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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/804442

Title: Robust identification of continuous-time systems from sampled data
Author/Creator: Yuz, Juan I.; Goodwin, Graham C.
Institution: The University of Newcastle. Faculty of Engineering & Built Environment, School of Electrical Engineering and Computer Science
Description: In this chapter we have explored the robustness issues that arise in the identification of continuous-time systems from sampled data. A key observation is that the fidelity of the models at high frequencies generally plays an important role in obtaining models suitable for continuous-time system identification. The problems discussed above have been illustrated for both, deterministic and stochastic systems. Special attention was given to the identification of continuous-time autoregressive stochastic models from sampled data. We have argued that traditional approaches to this problem are inherently sensitive to high-frequency modelling errors. We have also argued that these difficulties can be mitigated by using the proposed FDML with restricted bandwidth.
Relation: Identification of Continuous-Time Models from Sampled Data p. 67-89
Relation: Advances in Industrial Control
Publisher Link: http://dx.doi.org/10.1007/978-1-84800-161-9
Date: 2008
Publisher: Springer
Keyword(s): continuous-time systems; high-frequency; FDML; sampled data; robust
Resource Type: book chapter
Identifier: http://hdl.handle.net/1959.13/804442
Identifier: ISBN:9781848001602

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