Predictive approaches to control of complex systems:
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2013
|
| Schriftenreihe: | Studies in computational intelligence
454 |
| Schlagwörter: | |
| Links: | http://deposit.dnb.de/cgi-bin/dokserv?id=4116236&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025384245&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XII, 258 S. Ill., graph. Darst. |
| ISBN: | 9783642339462 |
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| 245 | 1 | 0 | |a Predictive approaches to control of complex systems |c Gorazd Karer ; Igor Skrjanc |
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Datensatz im Suchindex
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| adam_text | IMAGE 1
CONTENTS
PART I INTRODUCTION 1 INTRODUCTION 3
1.1 BOOK STRUCTURE 6
PART II MODELING OF COMPLEX SYSTEMS FOR PREDICTIVE CONTROL
2 HYBRID DYNAMICS II
2.1 GENERAL MATHEMATICAL FORMULATION 11
2.1.1 DYNAMICAL SYSTEM 11
2.1.2 GENERAL HYBRID DYNAMICAL SYSTEM 12
2.1.3 CONTROLLED GENERAL HYBRID DYNAMICAL SYSTEM 13
2.2 INTERLACING OF THE CONTINUOUS AND THE DISCRETE DYNAMICS 14
2.2.1 AUTONOMOUS SWITCHING 15
2.2.2 AUTONOMOUS JUMPS 15
2.2.3 CONTROLLED SWITCHING 16
2.2.4 CONTROLLED JUMPS 16
2.3 MODELING CONTRIBUTIONS FOR HYBRID SYSTEMS 17
2.3.1 WITSENHAUSEN S MODEL 18
2.4 MODELING OF HYBRID SYSTEMS FOR PREDICTIVE CONTROL IN A DISCRETE-TIME
SETTING 20
REFERENCES 21
3 PIECEWISE AFFINE AND EQUIVALENT MODELS 23
3.1 FORMULATIONS OF HYBRID SYSTEMS THAT ARE EQUIVALENT TO PIECEWISE
AFFINE SYSTEMS 23
3.1.1 PIECEWISE AFFINE SYSTEMS 24
3.1.2 MIXED LOGICAL-DYNAMICAL SYSTEMS 25
3.1.3 LINEAR COMPLEMENTARITY SYSTEMS 26
3.1.4 EXTENDED LINEAR COMPLEMENTARITY SYSTEMS 27
3.1.5 MAX-MIN-PLUS-SCALING SYSTEMS 28
HTTP://D-NB.INFO/102565174X
IMAGE 2
VIII CONTENTS
3.1.6 TRANSFORMATIONS AMONG FORMULATIONS OF MODELS 28
3.2 USABILITY OF PIECEWISE AFFINE AND EQUIVALENT MODELS 30
3.3 LIMITATIONS OF PIECEWISE AFFINE AND EQUIVALENT MODELS 30
REFERENCES 31
4 HYBRID FUZZY MODEL 33
4.1 NONLINEARITY AND FUZZY MODELS 33
4.1.1 TAKAGI-SUGENO FUZZY MODELS 33
4.2 MODELING OF A HYBRID FUZZY MODEL 35
4.2.1 HIERARCHY OF A HYBRID FUZZY MODEL 35
4.2.2 EXTENSION OF THE TAKAGI-SUGENO FUZZY MODEL FORMULATION TO
NONLINEAR HYBRID SYSTEMS 36
4.2.3 FORMULATION OF A HYBRID FUZZY MODEL 38
4.2.4 GENERALIZATION OF A THE HYBRID FUZZY MODEL 39
4.3 IDENTIFICATION OF A HYBRID FUZZY MODEL 40
4.3.1 FUZZY CLUSTERING 40
4.3.2 PROJECTIONS OF THE FUZZY CLUSTERS INTO THE INPUT SPACE OF THE
HYBRID FUZZY MODEL 42
4.3.3 GLOBAL LINEAR MODEL 45
4.3.4 PREPARATION OF THE DATA FOR ESTIMATION OF THE PARAMETERS OF THE
HYBRID FUZZY MODEL 45
4.3.5 ESTIMATION OF THE PARAMETERS OF THE HYBRID FUZZY MODEL BY MEANS OF
