A Fast Learning Control Strategy for Unmanned Aerial Manipulators

  • Nursultan Imanberdiyev
  • Erdal KayacanEmail author


We present an artificial intelligence-based control approach, the fusion of artificial neural networks and type-2 fuzzy logic controllers, namely type-2 fuzzy-neural networks, for the outer adaptive position controller of unmanned aerial manipulators. The performance comparison of proportional-derivative (PD) controller working alone and the proposed intelligent control structures working in parallel with a PD controller is presented. The simulation and real-time results show that the proposed online adaptation laws eliminate the need for precise tuning of conventional controllers by learning system dynamics and disturbances online. The proposed approach is also computationally inexpensive due to the implementation of the fast sliding mode control theory-based learning algorithm which does not require matrix inversions or partial derivatives. Both simulation and experimental results have shown that the proposed artificial intelligence-based learning controller is capable of reducing the root-mean-square error by around 50% over conventional PD and PID controllers.


Fuzzy neural networks Type-2 fuzzy logic control Sliding mode control Unmanned aerial vehicle Aerial manipulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The research was partially supported by the ST Engineering - NTU Corporate Lab through the NRF corporate lab@university scheme, and Aarhus University, Department of Engineering (28173).

Supplementary material

(MP4 23.5 MB)


  1. 1.
    Acosta, J.A., Sanchez, M.I., Ollero, A.: Robust control of underactuated aerial manipulators via IDA-PBC. In: 53Rd IEEE conference on decision and control, CDC 2014, pp. 673–678. Los Angeles, December 15-17, 2014 (2014)Google Scholar
  2. 2.
    Arleo, G., Caccavale, F., Muscio, G., Pierri, F.: Control of quadrotor aerial vehicles equipped with a robotic arm. In: 21St mediterranean conference on control and automation, pp. 1174–1180. Platanias, June 25-28, 2013 (2013)Google Scholar
  3. 3.
    Biglarbegian, M., Melek, W.W., Mendel, J.M.: On the stability of interval type-2 tsk fuzzy logic control systems. IEEE Trans. Syst. Man Cybern. Part B Cybern. 40(3), 798–818 (2010)CrossRefGoogle Scholar
  4. 4.
    Birkin, P.A.S., Garibaldi, J.M.: A comparison of type-1 and type-2 fuzzy controllers in a micro-robot context. In: 2009 IEEE international conference on fuzzy systems, pp. 1857–1862. Jeju Isl, August 20-24, 2009 (2009)Google Scholar
  5. 5.
    Bohn, C., Atherton, D.P.: An analysis package comparing pid anti-windup strategies. IEEE Control. Syst. 15(2), 34–40 (1995)CrossRefGoogle Scholar
  6. 6.
    Bouabdallah, S.: Design and control of quadrotors with application to autonomous flying. Ph.D. thesis, Ecole Polytechnique Federale de Lausanne (2007)Google Scholar
  7. 7.
    Caccavale, F., Giglio, G., Muscio, G., Pierri, F.: Adaptive control for uavs equipped with a robotic arm. IFAC Proceedings Volumes 47(3), 11,049–11,054 (2014). 19th IFAC World CongressCrossRefGoogle Scholar
  8. 8.
    Capitan, J., Merino, L., Ollero, A.: Cooperative decision-making under uncertainties for multi-target surveillance with multiples uavs. J. Intell. Robot. Syst. 84(1), 371–386 (2016)CrossRefGoogle Scholar
  9. 9.
    Castillo, O., Melin, P.: Overview of genetic algorithms applied in the optimization of type-2 fuzzy systems. In: Recent advances in interval type-2 fuzzy systems, vol. 1, pp 19–25. Springer, Berlin (2012)Google Scholar
