Detection and Estimation of Nonlinear Distortions in Industrial Robots

System identification in robotics often involves the estimation of linear models characterizing the behavior in certain operating points. In this paper, a method for the detection and estimation of nonlinear distortions in an estimated frequency response function (FRF) has successfully been applied to experimental data from an industrial robot. The results show that nonlinear distortions are indeed present and cause larger variability in the FRF than the measurement noise contributions