Experimental Evaluation of Methods for Estimating Frequency Response Functions of a 6-axes Robot

TL;DR

This paper evaluates local parametric methods for estimating frequency response functions of a 6-axes robot, demonstrating reduced experiment time and data requirements while maintaining accurate system modeling.

Contribution

It adapts and applies local parametric FRF estimation methods to a nonlinear MIMO robotic system, improving efficiency over classical techniques.

Findings

Local methods reduce experiment time significantly.

Accurate parametric models can be derived from fewer data.

Comparison shows advantages over classical MIMO FRF estimation.

Abstract

Nonparametric estimates of frequency response functions (FRFs) are often suitable for describing the dynamics of a mechanical system. If treating these estimates as measurement inputs, they can be used for parametric identification of, e.g., a gray-box model. Classical methods for nonparametric FRF estimation of MIMO systems require at least as many experiments as the system has inputs. Local parametric FRF estimation methods have been developed for avoiding multiple experiments. In this paper, these local methods are adapted and applied for estimating the FRFs of a 6-axes robotic manipulator, which is a nonlinear MIMO system operating in closed loop. The aim is to reduce the experiment time and amount of data needed for identification. The resulting FRFs are analyzed in an experimental study and compared to estimates obtained by classical MIMO techniques. It is furthermore shown that an accurate parametric model identification is possible based on local parametric FRF estimates and that the total experiment time can be significantly reduced.