Embedding of nonlinear systems in a linear parameter-varying representation

H. Abbas, R. Toth, M. Petreczky, N. Meskin, J. Mohammadpour

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

17 Citations (Scopus)
16 Downloads (Pure)

Abstract

This paper introduces a systematic approach to synthesize linear parameter-varying (LPV) representations of nonlinear (NL) systems which are originally defined by control affine state-space representations. The conversion approach results in LPV state-space representations in the observable canonical form. Based on the relative degree concept of NL systems, the states of a given NL representation are transformed to new coordinates that provide its normal form. In the SISO case, all nonlinearities of the original system are embedded in one NL function which is factorized to construct the LPV form. An algorithms is proposed for this purpose. The resulting transformation yields an LPV model where the scheduling parameter depends on the derivatives of the inputs and outputs of the system. In addition, if the states of the NL model can be measured or estimated, then the procedure can be modified to provide LPV models scheduled by these states. Examples are included for illustration.
Original languageEnglish
Title of host publicationProceedings of the 19th IFAC World Congress of the International Federation of Automatic Control, (IFAC'14), 24-29 August 2014, Cape Town, South Africa
Pages6907-6913
Publication statusPublished - 2014
Event19th IFAC World Congress on International Federation of Automatic Control ( IFAC 2014) - Cape Town International Convention Centre, Cape Town, South Africa
Duration: 24 Aug 201429 Aug 2014
Conference number: 19
http://www.ifac2014.org

Conference

Conference19th IFAC World Congress on International Federation of Automatic Control ( IFAC 2014)
Abbreviated titleIFAC 2014
CountrySouth Africa
CityCape Town
Period24/08/1429/08/14
Internet address

Fingerprint

Dive into the research topics of 'Embedding of nonlinear systems in a linear parameter-varying representation'. Together they form a unique fingerprint.

Cite this