Verfügbarkeit: Fuzzy logic, identification and predictive control

Fuzzy logic, identification and predictive control:

Gespeichert in:

Bibliographische Detailangaben
Beteiligte Personen:	Espinosa, Jairo (VerfasserIn), Vandewalle, Joos 1948- (VerfasserIn), Wertz, Vincent (VerfasserIn)
Format:	Buch
Sprache:	Deutsch
Veröffentlicht:	London [u.a.] Springer 2005
Schriftenreihe:	Advances in Industrial Control
Schlagwörter:	Fuzzy systems Predictive control Fuzzy-Regelung Systemidentifikation Fuzzy-Logik Prädiktive Regelung
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=012835453&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	XIX, 263 S. graph. Darst.
ISBN:	1852338288

Internformat

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Datensatz im Suchindex

DE-BY-TUM_call_number	1002 MSR 688 2005 A 405
DE-BY-TUM_katkey	1492583
DE-BY-TUM_location	10
DE-BY-TUM_media_number	040050361678
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adam_text	CONTENTS PART I FUZZY MODELING 1 FUZZY MODELING ............................................ 3 1.1 FUNCTIONAPPROXIMATION ................................. 4 1.1.1 SYSTEMDESCRIPTION................................ 4 1.1.2 APPROXIMATIONERROR .............................. 6 1.1.3 CONSTRUCTINGUNITSINTHEFUZZYMODELS.............. 8 1.2 APPROXIMATION CAPABILITIES OF TAKAGISUGENO FUZZYMODELS........................................... 18 1.3 CONCLUSIONANDSUMMARY ................................ 20 2 CONSTRUCTING FUZZY MODELS FROM INPUT-OUTPUT DATA ...... 21 2.1 MOSAICORTABLELOOKUPSCHEME........................... 22 2.1.1 ILLUSTRATIVEEXAMPLE ............................... 25 2.2 USINGGRADIENTDESCENT .................................. 26 2.2.1 GRADIENT UPDATING FOR TRAPEZOIDAL MEMBERSHIP FUNCTIONS ........................................ 30 2.2.2 GRADIENT UPDATING FOR TRIANGULAR MEMBERSHIP FUNCTIONS WITH OVERLAP 1 2 .......................... 31 2.2.3 GRADIENT UPDATING FOR POLYNOMIAL MEMBERSHIP FUNCTIONS ........................................ 32 2.2.4 GRADIENT UPDATING FOR POLYNOMIAL MEMBERSHIP FUNCTIONS WITH OVERLAP 1 2 AND B I J = C I J = M I J .......... 33 2.2.5 GRADIENT UPDATING FOR GAUSSIAN MEMBERSHIPFUNCTIONS............................. 34 2.2.6 ILLUSTRATIVEEXAMPLE ............................... 34 2.3 USINGCLUSTERINGANDGRADIENTDESCENT..................... 38 2.3.1 ALGORITHMFORMAMDANIMODELS ..................... 38 2.3.2 ALGORITHMFORTAKAGISUGENOMODELS ................ 39 2.3.3 ILLUSTRATIVEEXAMPLE ............................... 39 2.4 USINGEVOLUTIONARYSTRATEGIES............................. 46 XVI CONTENTS 2.5 GENERALIZATIONANDCONSEQUENCESESTIMATION................ 50 2.5.1 CONSEQUENCEINITIALIZATION.......................... 50 2.5.2 CONSEQUENCEESTIMATION ........................... 51 2.6 EXAMPLE OF AN INDUSTRIAL APPLICATION ...................... 55 2.7 CONCLUSIONS............................................. 58 3 FUZZY MODELING WITH LINGUISTIC INTEGRITY: A TOOL FOR DATA MINING ............................................... 59 3.1 INTRODUCTION ............................................ 59 3.2 STRUCTUREOFTHEFUZZYMODEL ............................. 61 3.3 THEAFRELIALGORITHM.................................. 63 3.4 THEFUZIONALGORITHM ................................... 68 3.5 EXAMPLES............................................... 71 3.5.1 MODELING A TWO-INPUT NONLINEAR FUNCTION ............ 71 3.5.2 MODELING OF A THREE-INPUT NONLINEAR FUNCTION ........ 77 3.5.3 PREDICTING CHAOTIC TIME SERIES ...................... 80 3.5.4 MODELING OF THE QUALITY PROPERTIES ON A HIGH-DENSITY POLYETHYLENE(HDPE)REACTOR ...................... 85 3.6 COMPLEXITYOFTHEAFRELIALGORITHM ..................... 89 3.7 CONCLUSIONS............................................. 89 4 NONLINEAR IDENTIFICATION USING FUZZY MODELS .............. 91 4.1 SYSTEMIDENTIFICATION .................................... 92 4.2 BASICSTRUCTUREOFTHEFUZZYSYSTEM....................... 93 4.3 EXPERIMENTDESIGNFORSYSTEMIDENTIFICATION................ 95 4.4 CHOOSINGTHEREGRESSORS.................................. 98 4.4.1 SEARCHMETHODS................................... 