• Proceedings of the KSME Conference
• /
• 2001.11a
• /
• pp.562-567
• /
• 2001

The number of required modes to provide accurate force information in a truncated model of a flexible structure is investigated. In the case of modal truncation of a distributed parameter system, the difference in convergence rates between displacements and forces is discussed. The residual flexibility, a term from past literature, is used to recapture some of the lost force information in a truncated model. This paper presents numerical and experimental results of a study where the residual flexibility is used in conjunction with a Kalman filter so that accurate force information may be obtained from a small set of displacement measurements with a reduced-order model. The motivation for this paper is to be able to obtain accurate information about unmeasurable dynamic reaction forces in a rotating machine for diagnostic and control purposes.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.1
• /
• pp.150-157
• /
• 2002

The number of required modes to provide accurate force information in a truncated model of a flexible structure is investigated. In the case of modal truncation of a distributed parameter system, the difference in convergence rates between displacements and forces is discussed. The residual flexibility. a term from past literature, is used to recapture some of the lost force information in a truncated model. This paper presents numerical and experimental results of a study where the residual flexibility is used in conjunction with a Kalman filter so that accurate force information may be obtained from a small set of displacement measurements wish a reduced-order model. The motivation for this paper is to be able to obtain accurate information about unmeasurable dynamic reaction forces in a rotating machine for diagnostic and control purposes.

• The Journal of the Acoustical Society of Korea
• /
• v.16 no.3
• /
• pp.38-44
• /
• 1997

In this study, wer derived LMF-Sato, LMSF-Sato complex blind equalization algorithms for complex data. And then, the convergence rates, the convergence characteristics at the steady state and the stability of the proposed LMF and LMSF blind equalization algorithms are compared with those of LMS-Sato blind equalization algorithm. In simulations with 16-QAM data, LMF-Sato and LMSF-Sato algorithms showed better performance comparing with LMS-Sato algorithm generally. When the initial estimation errors of the weights of the equalizer are large, LMF-Sato algorithm showed ill characteristic in stability. However, LMSF-Sato algorithm has good covergence characteristics and preserves robustness.