A new method of rapid rigid-body parameter identification of MIMO systems

The increasing need for manufacturing speed and accuracy leads to the desire of more accurate 
dynamic models of machine tools. Usually, parameter identification of multi-body systems is 
limited to the least-squares solution of over-determined linear equations. To enhance the 
modeling and identification accuracy, a new identification method is proposed by using a non-
linear optimization method with a normalized weight objective function to estimate the rigid-
body parameters of a MIMO system, which is coupled and nonlinear by nature. This method 
normalizes the prediction sensitivity of each axis. The proposed method is applied to a three-axis 
feed drive system, which contains one linear drive and two rotary drives, stacked in a series 
configuration. The results show that the total relative prediction error decreases significantly 
compared to the conventional least square methods. Moreover, the identified dynamic model can 
be used as a virtual machine to predict the tracking error more accurately compared to using 
separate SISO models.