A MODIFIED LEAST-SQUARES METHOD 46
REFERENCES 47
5 UNSUPERVISED LEARNING METHODS FOR IDENTIFICATION OF COMPLEX SYSTEMS 49
5.1 INTRODUCTION TO UNSUPERVISED LEARNING METHODS 51
5.2 PRINCIPAL COMPONENT ANALYSIS 51
5.2.1 INVERTING A MATRIX WITH COLLINEAR DATA 55
5.3 FIELDS OF USE OF PRINCIPAL COMPONENT ANALYSIS AND REGRESSION .... 60
5.3.1 STRUCTURE IDENTIFICATION USING PRINCIPAL COMPONENT ANALYSIS 60
5.3.2 PARAMETER ESTIMATION USING PRINCIPAL COMPONENT ANALYSIS 61
5.3.3 CASE STUDY: STRUCTURE IDENTIFICATION USING OF A SECOND-ORDER
SYSTEM USING PRINCIPAL COMPONENT ANALYSIS 62 5.3.4 ONLINE PROCESS
SUPERVISION 64
5.4 FUZZY CLUSTERING WITH CLUSTER CENTER POINTS 75
5.4.1 FUZZY C-MEANS CLUSTERING ALGORITHM 75
5.4.2 GUSTAFSON-KESSEL CLUSTERING 84
5.4.3 CLUSTERING ALGORITHM BASED ON FUZZY MAXIMUM LIKELIHOOD ESTIMATES
89
5.5 CLUSTERING ALGORITHM USING LINEAR PROTOTYPES - A GENERALIZED
CLUSTERING METHOD 91
IMAGE 3
CONTENTS
IX
5.5.1 FUZZY C-VARIETIES CLUSTERING ALGORITHM 93
5.5.2 CLUSTERING ALGORITHM BASED ON FUZZY ELLIPSOIDS 97
REFERENCES 98
PART III MODELING AN IDENTIFICATION OF A BATCH REACTOR
6 BATCH REACTOR 101
6.1 STRUCTURE OF THE BATCH REACTOR 101
6.2 BASIC MATHEMATICAL MODEL OF THE BATCH REACTOR USING DIFFERENTIAL
EQUATIONS 102
6.2.1 THE LAW OF CONSERVATION OF ENERGY FOR THE CORE OF THE BATCH
REACTOR 104
6.2.2 THE LAW OF CONSERVATION OF ENERGY FOR THE WATER JACKET OF THE
BATCH REACTOR 104
REFERENCE 104
7 MODELING AND IDENTIFICATION OF THE BATCH REACTOR: THE PWA APPROACH 105
7.1 IDENTIFICATION DATA 105
7.2 PARTITIONING OF THE SYSTEM 107
7.3 TEMPERATURE IN THE CORE OF THE BATCH REACTOR 108
7.3.1 MODEL STRUCTURE 108
7.3.2 MODEL PARAMETERS 109
7.4 TEMPERATURE IN THE WATER JACKET OF THE BATCH REACTOR 110
7.4.1 PWA MODEL - APPROACH 1 110
7.4.2 PWA MODEL - APPROACH 2 113
7.4.3 PWA MODEL - APPROACH 3 114
7.4.4 PWA MODEL - APPROACH 4 120
7.5 VALIDATION 121
7.5.1 VALIDATION DATA 122
7.5.2 EXPERIMENTS 122
7.5.3 DISCUSSION 128
8 MODELING AND IDENTIFICATION OF THE BATCH REACTOR: THE HFM A P P R O A
C H 1 3 1
8.1 IDENTIFICATION DATA 131
8.2 PARTITIONING OF THE SYSTEM 131
8.3 TEMPERATURE IN THE CORE OF THE BATCH REACTOR 132
8.4 TEMPERATURE IN THE WATER JACKET OF THE BATCH REACTOR 132
8.4.1 HFM-APPROACH 1 133
8.4.2 HFM - APPROACH 2: FUZZY CLUSTERING 135
8.5 VALIDATION 138
8.5.1 VALIDATION DATA 138
8.5.2 EXPERIMENTS 139
8.5.3 DISCUSSION 142
IMAGE 4
X
CONTENTS
PART IV PREDICTIVE CONTROL OF COMPLEX SYSTEMS
9 INTRODUCTION TO PREDICTIVE CONTROL OF COMPLEX SYSTEMS 1 47
9.1 OPTIMAL CONTROL 148
9.1.1 HAMILTON-JACOBI-BELLMAN EQUATION 149
9.1.2 OPTIMAL CONTROL OF LINEAR SYSTEMS 150
9.2 PREDICTIVE CONTROL AS A SIMPLIFICATION OF A GENERAL OPTIMAL-CONTROL
PROBLEM 150
9.3 USE OF DISCRETE-TIME MODELS 151
