A novel LLSDPso method for nonlinear dynamic parameter identification
Abstract
Purpose
This paper aims to propose a novel method to identify the parameters of robotic manipulators using the torque exerted by the robot joint motors (measured by current sensors).
Design/methodology/approach
Previous studies used additional sensors like force sensor and inertia measurement unit, or additional payload mounted on the end-effector to perform parameter identification. The settings of these previous works were complicated. They could only identify part of the parameters. This paper uses the torque exerted by each joint while performing Fourier periodic excited trajectories. It divides the parameters into a linear part and a non-linear part, and uses linear least square (LLS) parameter estimation and dual-swarm-based particle swarm optimization (DPso) to compute the linear and non-linear parts, respectively.
Findings
The settings are simpler and can identify the dynamic parameters, the viscous friction coefficients and the Coulomb friction coefficients of two joints at the same time. A SIASUN 7-Axis Flexible Robot is used to experimentally validate the proposal. Comparison between the predicted torque values and ground-truth values of the joints confirms the effectiveness of the method.
Originality/value
The proposed method identifies two joints at the same time with satisfying precision and high efficiency. The identification errors of joints do not accumulate.
Keywords
Citation
Yuan, J.-j., Wan, W., Fu, X., Wang, S. and Wang, N. (2017), "A novel LLSDPso method for nonlinear dynamic parameter identification", Assembly Automation, Vol. 37 No. 4, pp. 490-498. https://doi.org/10.1108/AA-08-2016-106
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited