@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.},
  address = {Paris, France},
  author = {Kroll, Andreas and Dürrbaum, Axel},
  booktitle = {CICA 2011 IEEE Symposium on Computational Intelligence in Control
	and Automation},
  interhash = {29ee47b9b69180a8af57a3cdf81281e0},
  intrahash = {ac34f35f071b90ce7182624f93c7427c},
  language = {english},
  month = {April 11-15, 2011},
  mrtnote = {peer},
  organization = {IEEE Symposium Series on Computational Intelligence},
  pages = {23-30},
  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
}