  10. 10.
    Celikyilmaz, A., Türksen, I.B.: Modeling uncertainty with improved fuzzy functions. In: Modeling uncertainty with fuzzy logic: with recent theory and applications. 1st edn., pp 149–215. Springer, Berlin (2009)Google Scholar
  11. 11.
    Cervantes, L., Castillo, O.: Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control. Inform. Sci. 324, 247–256 (2015)CrossRefGoogle Scholar
  12. 12.
    Dong, X., Zhao, Y., Karimi, H.R., Shi, P.: Adaptive variable structure fuzzy neural identification and control for a class of mimo nonlinear system. J. Franklin Inst. 350(5), 1221–1247 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Fu, C., Olivares-Mendez, M.A., Suarez-Fernandez, R., Campoy, P.: Monocular visual-inertial slam-based collision avoidance strategy for fail-safe uav using fuzzy logic controllers. J. Intell. Robot. Syst. 73(1), 513–533 (2014)CrossRefGoogle Scholar
  14. 14.
    Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R., Garibaldi, J.M.: A comparative study on the control of quadcopter Uavs by using singleton and non-singleton fuzzy logic controllers. In: 2016 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp. 1023–1030. Vancouver, July 24-29, 2016 (2016)Google Scholar
  15. 15.
    Garimella, G., Kobilarov, M.: Towards model-predictive control for aerial pick-and-place. In: 2015 IEEE international conference on robotics and automation (ICRA), pp. 4692–4697. Seattle, May 26-30, 2015 (2015)Google Scholar
  16. 16.
    Gomi, H., Kawato, M.: Neural network control for a closed-loop system using feedback-error-learning. Neural Netw. 6(7), 933–946 (1993)CrossRefGoogle Scholar
  17. 17.
    Hagras, H.A.: A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans. Fuzzy Syst. 12(4), 524–539 (2004)CrossRefGoogle Scholar
  18. 18.
    Huang, T., Javaherian, H., Liu, D.: Nonlinear torque and air-to-fuel ratio control of spark ignition engines using neuro-sliding mode techniques. Int. J. Neural Syst. 21(03), 213–224 (2011)CrossRefGoogle Scholar
  19. 19.
    Jimenez-Cano, A.E., Martin, J., Heredia, G., Ollero, A., Cano, R.: Control of an aerial robot with multi-link arm for assembly tasks. In: 2013 IEEE international conference on robotics and automation (ICRA), pp. 4916–4921. Karlsruhe, May 06-10, 2013 (2013)Google Scholar
  20. 20.
    Kawato, M., Uno, Y., Isobe, M., Suzuki, R.: Hierarchical neural network model for voluntary movement with application to robotics. IEEE Control. Syst. Mag. 8(2), 8–15 (1988)CrossRefGoogle Scholar
  21. 21.
    Kayacan, E., Kayacan, E., Chen, I.M., Ramon, H., Saeys, W.: On the comparison of model-based and model-free controllers in guidance, navigation and control of agricultural vehicles. In: John, R., Hagras, H., Castillo, O. (eds.) Type-2 fuzzy logic and systems: Dedicated to Professor Jerry Mendel for his pioneering contribution, pp 49–73. Springer International Publishing, Cham (2018)Google Scholar
  22. 22.
    Kayacan, E., Kayacan, E., Ramon, H., Saeys, W.: Neuro-fuzzy control with a novel training method based-on sliding mode control theory: Application to tractor dynamics. IFAC Proceedings Volumes 45(22), 889–894 (2012). 10th IFAC Symposium on Robot ControlCrossRefGoogle Scholar
  23. 23.
    Kayacan, E., Kayacan, E., Ramon, H., Saeys, W.: Adaptive neuro-fuzzy control of a spherical rolling robot using sliding-mode-control-theory-based online learning algorithm. IEEE Transactions on Cybernetics 43(1), 170–179 (2013)CrossRefGoogle Scholar
