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Improved Control of DLO Transportation by a Team of Quadrotors

  • Julian Estevez
  • Manuel GrañaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)

Abstract

Quasi-stationary sections of a deformable linear object (DLO) hanging freely from two extreme points can be modeled either by catenaries or parabolic curves, depending on the conditions of the UAVs. DLO transportation is an instance of a leader-follower platoon team strategy, in which the local quadrotor control must cope with the dynamic perturbations due to the DLO linking the quadrotors. The quadrotor team control has two phases, one achieving a spatial configuration with equal energy consumption, the other is to manage the horizontal motion which is the transportation process per se. We propose a Model Reference Adaptive Control (MRAC) for the quadrotors team, which uses fuzzy modeling of the error in order to modulate the activation of the adaptation rules applied to proportional-derivative (PD) controller parameters, which are derived as error gradient descent rules. In this paper, we contribute the parabolic representation of the DLO and improved follow the leader control, testing the MRAC stability and robustness under a series of experiments.

Keywords

Proportional Integral Derivative Adaptation Rule Proportional Integral Derivative Control Model Reference Adaptive Control Follower Robot 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Argentim, L.M., Rezende, W.C., Santos, P.E., Aguiar, R.A.: PID, LQR and LQR-PID on a quadcopter platform. In: 2013 International Conference on Informatics, Electronics Vision (ICIEV), pp. 1–6, May 2013Google Scholar
  2. 2.
    Duro, R.J., Graña, M., Lope, J.: On the potential contributions of hybrid intelligent approaches to multicomponent robotic system development. Inf. Sci. 180(14), 2635–2648 (2010)CrossRefGoogle Scholar
  3. 3.
    Echegoyen, Z., Villaverde, I., Moreno, R., Graña, M., d’Anjou, A.: Linked multi-component mobile robots: modeling, simulation and control. Robot. Auton. Syst. 58(12), 1292–1305 (2010)CrossRefGoogle Scholar
  4. 4.
    Estevez, J., Graña, M.: Robust control tuning by PSO of aerial robots hose transportation. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo-Moreo, F.J., Adeli, H. (eds.) IWINAC 2015. LNCS, vol. 9108, pp. 291–300. Springer, Cham (2015). doi: 10.1007/978-3-319-18833-1_31 CrossRefGoogle Scholar
  5. 5.
    Estevez, J., Lopez-Guede, J.M., Graña, M.: Particle swarm optimization quadrotor control for cooperative aerial transportation of deformable linear objects. Cybern. Syst. 47(1–2), 4–16 (2016)CrossRefGoogle Scholar
  6. 6.
    Estevez, J., Lopez-Guede, J.M., Graña, M.: Quasi-stationary state transportation of a hose with quadrotors. Robot. Auton. Syst. 63(2), 187–194 (2015). Cognition-oriented advanced robotic systemsCrossRefGoogle Scholar
  7. 7.
    Lopez-Guede, J.M., Estevez, J., Graña, M.: Online fuzzy modulated adaptive PD control for cooperative aerial transportation of deformable linear objects. Integr. Comput.-Aided Eng. Preprint(Preprint), 1–15 (2016)Google Scholar
  8. 8.
    Fernandez-Gauna, B., Lopez-Guede, J.M., Graña, M.: Transfer learning with partially constrained models: application to reinforcement learning of linked multicomponent robot system control. Robot. Auton. Syst. 61(7), 694–703 (2013)CrossRefGoogle Scholar
  9. 9.
    Floreano, D., Wood, R.J.: Science, technology and the future of small autonomous drones. Nature 521(7553), 460–466 (2015)CrossRefGoogle Scholar
  10. 10.
    Hsu, Y., Pan, C.: The static WKB solution to catenary problems with large sag and bending stiffness. Math. Probl. Eng. 2014 (2014)Google Scholar
  11. 11.
    Larsen, L., Pham, V.L., Kim, J., Kupke, M.: Collision-free path planning of industrial cooperating robots for aircraft fuselage production. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 2042–2047, May 2015Google Scholar
  12. 12.
    Lopez-Guede, J.M., Fernandez-Gauna, B., Graña, M.: State-action value modeled by ELM in reinforcement learning for hose control problems. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 21(supp02), 99–116 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lopez-Guede, J.M., Graña, M., Ramos-Hernanz, J.A., Oterino, F.: A neural network approximation of L-MCRS Dynamics for reinforcement learning experiments. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., Paz López, F., Toledo Moreo, F.J. (eds.) IWINAC 2013. LNCS, vol. 7931, pp. 317–325. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38622-0_33 CrossRefGoogle Scholar
  14. 14.
    Nguyen, D.Q., Gouttefarde, M., Company, O., Pierrot, F.: On the simplifications of cable model in static analysis of large-dimension cable-driven parallel robots. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 928–934, November 2013Google Scholar
  15. 15.
    Pruner, E., Necsulescu, D., Sasiadek, J., Kim, B.: Control of decentralized geometric formations of mobile robots. In: 2012 17th International Conference on Methods Models in Automation Robotics (MMAR), pp. 627–632, August 2012Google Scholar
  16. 16.
    Pruner,E.: Control of self-organizing and geometric formations. Ph.D. thesis. Université d’Ottawa/University of Ottawa (2014)Google Scholar
  17. 17.
    Seraji, H.: Decentralized adaptive control of manipulators: theory, simulation, and experimentation. IEEE Trans. Robot. Autom. 5(2), 183–201 (1989)CrossRefGoogle Scholar
  18. 18.
    Su, Y., Qiu, Y., Liu, P.: Optimal cable tension distribution of the high-speed redundant driven camera robots considering cable sag and inertia effects. Adv. Mech. Eng. 6 (2014)Google Scholar
  19. 19.
    Wei, K., Zhang, L.X., Ren, A.D.: The analysis method of highline cable of alongside replenishment system based on suspended cable theory. In: Advanced Materials Research, vol. 490, pp. 633–637. Trans Tech Publications (2012)Google Scholar
  20. 20.
    Wen, N., Zhao, L., Xiaohong, S., Ma, P.: Uav online path planning algorithm in a low altitude dangerous environment. IEEE/CAA J. Autom. Sin. 2(2), 173–185 (2015)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Yao, R., Tang, X., Wang, J., Huang, P.: Dimensional optimization design of the four-cable-driven parallel manipulator in fast. IEEE/ASME Trans. Mechatron. 15(6), 932–941 (2010)Google Scholar
  22. 22.
    Yeh, F.-K.: Attitude controller design of mini-unmanned aerial vehicles using fuzzy sliding-mode control degraded by white noise interference. Control Theory Applications 6(9), 1205–1212 (2012). IETMathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computational Intelligence GroupUniversity of the Basque Country, UPV/EHUSan SebastianSpain

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