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A new approach to model reduction of nonlinear control systems using smooth orthogonal decomposition
Author(s) -
Ilbeigi Shahab,
Chelidze David
Publication year - 2018
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/rnc.4238
Subject(s) - proper orthogonal decomposition , model order reduction , robustness (evolution) , nonlinear system , linear subspace , computer science , reduction (mathematics) , computation , point of delivery , dimensionality reduction , control theory (sociology) , decomposition , algorithm , mathematics , control (management) , artificial intelligence , projection (relational algebra) , biochemistry , chemistry , physics , geometry , quantum mechanics , gene , agronomy , biology , ecology
Summary A new approach to model order reduction of nonlinear control systems is aimed at developing persistent reduced order models (ROMs) that are robust to the changes in system's energy level. A multivariate analysis method called smooth orthogonal decomposition (SOD) is used to identify the dynamically relevant modal structures of the control system. The identified SOD subspaces are used to develop persistent ROMs. Performance of the resultant SOD‐based ROM is compared with proper orthogonal decomposition (POD)–based ROM by evaluating their robustness to the changes in system's energy level. Results show that SOD‐based ROMs are valid for a relatively wider range of the nonlinear control system's energy when compared with POD‐based models. In addition, the SOD‐based ROMs show considerably faster computations compared to the POD‐based ROMs of same order. For the considered dynamic system, SOD provides more effective reduction in dimension and complexity compared to POD.