Skip to main content
×
Home
    • Aa
    • Aa

Experimental parameter identification of flexible joint robot manipulators

  • Roger Miranda-Colorado (a1) and Javier Moreno-Valenzuela (a2)
Summary
SUMMARY

This paper contributes by presenting a parameter identification procedure for n-degrees-of-freedom flexible joint robot manipulators. An advantage of the given procedure is the obtaining of robot parameters in a single experiment. Guidelines are provided for the computing of the joint position filtering and velocity estimation. The method relies in the filtered robot model, for which no acceleration measurements are required. The filtered model is expressed in regressor form, which allows applying a parameter identification procedure based on the least squares algorithm. In order to assess the performance of the proposed parameter identification scheme, an implementation of a least squares with forgetting factor (LSFF) parameter identification method is carried out. In order to assess the reliability of the tested identification schemes, a model-based trajectory tracking controller has been implemented twice in different conditions: one control experiment using the estimated parameters provided by the proposed scheme, and another experiment using the parameters given by the LSFF method. These real-time control experiments are compared with respect to numerical simulations using the estimated parameters for each identification method. For the proposed scheme, the comparison between experiments and numerical simulations indicates better accuracy in the torque and position prediction.

Copyright
Corresponding author
*Corresponding author. E-mail: moreno@citedi.mx
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Robotica
  • ISSN: 0263-5747
  • EISSN: 1469-8668
  • URL: /core/journals/robotica
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 73 *
Loading metrics...

Abstract views

Total abstract views: 278 *
Loading metrics...

* Views captured on Cambridge Core between 29th May 2017 - 20th September 2017. This data will be updated every 24 hours.