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Experimental parameter identification of flexible joint robot manipulators

Published online by Cambridge University Press:  29 May 2017

Roger Miranda-Colorado
Affiliation:
CONACyT-Instituto Politécnico Nacional-CITEDI, Av. Instituto Politécnico Nacional No. 1310, Nueva Tijuana, Tijuana, Baja California, 22435, México. email: rmirandaco@gmail.com
Javier Moreno-Valenzuela*
Affiliation:
Instituto Politécnico Nacional-CITEDI, Av. Instituto Politécnico Nacional No. 1310, Nueva Tijuana, Tijuana, Baja California, 22435, México
*
*Corresponding author. E-mail: moreno@citedi.mx

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.

Type
Articles
Copyright
Copyright © Cambridge University Press 2017 

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