Filter based iterative learning least square identification method
                                                             SHI Wei min, WU Da wei, YANG Liang liang
                      （School of Mechanical and Automation, Zhejiang Sci Tech University, Hangzhou 310018, China）

Abstract: Aiming at the parameter identification of the permanent magnet linear synchronous motor（PMSLM）, the model parameters of servo system can be successfully identified by the block least squares algorithmon the basis of the study of the linear motor smathematical model and the least square identification algorithm.However, its model is not accurate enough, especially for the oscillating element identification in the servo system.According to the shortcomings of the traditional identification model, a filter based iterative learning least square identification method was proposed.The noise interference was reduced by the filter in the identification process, andnonlinear problem of the identification model was solved by iterating in this algorithm.The simulation and experimentresults indicate that the filter based iterative learning least square identification method can effectively improve the accuracy of identificationcompared with the block least squares algorithm, and the identification results of the oscillating element are more consistent with those obtained by power spectrum analysis.

Key words: permanent magnet linear synchronous motor（PMSLM）; parameter identification; iterative learning; least square