Supervision of nonlinear adaptive controllers based on fuzzy models
A novel approach for the supervision of fuzzy model on-line adaptation is proposed. A nonlinear predictive controller is designed based on a Takagi–Sugeno fuzzy model. By adapting the fuzzy model on-line, high control performance can be achieved even with time-variant process behaviour and changing...
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| Vydáno v: | Control engineering practice Ročník 8; číslo 10; s. 1093 - 1105 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.10.2000
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| ISSN: | 0967-0661, 1873-6939 |
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| Abstract | A novel approach for the supervision of fuzzy model on-line adaptation is proposed. A nonlinear predictive controller is designed based on a Takagi–Sugeno fuzzy model. By adapting the fuzzy model on-line, high control performance can be achieved even with time-variant process behaviour and changing unmodelled disturbances. A local weighted recursive least-squares algorithm exploits the local linearity of Takagi–Sugeno fuzzy models. In order to cope with problems resulting from insufficient excitation, a supervisory level is introduced. It comprises a variable forgetting factor and an additional adaptation model which makes the on-line adaptation robust and reliable. The effectiveness and real-world applicability of the proposed approach are demonstrated by application to temperature control of a heat exchanger. |
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| AbstractList | A novel approach for the supervision of fuzzy model on-line adaptation is proposed. A nonlinear predictive controller is designed based on a Takagi–Sugeno fuzzy model. By adapting the fuzzy model on-line, high control performance can be achieved even with time-variant process behaviour and changing unmodelled disturbances. A local weighted recursive least-squares algorithm exploits the local linearity of Takagi–Sugeno fuzzy models. In order to cope with problems resulting from insufficient excitation, a supervisory level is introduced. It comprises a variable forgetting factor and an additional adaptation model which makes the on-line adaptation robust and reliable. The effectiveness and real-world applicability of the proposed approach are demonstrated by application to temperature control of a heat exchanger. |
| Author | Fink, Alexander Nelles, Oliver Fischer, Martin Isermann, Rolf |
| Author_xml | – sequence: 1 givenname: Alexander surname: Fink fullname: Fink, Alexander email: afink@iat.tu-darmstadt.de organization: Institute of Automatic Control, Laboratory of Control Systems and Process Automation, Darmstadt University of Technology, Landgraf-Georg-Strasse 4, 64283 Darmstadt, Germany – sequence: 2 givenname: Martin surname: Fischer fullname: Fischer, Martin email: martin.fischer@at.siemens.de organization: Siemens-Automotive Systems, Wernerwerkstrasse 2, 93049 Regensburg, Germany – sequence: 3 givenname: Oliver surname: Nelles fullname: Nelles, Oliver email: nelles@mechatro2.me.berkeley.edu organization: Department of Mechanical Engineering, Mechanical Systems Control Laboratory, University of California at Berkeley, 6189 Etcheverry Hall, Berkeley, CA 94720, USA – sequence: 4 givenname: Rolf surname: Isermann fullname: Isermann, Rolf email: risermann@iat.tu-darmstadt.de organization: Institute of Automatic Control, Laboratory of Control Systems and Process Automation, Darmstadt University of Technology, Landgraf-Georg-Strasse 4, 64283 Darmstadt, Germany |
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| Cites_doi | 10.1109/72.501735 10.1080/002071797224487 10.1016/0005-1098(81)90070-4 10.1016/0005-1098(93)90049-Y 10.1016/0165-0114(88)90113-3 10.1109/21.256541 10.1162/neco.1989.1.2.281 10.1007/978-3-642-83530-8 10.1109/TSMC.1985.6313399 10.1109/ACC.1995.533847 10.1080/00207179308923046 10.1016/0967-0661(96)00175-X 10.1016/0005-1098(85)90037-8 10.1016/S1474-6670(17)42927-2 10.1016/S0005-1098(97)00010-1 10.1109/CDC.1996.574356 |
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| Keywords | On-line adaptation Recursive least squares (RLS) Takagi–Sugeno fuzzy models Heat exchanger Variable forgetting factor Adaptive control Nonlinear control |
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| SubjectTerms | Adaptive control Heat exchanger Nonlinear control On-line adaptation Recursive least squares (RLS) Takagi–Sugeno fuzzy models Variable forgetting factor |
| Title | Supervision of nonlinear adaptive controllers based on fuzzy models |
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