Inertial parameter identification including friction and motor dynamics
Inertial parameter identification including friction and motor dynamics
复制标题
惯性参数识别，包括摩擦和电机动力学
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
F. Nori
中科院分区：
文献类型：
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作者：
Silvio Traversaro;A. Prete;R. Muradore;L. Natale;F. Nori
惯性参数辨识是类人机器人实现力矩控制的基础。同时，良好的摩擦和致动器动力学模型对于关节扭矩的低水平控制至关重要。我们提出了一种新的方法来识别惯性，摩擦和电机参数在一个单一的过程。该识别利用了直流电机的PWM测量值和安装在运动链内部的6轴力/扭矩传感器。偏最小二乘（PLS）方法被用来执行回归。我们确定了iCub人形机器人右臂的惯性，摩擦和电机参数。我们验证了所识别的模型可以准确地预测力/扭矩传感器测量值和电机电压。此外，我们比较了所识别的参数对CAD参数，在预测的力/扭矩传感器测量。最后，我们证明了估计模型可以有效地检测外部接触，并将其与基于网络的接触检测进行比较。所提出的方法相对于其他最先进的方法提供了一些优势，因为它的完整性（即，它识别惯性，摩擦和电机参数）和简单性（只有一个数据收集，没有特别的要求）。
Identification of inertial parameters is fundamental for the implementation of torque-based control in humanoids. At the same time, good models of friction and actuator dynamics are critical for the low-level control of joint torques. We propose a novel method to identify inertial, friction and motor parameters in a single procedure. The identification exploits the measurements of the PWM of the DC motors and a 6-axis force/torque sensor mounted inside the kinematic chain. The partial least-square (PLS) method is used to perform the regression. We identified the inertial, friction and motor parameters of the right arm of the iCub humanoid robot. We verified that the identified model can accurately predict the force/torque sensor measurements and the motor voltages. Moreover, we compared the identified parameters against the CAD parameters, in the prediction of the force/torque sensor measurements. Finally, we showed that the estimated model can effectively detect external contacts, comparing it against a tactile-based contact detection. The presented approach offers some advantages with respect to other state-of-the-art methods, because of its completeness (i.e. it identifies inertial, friction and motor parameters) and simplicity (only one data collection, with no particular requirements).