@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{2014-Kroll-Benchmark_NL-Applied_Soft_Computing, abstract = {Using benchmark problems to demonstrate and compare novel methods to the work of others could be more widely adopted by the Soft Computing community. This article contains a collection of several benchmark problems in nonlinear control and system identification, which are presented in a standardized format. Each problem is augmented by examples where it has been adopted for comparison. The selected examples range from component to plant level problems and originate mainly from the areas of mechatronics/drives and process systems. The authors hope that this overview contributes to a better adoption of benchmarking in method development, test and demonstration.}, author = {Kroll, Andreas and Schulte, Horst}, doi = {10.1016/j.asoc.2014.08.034}, interhash = {ca58b56ccd2109ffe849d7f07a63b80b}, intrahash = {7139422ab10bf0794e499313d3701cbf}, journal = {Applied Soft Computing}, language = {english}, month = {Dezember}, mrtnote = {peer}, mrturla = {http://141.51.48.24/MRT/Bibliothek/Publikationen/2014-Kroll_Schulte-ASC-Benchmark.pdf}, owner = {duerrbaum}, pages = {496-513}, title = {Benchmark problems for nonlinear system identification and control using Soft Computing methods: need and overview}, url = {http://www.sciencedirect.com/science/article/pii/S1568494614003998}, volume = 25, year = 2014 } @article{2017-Kroll_Duerrbaum-ASOC-OED, abstract = {Optimal Experiment Design (OED) is a well-developed concept for regression problems that are linear-in-the-parameters. In case of experiment design to identify nonlinear Takagi-Sugeno (TS) models, non-model-based approaches or OED restricted to the local model parameters (assuming the partitioning to be given) have been proposed. In this article, a Fisher Information Matrix (FIM) based OED method is proposed that considers local model and partition parameters. Due to the nonlinear model, the FIM depends on the model parameters that are subject of the subsequent identification. To resolve this paradoxical situation, at first a model-free space filling design (such as Latin Hypercube Sampling) is carried out. The collected data permits making design decisions such as determining the number of local models and identifying the parameters of an initial TS model. This initial TS model permits a FIM-based OED, such that data is collected which is optimal for a TS model. The estimates of this first stage will in general not be ideal. To become robust against parameter mismatch, a sequential optimal design is applied. In this work the focus is on D-optimal designs. The proposed method is demonstrated for three nonlinear regression problems: an industrial axial compressor and two test functions.}, author = {Kroll, Andreas and Dürrbaum, Axel}, doi = {10.1016/j.asoc.2017.07.015}, interhash = {a2bd07c0fd0a6d9e64cc7d4ad05ece28}, intrahash = {53184f758e02356412cf7982b5977f90}, journal = {Applied Soft Computing}, language = {english}, mrtnote = {peer,OED}, mrturla = {http://141.51.48.24/MRT/Bibliothek/Publikationen/2017-Kroll_Duerrbaum-ASoC-OED-submitted-PUB.pdf}, owner = {duerrbaum}, pages = {407 -- 422}, title = {On optimal experiment design for identifying premise and conclusion parameters of Takagi-Sugeno models: nonlinear regression case}, url = {https://reader.elsevier.com/reader/sd/pii/S1568494617304246}, volume = 60, year = 2017 } @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,oed}, pages = {1427 -- 1432}, title = {On joint optimal experiment design for identifying partition and local model parameters of Takagi-Sugeno models}, year = 2015 } @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 } @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 = {785bbc142c8d3d1405583d2da1181494}, journal = {International Journal of Fuzzy Systems}, language = {english}, mrtnote = {FuzzyIdControl,peer}, owner = {duerrbaum}, pages = {1-11}, 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}, year = 2017 } @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{2014-Kroll-Benchmark_NL-Applied_Soft_Computing, abstract = {Using benchmark problems to demonstrate and compare novel methods to the work of others could be more widely adopted by the Soft Computing community. This article contains a collection of several benchmark problems in nonlinear control and system identification, which are presented in a standardized format. Each problem is augmented by examples where it has been adopted for comparison. The selected examples range from component to plant level problems and originate mainly from the areas of mechatronics/drives and process systems. The authors hope that this overview contributes to a better adoption of benchmarking in method development, test and demonstration.}, author = {Kroll, Andreas and Schulte, Horst}, doi = {10.1016/j.asoc.2014.08.034}, interhash = {ca58b56ccd2109ffe849d7f07a63b80b}, intrahash = {7139422ab10bf0794e499313d3701cbf}, journal = {Applied Soft Computing}, language = {english}, month = {Dezember}, mrtnote = {peer}, owner = {duerrbaum}, pages = {496-513}, title = {Benchmark problems for nonlinear system identification and control using Soft Computing methods: need and overview}, url = {http://www.sciencedirect.com/science/article/pii/S1568494614003998}, volume = 25, year = 2014 } @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 } @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 }