98 4.4.2 REGRESSORSEVALUATION..............................101 4.5 CHOOSINGTHESTRUCTURE...................................105 4.6 CALCULATINGTHEPARAMETERS...............................106 4.7 VALIDATION..............................................108 4.8 EXAMPLE: IDENTIFICATION OF THE BOX AND JENKINS GAS FURNACE DATA SET ...............................................109 4.9 IDENTIFICATION OF TAKAGISUGENO FUZZY MODELS USING LOCAL LINEARIDENTIFICATION .....................................118 4.10 CONCLUSIONS.............................................119 PART II FUZZY CONTROL 5 FUZZY CONTROL ............................................. 123 5.1 MODEL-FREEFUZZYCONTROL ................................124 5.1.1 HEURISTICTRIAL-AND-ERRORDESIGN ....................124 5.1.2 DESIGNOFPID-LIKEFUZZYCONTROLLERS.................124 5.2 MODELBASEDFUZZYCONTROL...............................129 CONTENTS XVII 5.2.1 USINGADAPTIVEMETHODS ...........................129 5.2.2 USINGDIRECTSYNTHESIS .............................134 5.3 CONCLUSIONSANDFUTUREPERSPECTIVES.......................146 6 PREDICTIVE CONTROL BASED ON FUZZY MODELS ................ 151 6.1 THEPREDICTIVECONTROLSTRATEGY...........................152 6.2 UNCONSTRAINEDNONLINEARPREDICTIVECONTROL ................154 6.2.1 ESTIMATION OF THE STEP RESPONSE TO CONSTRUCT G ( T ) ...157 6.2.2 EXAMPLE PREDICTIVE CONTROL OF A CSTR USING A FUZZY MODEL ...........................................158 6.3 CONSTRAINEDNONLINEARPREDICTIVECONTROL ..................167 6.3.1 THE CONSTRAINED NONLINEAR PREDICTIVE CONTROLPROBLEM ..................................168 6.3.2 APPROACHUSINGESTIMATEDSTEPRESPONSE............173 6.3.3 APPROACHUSINGTAKAGISUGENOFUZZYMODELS.........177 6.3.4 APPROACH USING TAKAGISUGENO FUZZY MODELS AND MULTIPLEMODELSINTHEPREDICTOR....................178 6.3.5 EXAMPLE PREDICTIVE CONTROL OF A STEAM GENERATOR USINGA FUZZYMODEL ..............................181 6.3.6 EXAMPLE: NONLINEAR PREDICTIVE CONTROL OF A GAS-PHASE HIGH-DENSITYPOLYETHYLENE(HDPE)REACTOR ..........187 6.4 CONCLUSIONS.............................................191 7 ROBUST NONLINEAR PREDICTIVE CONTROL USING FUZZY MODELS .............................................. 195 7.1 INTRODUCTION ............................................195 7.2 ROBUSTQUADRATICPROGRAMMING...........................196 7.3 PROBLEMDESCRIPTION.....................................198 7.4 NOMINALSOLUTION........................................199 7.5 FORMULATIONOFTHEMPCPROBLEMASA ROBUSTQP...........201 7.6 THECONTROLALGORITHM...................................202 7.7 UNCERTAINTYDESCRIPTIONINFUZZYMODELS...................202 7.7.1 LOCALUNCERTAINTYDESCRIBEDONEACHRULE ...........203 7.7.2 USINGTHEACTIVERULES.............................204 7.7.3 USINGALLTHERULES................................204 7.7.4 USINGTHEREACHABLESET............................205 7.8 CONCLUSIONSANDPERSPECTIVES .............................205 8 CONCLUSIONS AND FUTURE PERSPECTIVES ...................... 207 8.1 CONCLUSIONSANDSUMMARY................................207 8.2 PERSPECTIVESANDFUTUREWORK.............................210 PART III APPENDICES XVIII CONTENTS A FUZZY SET THEORY .......................................... 215 A.1 INTRODUCTION ............................................215 A.2 FUZZYSETS..............................................215 A.2.1 SOMEEXAMPLESOFMEMBERSHIPFUNCTIONS ............215 A.3 BASICDEFINITIONSOFFUZZYSETS............................216 A.3.1 SUPPORT ..........................................216 A.3.2 CORE.............................................216 A.3.3 HEIGHT ...........................................217 A.3.4 NORMALFUZZYSET .................................217 A.3.5 -CUT............................................217 A.3.6 STRICT * -CUT ......................................217 A.3.7 CONVEXITY ........................................217 A.4 OPERATIONSONFUZZYSETS.................................217 A.4.1 A IS CONTAINED IN B ...............................217 A.4.2 COMPLEMENT, NEGATION .............................217 A.4.3 INTERSECTION.......................................218 A.4.4 UNION............................................218 A.5 FUZZYRELATIONS..........................................219 A.5.1 PROJECTIONOFFUZZYRELATIONS.......................220 A.5.2 COMPOSITIONOFRELATIONS...........................220 A.6 APPROXIMATEREASONING..................................220 A.6.1 INTRODUCTION......................................220 A.6.2 LINGUISTICVARIABLES................................221 A.7 GENERALSTRUCTUREOFA FUZZYINFERENCESYSTEM ..............221 A.7.1 CONTROL RULES AS A KNOWLEDGE REPRESENTATION .........221 A.7.2 DEFUZZIFICATION....................................223 B CLUSTERING METHODS ....................................... 