@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.},
  address = {Paris, France},
  author = {Kroll, Andreas and Dürrbaum, Axel},
  booktitle = {CICA 2011 IEEE Symposium on Computational Intelligence in Control
	and Automation},
  interhash = {29ee47b9b69180a8af57a3cdf81281e0},
  intrahash = {18d0b3bec62d3810613f240dc19df603},
  language = {english},
  month = {April 11-15},
  mrtnote = {peer, FuzzyIdControl, talk:Dürrbaum},
  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
}

@inproceedings{Duerrbaum-2015-SysID,
  abstract = {Optimal Experiment Design (OED) is a well-developed concept for regression
	problems that are linear-in-their-parameters or for linear dynamical
	models. In case of nonlinear Takagi-Sugeno models either non-model-based
	experiment design or OED restricted to the local model parameters
	has been examined. This article proposes a joint design of local
	model and partition parameters that bases on the Fisher Information
	Matrix (FIM). For this purpose, a symbolic description of the joint
	FIM is derived. Its heterogeneous structure can make it badly conditioned,
	complicating computation of the determinant for D-optimal design.
	This problem is relaxed using determinant decomposition. A theoretical
	analysis and a case study show that experiment design for local model
	and partition parameters may significantly differ from each other.},
  address = {Beijing, China},
  author = {Kroll, Andreas and Dürrbaum, Axel},
  booktitle = {Proceedings of the 17th IFAC Symposium on System Identification ({SysID})},
  doi = {doi:10.1016/j.ifacol.2015.12.333},
  interhash = {c8b8e8d219667b0df23b145097939fd4},
  intrahash = {a62f4bcd0dac434df35ebba90261698d},
  language = {english},
  month = {October 19-21},
  mrtnote = {peer,talk:Dürrbaum,oedg},
  pages = {1427 -- 1432},
  title = {On joint optimal experiment design for identifying partition and
	local model parameters of Takagi-Sugeno models},
  year = 2015
}