTAV-IPN and CONACYT for the scholarship granted towards pursuing his Ph.D. studies.

J. M. Vázquez-Nicolás was born in Mexico City, on January 6, 1990. He received his B.S. degree in Mechatronics and M.S. in Computer Engineering from National Polytechnic Institute of Mexico, in 2013 and 2016, respectively. He is currently working toward a Ph.D. degree at CINVESTAV-IPN. His current research interests include the area of dynamics and control of UAV, artificial intelligence and robotics systems.

Erik Zamora is a full professor with the National Polytechnic Institute of Mexico. He received his Diploma in Electronics from UV (2004), his Master’s degree in Electrical Engineering from CINVESTAV (2007), and his PhD in automatic control from CINVESTAV (2015). He developed the first commercial Mexican myoelectric system to control a prosthesis in the Pro/Bionics company and a robotic navigation system for unknown environments guided by emergency signals at the University of Bristol. He had a postdoctoral position in CIC-IPN for two years (2016-2017). His current interests include autonomous robots and machine learning. He has published 18 technical papers in international conference proceedings and journals and has directed 19 theses on these topics.

Iván González-Hernández was born in Mexico City, on March 18, 1981. He received an engineering degree in Communications and Electronics from the Instituto Politécnico Nacional, Mexico City, in 2003, and his M.Sc. and Ph.D. studies in Automatic Control at the Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City, in 2009 and 2013, respectively. At present, he works as research professor in the UMI-LAFMIA 3175 CNRS laboratory by agreement called Catédra-CONACYT, where his current research interests include real-time robust control applications, sliding mode control techniques and embedded control systems in Unmanned Aerial Vehicles (UAV) in particular the Quad-rotor aircraft configuration.

Rogelio Lozano was born in Monterrey Mexico, on July 12, 1954. He received his B.S. degree in Electronic Engineering from the National Polytechnic Institute (IPN) of Mexico in 1975, an M.S. degree in Electrical Engineering from CINVESTAV-IPN, Mexico in 1977, and a Ph.D. degree in Automatic Control from LAG, INPG, France, in 1981. He joined the Department of Electrical Engineering at the CINVESTAV, Mexico, in 1981 where he worked until 1989 and is a CNRS Research Director since 1990. Since April 2008 he is the head of the UMI 3175 LAFMIA at CINVESTAV Mexico, which is a joint research laboratory founded by CNRS, CINVESTAV and CONACYT. He is author or co-author of 86 journal papers, 164 conference presentations and 5 Springer-Verlag books in the areas of control and observers for non linear dynamical systems, adaptive control, passive systems, modeling and control of small UAV and localization of UAV using vision systems or radio signals.

Humberto Sossa was born in Guadalajara, Jalisco, Mexico in 1956. He received his B.Sc. degree in Electronics from the University of Guadalajara in 1981, an M.Sc. degree in Electrical Engineering from CINVESTAV-IPN in 1987, and a Ph.D. degree in Informatics from the National Polytechnic Institute of Grenoble, France in 1992. He is a full time professor at the Centre for Computing Research of the National Polytechnic Institute of Mexico. He is also the Head of the Robotics and Mechatronics Laboratory. His main research interests are in Artificial Intelligence, Machine Learning and Robotics.

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Vazquez-Nicolas, J.M., Zamora, E., González-Hernández, I. et al. PD+SMC Quadrotor Control for Altitude and Crack Recognition Using Deep Learning. Int. J. Control Autom. Syst. 18, 834–844 (2020). https://doi.org/10.1007/s12555-018-0852-9

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