  24. 24.
    Kayacan, E., Kaynak, O., Abiyev, R., Torresen, J., Hovin, M., Glette, K.: Design of an adaptive interval type-2 fuzzy logic controller for the position control of a servo system with an intelligent sensor. In: international conference on fuzzy systems, pp. 1–8. Barcelona, July 18–23, 2010 (2010)Google Scholar
  25. 25.
    Kayacan, E., Khanesar, M.A.: Fuzzy neural networks for real time control applications: concepts, modeling and algorithms for fast learning, pp 105–130. Heinemann, Butterworth (2015)Google Scholar
  26. 26.
    Kayacan, E., Maslim, R.: Type-2 fuzzy logic trajectory tracking control of quadrotor vtol aircraft with elliptic membership functions. IEEE/ASME Trans. Mechatron. 22(1), 339–348 (2017)CrossRefGoogle Scholar
  27. 27.
    Kayacan, E., Peschel, J.M., Chowdhary, G.: A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme. Eng. Appl. Artif. Intel. 62, 276–285 (2017)CrossRefGoogle Scholar
  28. 28.
    Kayacan, E., Saeys, W., Kayacan, E., Ramon, H., Kaynak, O.: Intelligent control of a tractor-implement system using type-2 fuzzy neural networks. In: 2012 IEEE international conference on fuzzy systems, pp. 1–8. Brisbane, June 10-15, 2012 (2012)Google Scholar
  29. 29.
    Khanesar, M.A., Kayacan, E., Reyhanoglu, M., Kaynak, O.: Feedback error learning control of magnetic satellites using type-2 fuzzy neural networks with elliptic membership functions. IEEE Transactions on Cybernetics 45(4), 858–868 (2015)CrossRefGoogle Scholar
  30. 30.
    Khanesar, M.A., Kayacan, E., Teshnehlab, M., Kaynak, O.: Levenberg marquardt algorithm for the training of type-2 fuzzy neuro systems with a novel type-2 fuzzy membership function. In: 2011 IEEE symposium on advances in type-2 fuzzy logic systems (T2FUZZ), pp. 88–93. Paris, April 11-15, 2011 (2011)Google Scholar
  31. 31.
    Kim, S., Seo, H., Choi, S., Kim, H.J.: Vision-guided aerial manipulation using a multirotor with a robotic arm. IEEE/ASME Trans. Mechatron. 21(4), 1912–1923 (2016)CrossRefGoogle Scholar
  32. 32.
    Korpela, C., Orsag, M., Pekala, M., Oh, P.: Dynamic stability of a mobile manipulating unmanned aerial vehicle. In: 2013 IEEE international conference on robotics and automation (ICRA), pp. 4922–4927. Karlsruhe, May 06-10, 2013 (2013)Google Scholar
  33. 33.
    Lee, T., Leok, M., McClamroch, N.H.: Nonlinear robust tracking control of a quadrotor uav on se(3). In: 2012 American control conference (ACC), pp. 4649–4654. Montreal, June 27-29, 2012 (2012)Google Scholar
  34. 34.
    Li, B., Zhou, W., Sun, J., Wen, C., Chen, C.: Model Predictive Control for Path Tracking of a Vtol Tailsitter Uav in an Hil Simulation Environment. In: 2018 AIAA modeling and simulation technologies conference, p. 1919. Kissimmee, January 8–12, 2018 (2018)Google Scholar
  35. 35.
    Lin, F.J., Hung, Y.C., Ruan, K.C.: An intelligent second-order sliding-mode control for an electric power steering system using a wavelet fuzzy neural network. IEEE Trans. Fuzzy Syst. 22(6), 1598–1611 (2014)CrossRefGoogle Scholar
  36. 36.
    Lippiello, V., Ruggiero, F.: Cartesian impedance control of a uav with a robotic arm. IFAC Proceedings 45(22), 704–709 (2012)CrossRefGoogle Scholar
  37. 37.
    Maza, I., Caballero, F., Capitán, J., Martínez-de Dios, J.R., Ollero, A.: Experimental results in multi-uav coordination for disaster management and civil security applications. J. Intell. Robot. Syst. 61(1), 563–585 (2011)CrossRefGoogle Scholar
