On Control-specific Derivation of Affine Takagi-Sugeno Models from Physical Models: Assessment Criteria and Modeling Procedure
A. Kroll, und A. Dürrbaum.
CICA 2011 IEEE Symposium on Computational Intelligence in Control and Automation, Seite 23 -- 30. Paris, France, IEEE Symposium Series on Computational Intelligence, (April 2011)

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.

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@inproceedings{KrollSSCI2011TSK,
  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.},
  added-at = {2022-03-31T09:31:59.000+0200},
  address = {Paris, France},
  author = {Kroll, Andreas and Dürrbaum, Axel},
  biburl = {https://puma.uni-kassel.de/bibtex/218d0b3bec62d3810613f240dc19df603/00987301},
  booktitle = {CICA 2011 IEEE Symposium on Computational Intelligence in Control and Automation},
  interhash = {29ee47b9b69180a8af57a3cdf81281e0},
  intrahash = {18d0b3bec62d3810613f240dc19df603},
  keywords = {Nonlinear Takagi-Sugeno control dynamic for isac-www modeling modeling, systems,},
  language = {english},
  month = {April 11-15},
  mrtnote = {peer, FuzzyIdControl, talk:Dürrbaum},
  organization = {IEEE Symposium Series on Computational Intelligence},
  pages = {23 -- 30},
  timestamp = {2022-03-31T11:21:32.000+0200},
  title = {On Control-specific Derivation of Affine Takagi-Sugeno Models from Physical Models: Assessment Criteria and Modeling Procedure},
  url = {http://www.ieee-ssci.org/},
  year = 2011
}

%0 Conference Paper
%1 KrollSSCI2011TSK
%A Kroll, Andreas
%A Dürrbaum, Axel
%B CICA 2011 IEEE Symposium on Computational Intelligence in Control and Automation
%C Paris, France
%D 2011
%K Nonlinear Takagi-Sugeno control dynamic for isac-www modeling modeling, systems,
%P 23 -- 30
%T On Control-specific Derivation of Affine Takagi-Sugeno Models from Physical Models: Assessment Criteria and Modeling Procedure
%U http://www.ieee-ssci.org/
%X 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.

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