@inproceedings{32,
  author = {Niels van Tuijl and Chia-Pei Wang and Tom Oomen and Kaan Erkorkmaz},
  title = {A new method of rapid rigid-body parameter  identification of MIMO systems},
  abstract = {<p>The increasing need for manufacturing speed and accuracy leads to the desire of more accurate&nbsp;<br />
dynamic models of machine tools. Usually, parameter identification of multi-body systems is&nbsp;<br />
limited to the least-squares solution of over-determined linear equations. To enhance the&nbsp;<br />
modeling and identification accuracy, a new identification method is proposed by using a non-<br />
linear optimization method with a normalized weight objective function to estimate the rigid-<br />
body parameters of a MIMO system, which is coupled and nonlinear by nature. This method&nbsp;<br />
normalizes the prediction sensitivity of each axis. The proposed method is applied to a three-axis&nbsp;<br />
feed drive system, which contains one linear drive and two rotary drives, stacked in a series&nbsp;<br />
configuration. The results show that the total relative prediction error decreases significantly&nbsp;<br />
compared to the conventional least square methods. Moreover, the identified dynamic model can&nbsp;<br />
be used as a virtual machine to predict the tracking error more accurately compared to using&nbsp;<br />
separate SISO models.</p>
},
  year = {2018},
  journal = {7th International Conference on Virtual Machining Process Technology (VMPT)},
  address = {Hamilton, Canada},
}