Advertisement

Online Estimation of Impedance Parameters for a Variable Impedance Controlled Robotic Manipulator

  • Ajinkya Bhole
  • Fanny Ficuciello
  • Ahmad Mashayekhi
  • Salvatore StranoEmail author
  • Mario Terzo
  • Luigi Villani
  • Bruno Siciliano
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 68)

Abstract

The aim of this work is to estimate the impedance parameters, namely the damping and stiffness, of a variable impedance dynamic system. The estimation is performed using a Constrained Extended Kalman Filter (CEKF). Comparing the various non-linear estimation techniques, Extended Kalman Filter shows a superiority with respect to speed of execution. This is a major requirement in case the estimation is used for a task involving online-tuning of the parameters of a variable impedance controlled robotic manipulator in contact with a variable impedance dynamic environment, for example, during human-robot physical interaction. In order to have a ground truth, the algorithm was experimentally tested on a system with known variable impedance, namely, a variable impedance controlled KUKA LWR. For the estimation procedure, the position of the end-effector was used as the measurement and the external force applied on it as a known input. Without giving explicit information on the dynamics of the variable impedance parameters of the controlled manipulator, the CEKF appreciably tracked the real parameters. The performance of the estimator declines in case the impedance variation is highly non-linear.

References

  1. 1.
    Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)CrossRefGoogle Scholar
  2. 2.
    Sircoulomb, V., et al.: State estimation under nonlinear state inequality constraints: a tracking application. In: 2008 16th Mediterranean Conference on Control and Automation. IEEE (2008)Google Scholar
  3. 3.
    Bangjun, L.: Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB. Wiley, New York (2017)Google Scholar
  4. 4.
    Anderson, B.D., Moore, J.B.: Optimal Filtering, vol. 21, pp. 22–95. Englewood Cliffs, New Jersy (1979)zbMATHGoogle Scholar
  5. 5.
    Hogan, N.: Impedance control: an approach to manipulation. In: 1984 American Control Conference. IEEE (1984)Google Scholar
  6. 6.
    Fanny, F., Villani, L., Siciliano, B.: Variable impedance control of redundant manipulators for intuitive human-robot physical interaction. IEEE Trans. Robot. 31(4), 850–863 (2015)CrossRefGoogle Scholar
  7. 7.
    Khatib, O.: A unified approach for motion and force control of robot manipulators: the operational space formulation. IEEE J. Robot. Autom. 3(1), 43–53 (1987)CrossRefGoogle Scholar
  8. 8.
    Christian, O., Mukherjee, R., Nakamura, Y.: Unified impedance and admittance control. In: 2010 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ajinkya Bhole
    • 1
  • Fanny Ficuciello
    • 2
  • Ahmad Mashayekhi
    • 3
  • Salvatore Strano
    • 2
    Email author
  • Mario Terzo
    • 2
  • Luigi Villani
    • 2
  • Bruno Siciliano
    • 2
  1. 1.University of TwenteEnschedeThe Netherlands
  2. 2.University of Naples Federico IINaplesItaly
  3. 3.Isfahan University of TechnologyIsfahanIran

Personalised recommendations