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Advanced Manual System Identification Using Lead and Lag Compensators With Complex Poles and Zeros

[+] Author Affiliations
Piers M. Echols-Jones, Maxim E. Dokukin, Igor Sokolov, William C. Messner

Tufts University, Medford, MA

Paper No. DSCC2016-9854, pp. V001T14A005; 7 pages
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5069-5
  • Copyright © 2016 by ASME


Automated methods for deriving dynamic models from frequency response data of high-order dynamics systems are the default choice of most engineers. However, these methods themselves often require manual tuning of weighting parameters, a priori selection of system order, and even by hand removal of extraneous dynamics. On the other hand, manually matching complicated features in the Bode plot of the frequency response of high-order system is difficult with conventional first and second order numerators and denominators. In this papers we present a manual technique for systematically creating a dynamic model from Bode plots of frequency response data with complicated features. We apply the method to identifying dynamics of a piezoelectric stage holding the sample of an atomic force microscope (AFM). We show the manual method works better than the tfest command of Matlab™ for this example system.

Copyright © 2016 by ASME



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