  38. 38.
    Melendez, A., Castillo, O.: Optimization of type-2 fuzzy reactive controllers for an autonomous mobile robot. In: 2012 Fourth world congress on nature and biologically inspired computing (NaBIC), pp. 207–211. Mexico City, November 05-09, 2012 (2012)Google Scholar
  39. 39.
    Mendel, J., Hagras, H., Tan, W.W., Melek, W.W., Ying, H.: Introduction to type-2 fuzzy logic control: theory and applications. Wiley, Hoboken (2014)CrossRefzbMATHGoogle Scholar
  40. 40.
    Mendel, J.M.: Computing derivatives in interval type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 12(1), 84–98 (2004)MathSciNetCrossRefGoogle Scholar
  41. 41.
    Mendel, J.M., John, R.I.B.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002)CrossRefGoogle Scholar
  42. 42.
    Muscio, G., Pierri, F., Trujillo, M.A., Cataldi, E., Giglio, G., Antonelli, G., Caccavale, F., Viguria, A., Chiaverini, S., Ollero, A.: Experiments on coordinated motion of aerial robotic manipulators. In: 2016 IEEE international conference on robotics and automation (ICRA), pp. 1224–1229. Stockholm, May 16-21, 2016 (2016)Google Scholar
  43. 43.
    Qi, J., Song, D., Shang, H., Wang, N., Hua, C., Wu, C., Qi, X., Han, J.: Search and rescue rotary-wing uav and its application to the lushan ms 7.0 earthquake. Journal of Field Robotics (2015)Google Scholar
  44. 44.
    Ruggiero, F., Trujillo, M.A., Cano, R., Ascorbe, H., Viguria, A., Peréz, C., Lippiello, V., Ollero, A., Siciliano, B.: A multilayer control for multirotor uavs equipped with a servo robot arm. In: 2015 IEEE international conference on robotics and automation (ICRA), pp. 4014–4020. Seattle, May 26-30, 2015 (2015)Google Scholar
  45. 45.
    Sanchez, M.A., Castillo, O., Castro, J.R.: Generalized Type-2 Fuzzy Systems for controlling a mobile robot and a performance comparison with Interval Type-2 and Type-1 Fuzzy Systems. Expert Syst. Appl. 42(14), 5904–5914 (2015)CrossRefGoogle Scholar
  46. 46.
    Sarabakha, A., Imanberdiyev, N., Kayacan, E., Khanesar, M.A., Hagras, H.: Novel levenberg–marquardt based learning algorithm for unmanned aerial vehicles. Inform. Sci. 417, 361–380 (2017)CrossRefGoogle Scholar
  47. 47.
    Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics: modelling, planning and control. 1st edn., Springer Publishing Company, Incorporated (2008)Google Scholar
  48. 48.
    Spong, M.W., Hutchinson, S., Vidyasagar, M.: Robot modeling and control, vol. 3. Wiley, New York (2006)Google Scholar
  49. 49.
    Tai, K., El-Sayed, A.R., Biglarbegian, M., Gonzalez, C.I., Castillo, O., Mahmud, S.: Review of recent type-2 fuzzy controller applications. Algorithms 9(2), 1–19 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  50. 50.
    Tavoosi, J., Suratgar, A.A., Menhaj, M.B.: Stable anfis2 for nonlinear system identification. Neurocomputing 182, 235–246 (2016)CrossRefGoogle Scholar
  51. 51.
    Valavanis, K.P., Vachtsevanos, G.J. Valavanis, K.P., Vachtsevanos, G.J. (eds.): Uav Control: introduction. Springer, Netherlands (2015)Google Scholar
  52. 52.
    Wai, R.J., Muthusamy, R.: Fuzzy-neural-network inherited sliding-mode control for robot manipulator including actuator dynamics. IEEE Transactions on Neural Networks and Learning Systems 24(2), 274–287 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.ST Engineering-NTU Corp LaboratorySingaporeSingapore
  3. 3.Department of EngineeringAarhus UniversityAarhusDenmark

Personalised recommendations