%0 Generic %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 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.

@conference{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 = {2012-11-28T10:46:17.000+0100}, address = {Paris, France}, author = {Kroll, Andreas and Dürrbaum, Axel}, biburl = {https://www.bibsonomy.org/bibtex/2ac34f35f071b90ce7182624f93c7427c/axel_d}, booktitle = {CICA 2011 IEEE Symposium on Computational Intelligence in Control and Automation}, interhash = {29ee47b9b69180a8af57a3cdf81281e0}, intrahash = {ac34f35f071b90ce7182624f93c7427c}, keywords = {Nonlinear Takagi-Sugeno control dynamic for modeling modeling, systems,}, language = {english}, month = {April 11-15, 2011}, mrtnote = {peer, FuzzyIdControl}, organization = {IEEE Symposium Series on Computational Intelligence}, pages = {23-30}, timestamp = {2012-11-28T10:46:19.000+0100}, 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 }