Frequency Domain Structural System Identification by Observability Range Space Extraction
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Published:1996-06-01
Issue:2
Volume:118
Page:211-220
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ISSN:0022-0434
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Container-title:Journal of Dynamic Systems, Measurement, and Control
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language:en
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Short-container-title:
Author:
Liu Ketao1, Jacques Robert N.1, Miller David W.1
Affiliation:
1. Space Engineering Research Center, Massachusetts Institute of Technology, Cambridge, MA 02139
Abstract
This paper presents the Frequency Domain Observability Range Space Extraction (FORSE) identification algorithm. FORSE is a singular value decomposition based identification algorithm which constructs a state space model directly from frequency domain data. The concept of system identification by observability range space extraction was developed by generalizing the Q-Markov Covariance Equivalent Realization and Eigensystem Realization Algorithm. The numerical properties of FORSE are well behaved when applied to multi-variable and high dimensional structural systems. It can achieve high modeling accuracy by properly overparameterizing the system. The effectiveness of this algorithm for structural system identification is demonstrated using the MIT Middeck Active Control Experiment (MACE). MACE is an active structural control experiment to be conducted in the Space Shuttle middeck. Results of ground experiments using this algorithm will be discussed.
Publisher
ASME International
Subject
Computer Science Applications,Mechanical Engineering,Instrumentation,Information Systems,Control and Systems Engineering
Reference23 articles.
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C. W.
, HuangJ. K., PhanM., and JuangJ. N., 1992, “Integrated System Identification and State Estimation for Control of Large Flexible Space Structures,” Journal of Guidance, Control and Dynamics, Vol. 15, No. 1, pp. 88–95. 4. Chen, C. W., Juang, J. N., and Lee, G., 1993, “Frequency Domain State-Space System Identification,” Proceedings of American Control Conference, San Francisco, CA, June, 1993. 5. Jacques, R. N., and Miller, W. D., 1993, “Multivariable Model Identification from Frequency Response Data,” Proceedings of 32th Conference on Decision and Control, Dec. 1993.
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