Advertisement

A Fast Learning Control Strategy for Unmanned Aerial Manipulators

  • 294 Accesses

  • 3 Citations

Abstract

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.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 199

This is the net price. Taxes to be calculated in checkout.

References

  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)

  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)

  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)

  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)

  5. 5.

    Bohn, C., Atherton, D.P.: An analysis package comparing pid anti-windup strategies. IEEE Control. Syst. 15(2), 34–40 (1995)

  6. 6.

    Bouabdallah, S.: Design and control of quadrotors with application to autonomous flying. Ph.D. thesis, Ecole Polytechnique Federale de Lausanne (2007)

  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 Congress

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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 Control

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  36. 36.

    Lippiello, V., Ruggiero, F.: Cartesian impedance control of a uav with a robotic arm. IFAC Proceedings 45(22), 704–709 (2012)

  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)

  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)

  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)

  40. 40.

    Mendel, J.M.: Computing derivatives in interval type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 12(1), 84–98 (2004)

  41. 41.

    Mendel, J.M., John, R.I.B.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002)

  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)

  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)

  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)

  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)

  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)

  47. 47.

    Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics: modelling, planning and control. 1st edn., Springer Publishing Company, Incorporated (2008)

  48. 48.

    Spong, M.W., Hutchinson, S., Vidyasagar, M.: Robot modeling and control, vol. 3. Wiley, New York (2006)

  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)

  50. 50.

    Tavoosi, J., Suratgar, A.A., Menhaj, M.B.: Stable anfis2 for nonlinear system identification. Neurocomputing 182, 235–246 (2016)

  51. 51.

    Valavanis, K.P., Vachtsevanos, G.J. Valavanis, K.P., Vachtsevanos, G.J. (eds.): Uav Control: introduction. Springer, Netherlands (2015)

  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)

Download references

Acknowledgements

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).

Author information

Correspondence to Erdal Kayacan.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

(MP4 23.5 MB)

(MP4 23.5 MB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Imanberdiyev, N., Kayacan, E. A Fast Learning Control Strategy for Unmanned Aerial Manipulators. J Intell Robot Syst 94, 805–824 (2019). https://doi.org/10.1007/s10846-018-0884-7

Download citation

Keywords

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