Nonlinear gray-box identification using local models applied to industrial robots
Nonlinear gray-box identification using local models applied to industrial robots
复制标题
使用局部模型的非线性灰盒识别应用于工业机器人
DOI：
10.1016/j.automatica.2011.01.021
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
S. Moberg
中科院分区：
文献类型：
--
作者：
Erik Wernholt;S. Moberg
本文研究了非线性灰箱模型中未知参数的估计问题，该模型可能同时是多变量、非线性、不稳定和共振的。直接使用时域预测误差方法来解决这类问题很容易导致大型和数值刚性的优化问题。因此，我们提出了一个识别过程，使用中间的本地模型，允许数据压缩和一个不太复杂的优化问题。该过程基于对多个工作点的非参数频率响应函数（FRF）的估计。非线性灰箱模型在相同的工作点被线性化，从而得到参数化的FRF。最后通过最小化非参数和参数频响函数之间的差异来获得最优参数。通过估计六轴工业机器人的弹性参数说明了该过程。不同的参数估计进行了比较，实验结果表明，所提出的识别程序的实用性。加权对数最小二乘估计器实现了最佳的结果，所识别的模型给出了一个很好的全局描述的动态在感兴趣的频率范围内的机器人控制。
In this paper, we study the problem of estimating unknown parameters in nonlinear gray-box models that may be multivariable, nonlinear, unstable, and resonant at the same time. A straightforward use of time-domain predication-error methods for this type of problem easily ends up in a large and numerically stiff optimization problem. We therefore propose an identification procedure that uses intermediate local models that allow for data compression and a less complex optimization problem. The procedure is based on the estimation of the nonparametric frequency response function (FRF) in a number of operating points. The nonlinear gray-box model is linearized in the same operating points, resulting in parametric FRFs. The optimal parameters are finally obtained by minimizing the discrepancy between the nonparametric and parametric FRFs. The procedure is illustrated by estimating elasticity parameters in a six-axis industrial robot. Different parameter estimators are compared and experimental results show the usefulness of the proposed identification procedure. The weighted logarithmic least squares estimator achieves the best result and the identified model gives a good global description of the dynamics in the frequency range of interest for robot control.
影响因子：
5.2
作者：
M. Spong;S. Hutchinson;M. Vidyasagar
通讯作者：
M. Spong;S. Hutchinson;M. Vidyasagar