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Parametric Neurocontroller for Positioning of an Anthropomorfic Finger Based on an Oponent Driven-Tendon Transmission System

  • J. I. Mulero
  • J. Feliú Batlle
  • J. López Coronado
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)

Abstract

An anthropomorfic finger with a transmission system based on tendons has been proposed. This system is able to work in an agonist/antagonist mode. The main problem to control tendons proceeds from the different dimensions between the joint and tendon spaces. In order to solve this problem we propose a position controller that provides motor torques instead of joint torques as proposed in the literature. This position controller is built as a parametric neural network by using of basis functions obtained from the finger structure. This controller insure that the tracking error converges to zero and the weights of the network are bounded. Both control and weight updating has been designed by means of a Lyapunov energy function. In order to improve the computational efficient of the neural network, this has been split up into subnets to compensate inertial, coriolis/centrifugal and gravitational effects.

Keywords

Tracking Error Transmission System Joint Torque Motor Torque Position Controller 
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.

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References

  1. 1.
    Jacobsen, S.C., Ko, H., Iversen E.K., Davis, C.C.: Antagonist control of a tendon driven manipulator, IEEE Proc.Int. Conference on Robotics and Automation, (1989), 1334–1339.Google Scholar
  2. 2.
    Jacobsen, S.C.: The Utah/MIT hand: Work in Progress. IEEE Journal of Robotics and Automation, 3(4), (1984)Google Scholar
  3. 3.
    Lee, J.J., Tsai, L.W.: Torque Resolver Design for Tendon-Drive Manipulators, Technical Research Report. University of Maryland. TR 91-52. (1991)Google Scholar
  4. 4.
    Lee, J.J., Tsai, L.W.: Dynamic Simulation of Tendon-Driven Manipulators, Technical Research Report. University of Maryland. TR 91-53. (1991)Google Scholar
  5. 5.
    Lee, J.J.: Tendon-Driven Manipulators:Analysis, Synthesis, and Control, Thesis Report. Harvard University. (1991)Google Scholar
  6. 6.
    Tsai, L.W.: Design of Tendon-Driven Manipulators. Technical Research Report. University of Maryland. TR 95-96. (1995)Google Scholar
  7. 7.
    Li, W. and Slotine, J-J.E.. Applied Non-Linear Control. Prentice Hall, (1991)Google Scholar
  8. 8.
    Lewis, F.L., Jagannathan, S., Yesildirek, A.: Neural Network Control of Robot Manipulators and Nonlinear Systems. Taylor & Francis, (1999)Google Scholar
  9. 9.
    Ge, S.S., Lee, T.H. and Harris, C.J.: Adaptive Neural Network Control of Robotic Manipulators. World Scientific Series in Robotics and Inteligent Systems-Vol. 19. (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • J. I. Mulero
    • 1
  • J. Feliú Batlle
    • 1
  • J. López Coronado
    • 1
  1. 1.Departamento Ingeniería de Sistemas y AutomáticaUniversidad Politécnica de CartagenaMurciaSpain

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