@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
}

@article{2016-AS_AK_-IJFS-TSController,
  abstract = {Often models are used for controller design that was identified under the objective to well approximate the system under study. In this paper, a scheme for identifying discrete-time locally affine Takagi -- Sugeno (TS) models is presented, which better reflects the dedicated model use for designing a TS controller. For this purpose, after an initial open-loop experiment and controller design step, additional experiments are carried out in closed loop, each followed by an identification and controller design step. The deployed TS controllers are of parallel distributed compensator type but augmented by parallel drift and steady-state error compensation. The focus in this work is on a complete method that is simple and usable for real-world applications. To illustrate the practicality of the method, it is demonstrated on a laboratory-scale three-tank system.},
  author = {Schrodt, Alexander and Kroll, Andreas},
  doi = {10.1007/s40815-016-0290-x},
  interhash = {c2aa345ebc8a74bf51f96746753528b9},
  intrahash = {7ff5788ce25791805fd9245a47a405ea},
  journal = {International Journal of Fuzzy Systems},
  language = {english},
  mrtnote = {FuzzyIdControl,peer},
  number = 6,
  owner = {duerrbaum},
  pages = {1978-1988},
  title = {On Iterative Closed-Loop Identification Using Affine Takagi-Sugeno Models and Controllers},
  url = {http://link.springer.com/article/10.1007/s40815-016-0290-x},
  volume = 19,
  year = 2017
}