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.

翻译：频率响应函数（FRF）的非参数估计通常适用于描述机械系统的动力学特性。若将这些估计视为测量输入，可用于灰盒模型等参数辨识。多输入多输出（MIMO）系统的经典非参数FRF估计方法至少需要与系统输入数量相等的实验次数。为减少实验次数，已开发出局部参数化FRF估计方法。本文对这些局部方法进行改进并应用于六轴机器人操作臂的FRF估计——这是一个在闭环条件下运行的非线性MIMO系统。研究目标在于缩短辨识所需的实验时间与数据量。通过实验研究分析所得FRF，并与经典MIMO技术获得的估计值进行对比。进一步研究表明，基于局部参数化FRF估计可实现精确的参数模型辨识，且总实验时间可显著缩短。