System identification of Active Magnetic Bearing for commissioning

System identification is a prerequisite for the operation of Active Magnetic Bearings (AMB). An identified model is required for synthesizing high performance model based controllers. However, from a commissioning point of view, certain parameters such as AMB stiffness constants and in the case of a flexible rotor, the flexible mode natural frequency (and damping ratio) of the rotor have to be explicitly identified. In this work, system identification of AMB is approached within the context of commissioning. A procedure for identification is developed and applied to experimental data from a prototype AMB system. A linear state-space model, along with the required parameters, is identified.

[1]  R. Mohd-Mokhtar,et al.  Continuous time system identification of magnetic bearing systems using frequency response data , 2004, 2004 5th Asian Control Conference (IEEE Cat. No.04EX904).

[2]  Hwa Soo Kim,et al.  Modelling and system identification of active magnetic bearing systems , 2007 .

[3]  Urban Forssell.  Closed-loop Identification : Methods, Theory, and Applications , 1999 .

[5]  L. Ljung.  State of the art in linear system identification: time and frequency domain methods , 2004, Proceedings of the 2004 American Control Conference.

[6]  T. McKelvey,et al.  Subspace methods for frequency domain data , 2004, Proceedings of the 2004 American Control Conference.

[7]  Stig Moberg,et al.  Experimental Comparison of Methods for Multivariable Frequency Response Function Estimation , 2008 .

[8]  Alessandro Chiuso,et al.  On the Relation Between CCA and Predictor-Based Subspace Identification , 2005, IEEE Transactions on Automatic Control.

[9]  Katja Hynynen,et al.  Broadband excitation in the system identification of active magnetic bearing rotor systems , 2011 .

[10]  Florian Lösch.  Identification and automated controller design for active magnetic bearing systems , 2002 .

[11]  L. Ljung,et al.  Subspace-based multivariable system identification from frequency response data , 1996, IEEE Trans. Autom. Control..

[12]  Sang-Ho Lee,et al.  Frequency domain control-relevant identification of MIMO AMB rigid rotor , 2003, Autom..

[13]  Paul M. J. Van den Hof,et al.  Multivariable frequency domain identification using IV-based linear regression , 2010, 49th IEEE Conference on Decision and Control (CDC).

[14]  Philipp Beckerle,et al.  Model-Based Fault Detection on Active Magnetic Bearings by Means of Online Transfer-Factor Estimation , 2012 .

[15]  Alexander Smirnov,et al.  AMB system for high-speed motors using automatic commissioning , 2012 .

[16]  G. Schweitzer,et al.  Magnetic bearings : theory, design, and application to rotating machinery , 2009 .

[17]  T. McKelvey.  Identification of State-Space Models from Time and Frequency Data , 1995 .

[18]  Erik Wernholt,et al.  Multivariable Frequency-Domain Identification of Industrial Robots , 2007 .

[19]  C. Sanathanan,et al.  Transfer function synthesis as a ratio of two complex polynomials , 1963 .

[20]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[21]  Raoul Herzog,et al.  Multivariable Identification of Active Magnetic Bearing Systems , 1997 .

[22]  Rik Pintelon,et al.  System Identification: A Frequency Domain Approach , 2012 .

[23]  Eric Walter,et al.  Identification of Parametric Models: from Experimental Data , 1997 .

[24]  Erkki Lantto,et al.  Robust control of magnetic bearings in subcritical machines , 1999 .

[25]  Jerzy T. Sawicki,et al.  Detecting cracked rotors using auxiliary harmonic excitation , 2011 .