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MULTIDIMENSIONAL REALIZATION OF LARGE SCALE UNCERTAIN SYSTEMS FOR MULTIVARIABLE STABILITY MARGIN COMPUTATION
Author(s) -
Russell Evan L.,
Power Christopher P. H.,
Braatz Richard D.
Publication year - 1997
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(199702)7:2<113::aid-rnc304>3.0.co;2-q
Subject(s) - multivariable calculus , realization (probability) , computation , stability (learning theory) , scale (ratio) , computer science , margin (machine learning) , control theory (sociology) , mathematics , control engineering , engineering , artificial intelligence , control (management) , algorithm , machine learning , statistics , geography , cartography
The prevailing framework for robust stability and performance analysis requires that the uncertain system be written as a linear fractional transformation of the uncertain parameters. This problem is algebraically equivalent to the problem of deriving the state space realization for a multidimensional transfer function matrix, for which a systematic algorithm was recently provided by Cheng and DeMoor. In this work an algorithm is developed that reduces the dimension of the realizations while improving numerical accuracy, reducing computational expense, and reducing run‐time memory requirements. Such improvements are required for the realization of large scale uncertain systems, which have large numbers of inputs, outputs, states, and/or uncertain parameters. © 1997 by John Wiley & Sons, Ltd.