Abstract
A global and a local identification approach are developed for approximation of linear parameter-varying (LPV) systems. The utilized model structure is a linear combination of globally fixed (scheduling-independent) orthonormal basis functions (OBFs) with scheduling-parameter dependent weights. Whether the weighting is applied on the input or on the output side of the OBFs, the resulting models have different modeling capabilities. The local identification approach of these structures is based on the interpolation of locally identified LTI models on the scheduling domain where the local models are composed from a fixed set of OBFs. The global approach utilizes a priori chosen functional dependence of the parameter-varying weighting of a fixed set of OBFs to deliver global model estimation from measured I/O data. Selection of the OBFs that guarantee the least worst-case modeling error for the local behaviors in an asymptotic sense, is accomplished through the fuzzy Kolmogorov c-max approach. The proposed methods are analyzed in terms of applicability and consistency of the estimates.
Original language | English |
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Title of host publication | Proceedings of the 46th Conference on Decision and Control (CDC 2007) 12-14 December 2007, New Orleans, Louisiana |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 3646-3653 |
ISBN (Print) | 978-1-4244-1497-0 |
DOIs | |
Publication status | Published - 2007 |
Event | 46th IEEE Conference on Decision and Control (CDC 2007) - New Orleans, United States Duration: 12 Dec 2007 → 14 Dec 2007 Conference number: 46 |
Conference
Conference | 46th IEEE Conference on Decision and Control (CDC 2007) |
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Abbreviated title | CDC 2007 |
Country/Territory | United States |
City | New Orleans |
Period | 12/12/07 → 14/12/07 |