225 B.1 FUZZYC-MEANS[2].......................................225 B.2 USING FUZZY COVARIANCE MATRIX: GUSTAFSON AND KESSEL ALGORITHM[3] ...........................................226 B.3 MOUNTAINCLUSTERING[4] ..................................228 C GRADIENTS USED IN IDENTIFICATION WITH FUZZY MODELS ....... 231 C.1 GRADIENTFORTHESINGLETONCONSEQUENCES ...................231 C.1.1 WITHTRAPEZOIDALMEMBERSHIPFUNCTIONS.............232 C.1.2 WITHPOLYNOMIALMEMBERSHIPFUNCTIONS .............233 C.1.3 WITHGAUSSIANMEMBERSHIPFUNCTIONS ...............233 C.2 GRADIENTFORTHEPARAMETERSOFTHEMEM BERSHIPFUNCTIONS....234 C.2.1 WITHTRAPEZOIDALMEMBERSHIPFUNCTIONS.............234 C.2.2 WITHPOLYNOMIALMEMBERSHIPFUNCTIONS .............235 C.2.3 WITHGAUSSIANMEMBERSHIPFUNCTIONS ...............237 C.2.4 WITH TRIANGULAR MEMBERSHIP FUNCTIONS WITH 0.5 OVERLAP..........................................237 CONTENTS XIX C.2.5 WITH POLYNOMIAL MEMBERSHIP FUNCTIONS WITH 0.5 OVERLAP..........................................239 D DISCRETE LINEAR DYNAMICAL SYSTEM APPROXIMATION THEOREM ................................... 243 E FUZZY CONTROL FOR A CONTINUOUSLY VARIABLE TRANSMISSION ... 247 E.1 INTRODUCTIONANDPROCESSDESCRIPTION ......................247 E.2 PERFORMANCESPECIFICATIONS................................248 E.3 A PHYSICALMODELFORTHECVT............................249 E.4 DESIGNOFTHECONTROLLER..................................251 E.5 STABILITY ANALYSIS ........................................252 E.6 CONCLUSIONS.............................................253 REFERENCES ..................................................... 255 INDEX .......................................................... 261
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author	Espinosa, Jairo Vandewalle, Joos 1948- Wertz, Vincent
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illustrated	Illustrated
indexdate	2024-12-20T11:59:11Z
institution	BVB
isbn	1852338288
language	German
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-012835453
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owner	DE-703 DE-M49 DE-BY-TUM
owner_facet	DE-703 DE-M49 DE-BY-TUM
physical	XIX, 263 S. graph. Darst.
publishDate	2005
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publisher	Springer
record_format	marc
series2	Advances in Industrial Control
spellingShingle	Espinosa, Jairo Vandewalle, Joos 1948- Wertz, Vincent Fuzzy logic, identification and predictive control Fuzzy systems Predictive control Fuzzy-Regelung (DE-588)4395755-9 gnd Systemidentifikation (DE-588)4121753-6 gnd Fuzzy-Logik (DE-588)4341284-1 gnd Prädiktive Regelung (DE-588)4271836-3 gnd
subject_GND	(DE-588)4395755-9 (DE-588)4121753-6 (DE-588)4341284-1 (DE-588)4271836-3
title	Fuzzy logic, identification and predictive control
title_auth	Fuzzy logic, identification and predictive control
title_exact_search	Fuzzy logic, identification and predictive control
title_full	Fuzzy logic, identification and predictive control Jairo Espinosa ; Joseph Vandewalle ; Vincent Wertz
title_fullStr	Fuzzy logic, identification and predictive control Jairo Espinosa ; Joseph Vandewalle ; Vincent Wertz
title_full_unstemmed	Fuzzy logic, identification and predictive control Jairo Espinosa ; Joseph Vandewalle ; Vincent Wertz
title_short	Fuzzy logic, identification and predictive control
title_sort	fuzzy logic identification and predictive control
topic	Fuzzy systems Predictive control Fuzzy-Regelung (DE-588)4395755-9 gnd Systemidentifikation (DE-588)4121753-6 gnd Fuzzy-Logik (DE-588)4341284-1 gnd Prädiktive Regelung (DE-588)4271836-3 gnd
topic_facet	Fuzzy systems Predictive control Fuzzy-Regelung Systemidentifikation Fuzzy-Logik Prädiktive Regelung
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=012835453&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT espinosajairo fuzzylogicidentificationandpredictivecontrol AT vandewallejoos fuzzylogicidentificationandpredictivecontrol AT wertzvincent fuzzylogicidentificationandpredictivecontrol

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