Andere Publikation (z.B. Zeitungsartikel, Interview, populärwissenschaftliche Darstellung)
On Control-specific Derivation of Affine Takagi-Sugeno Models from Physical Models: Assessment Criteria and Modeling Procedure
Details zur Publikation
Autor(inn)en: | Dürrbaum, A. |
Verlagsort / Veröffentlichungsort: | Paris, France |
Publikationsjahr: | 2011 |
Seitenbereich: | 23-30 |
Buchtitel: | CICA 2011 IEEE Symposium on Computational Intelligence in Control and Automation |
DOI-Link der Erstveröffentlichung: |
URN / URL: |
Zusammenfassung, Abstract
Models are commonly derived and their performance is assessed wrt. minimal prediction error on a closed data set. However, if no perfect model can be used, the degrees of freedom in modeling should be used to adjust the model to application-specific metrics. For model-based controller design, control-oriented performance metrics (e.g. performance wrt. to control-critical properties) are important, but not primarily prediction (i.e. prognosis- and simulation-oriented) ones. This motivates the derivation of control-specific models. The contribution introduces structured and quantitative measures on "model suitability for control" for the class of affine dynamic Takagi-Sugeno models. A method is suggested that derives control-specific dynamic models from a physical model given as a set of nonlinear differential equations. Within a case study, the proposed method demonstrates its significance: Using control-specific models improves control performance metrics such as set-point tracking quality, stability region and energy efficiency.
Models are commonly derived and their performance is assessed wrt. minimal prediction error on a closed data set. However, if no perfect model can be used, the degrees of freedom in modeling should be used to adjust the model to application-specific metrics. For model-based controller design, control-oriented performance metrics (e.g. performance wrt. to control-critical properties) are important, but not primarily prediction (i.e. prognosis- and simulation-oriented) ones. This motivates the derivation of control-specific models. The contribution introduces structured and quantitative measures on "model suitability for control" for the class of affine dynamic Takagi-Sugeno models. A method is suggested that derives control-specific dynamic models from a physical model given as a set of nonlinear differential equations. Within a case study, the proposed method demonstrates its significance: Using control-specific models improves control performance metrics such as set-point tracking quality, stability region and energy efficiency.
Schlagwörter
modeling for control, Nonlinear dynamic modeling, Takagi-Sugeno systems
