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Sampled‐data model validation: An algorithm and experimental application
Author(s) -
Dullerud Geir,
Smith Roy
Publication year - 1996
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/(sici)1099-1239(199611)6:9/10<1065::aid-rnc269>3.0.co;2-n
Subject(s) - bounded function , algorithm , computer science , geodetic datum , decimation , matrix (chemical analysis) , noise (video) , mathematical optimization , mathematics , artificial intelligence , mathematical analysis , materials science , image (mathematics) , cartography , filter (signal processing) , composite material , computer vision , geography
The application of robust control theory requires representative models containing unknown bounded perturbations and unknown bounded noise/disturbance signals. Model validation is a means of assessing the applicability of a given model with respect to experimental data. We consider a sampled‐data approach, using a continuous time model, including unknown perturbations and signals, and a discrete experimental datum of finite length. The sampled‐data model validation problem can be formulated as a linear matrix inequality problem. A computationally tractable algorithm, which employs data decimation and exploits the problem structure, is presented in the paper. This method is applied to a 2‐D heating experiment.