This paper proposes two techniques for reducing the number of uncertain parameters in order to simplify robust controller design and to reduce conservatism inherent in robust controllers. The system is assumed to have a known structure with parametric uncertainties that represent plant dynamics variation. An original set of parameters is estimated by nonlinear least-squares (NLS) optimization using noisy frequency response functions. Utilizing the property of asymptotic normality for NLS estimates, the original parameter set can be reparameterized by an affine function of the smaller number of uncorrelated parameters. The correlation among uncertain parameters is detected by the principal component analysis in one technique and optimization with a bilinear matrix inequality in the other. Numerical examples illustrate the usefulness of the proposed techniques.
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e-mail: nagamune@mech.ubc.ca
e-mail: jchoi@egr.msu.edu
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March 2010
Research Papers
Parameter Reduction in Estimated Model Sets for Robust Control
Ryozo Nagamune,
Ryozo Nagamune
Department of Mechanical Engineering,
e-mail: nagamune@mech.ubc.ca
University of British Columbia
, Vancouver, BC, V6T 1Z4, Canada
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Jongeun Choi
Jongeun Choi
Department of Mechanical Engineering and Department of Electrical and Computer Engineering,
e-mail: jchoi@egr.msu.edu
Michigan State University
, East Lansing, MI 48824-1226
Search for other works by this author on:
Ryozo Nagamune
Department of Mechanical Engineering,
University of British Columbia
, Vancouver, BC, V6T 1Z4, Canadae-mail: nagamune@mech.ubc.ca
Jongeun Choi
Department of Mechanical Engineering and Department of Electrical and Computer Engineering,
Michigan State University
, East Lansing, MI 48824-1226e-mail: jchoi@egr.msu.edu
J. Dyn. Sys., Meas., Control. Mar 2010, 132(2): 021002 (10 pages)
Published Online: February 2, 2010
Article history
Received:
November 9, 2008
Revised:
September 18, 2009
Online:
February 2, 2010
Published:
February 2, 2010
Citation
Nagamune, R., and Choi, J. (February 2, 2010). "Parameter Reduction in Estimated Model Sets for Robust Control." ASME. J. Dyn. Sys., Meas., Control. March 2010; 132(2): 021002. https://doi.org/10.1115/1.4000661
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