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Robust model order reduction technique for MIMO systems via ANN‐LMI‐based state residualization
Author(s) -
Alsmadi Othman M. K.,
AboHammour Zaer. S.,
AlSmadi Adnan M.
Publication year - 2012
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.728
Subject(s) - linear matrix inequality , eigenvalues and eigenvectors , model order reduction , control theory (sociology) , reduction (mathematics) , mathematics , state (computer science) , realization (probability) , artificial neural network , matrix (chemical analysis) , projection (relational algebra) , mimo , matrix decomposition , mathematical optimization , computer science , algorithm , control (management) , artificial intelligence , beamforming , statistics , physics , geometry , materials science , quantum mechanics , composite material
SUMMARY Even though model order reduction (MOR) techniques for linear dynamical systems are developed rather properly, there are still quite a lot of issues to be considered. This paper addresses a novel MOR technique for multi‐input multi‐output system with dominant eigenvalue preservation, which leads to controller cost minimization. The new technique is formulated based on an artificial neural network (ANN) prediction of an upper triangular form of the system state matrix A. Using the new system state matrix along with the linear matrix inequality (LMI) optimization method, a permutation matrix is obtained which leads to the new formulation of the complete system considered for MOR. Utilizing the non‐projection state residualization technique, a reduced model order is obtained. The proposed ANN‐LMI‐based MOR method is compared with well‐known reduction techniques such as the balanced Schur decomposition, proper orthogonal decomposition (POD), and state elimination through balanced realization. Copyright © 2010 John Wiley & Sons, Ltd.