9.4 MECHANISM OF THE PREDICTIVE-CONTROL ALGORITHMS 151
9.5 COST FUNCTION 152
9.6 COMPUTATIONAL COMPLEXITY OF THE PREDICTIVE-CONTROL PROBLEM 153 9.7
PREDICTIVE CONTROL OF COMPLEX SYSTEMS 154
REFERENCES 154
10 SOLVING MIXED-INTEGER OPTIMIZATION PROBLEMS 157
10.1 PIECEWISE AFFINE OR EQUIVALENT MODELS 157
10.2 ONLINE AND OFFLINE SOLVING OF THE OPTIMIZATION PROBLEM 157
10.3 MECHANISM OF THE PREDICTIVE-CONTROL ALGORITHM 158
10.3.1 POLYTOPES 158
10.3.2 MIXED-INTEGER OPTIMIZATION PROBLEMS 159
10.4 THE CURSE OF DIMENSIONALITY 163
10.5 CASE STUDY: IMPLEMENTATION ON THE BATCH REACTOR 164
10.5.1 MATHEMATICAL MODEL OF THE BATCH REACTOR 165
10.5.2 LIMITING THE SET OF THE POSSIBLE INPUT VECTOR VALUES 165
10.5.3 CONTROL 166
10.5.4 COST FUNCTION 166
10.5.5 DISCUSSION 166
REFERENCES 167
11 PREDICTIVE CONTROL BASED ON A REACHABILITY ANALYSIS 169
11.1 TREE OF EVOLUTION 169
11.1.1 DEVELOPMENT OF THE TREE OF EVOLUTION 169
11.1.2 CONDITIONS THAT ARE CHECKED IN EVERY NODE DURING THE EXPLORATION
OF THE TREE OF EVOLUTION 171
11.1.3 CONDITIONS THAT ARE CHECKED IN EVERY NODE DURING THE EXPLORATION
OF THE TREE OF EVOLUTION AFTER A CONDITION IS MET 172
11.2 REACHABILITY ANALYSIS 173
11.3 COST FUNCTION 173
11.3.1 CONDITION FOR COST-FUNCTION SUITABILITY 174
11.3.2 COST-FUNCTION FORM 174
1 1.4 COMPUTATIONAL COMPLEXITY 176
11.4.1 DECREASING THE COMPUTATIONAL COMPLEXITY BY APPLYING REACHABILITY
ANALYSIS 177
IMAGE 5
CONTENTS XI
11.4.2 DECREASING THE COMPUTATIONAL COMPLEXITY BY LIMITING
THE NUMBER OF POSSIBLE INPUT VECTORS 177
11.4.3 DECREASING THE COMPUTATIONAL COMPLEXITY BY HOLDING THE INPUTS
THROUGH A NUMBER OF TIME-STEPS 178
11.5 CASE STUDY: IMPLEMENTATION ON THE BATCH REACTOR 181
11.5.1 MATHEMATICAL MODEL OF THE BATCH REACTOR 181
11.5.2 LIMITING THE NUMBER OF POSSIBLE INPUT VECTORS 181
11.5.3 CONTROL 182
11.5.4 COST FUNCTION 182
11.5.5 RESULTS - APPROACH 1 182
11.5.6 RESULTS - APPROACH 2 184
11.5.7 COMPARISON BETWEEN PREDICTIVE CONTROL EMPLOYING A HYBRID FUZZY
MODEL AND A HYBRID LINEAR MODEL 186
11.5.8 DISCUSSION 189
REFERENCES 191
12 PREDICTIVE CONTROL BASED ON A GENETIC ALGORITHM 1 93
12.1 USE OF A GENETIC ALGORITHM FOR OPTIMIZATION PROBLEMS WITH DISCRETE
VARIABLES 193
12.2 OPTIMIZATION MECHANISM BASED ON A GENETIC ALGORITHM 194
12.3 GENETIC OPERATORS 194
12.3.1 CROSSOVER 195
12.3.2 MUTATION 196
12.4 SUBOPTIMALITY OF THE APPROACH 196
12.5 COST FUNCTION 197
12.6 COMPUTATIONAL COMPLEXITY 197
12.6.1 DECREASING THE COMPUTATIONAL COMPLEXITY BY LIMITING THE NUMBER OF
POSSIBLE INPUT VECTORS 199
12.6.2 DECREASING THE COMPUTATIONAL COMPLEXITY BY HOLDING THE INPUTS
THROUGH A NUMBER OF TIME-STEPS 200
12.7 CASE STUDY: IMPLEMENTATION ON THE BATCH REACTOR 202
12.7.1 MATHEMATICAL MODEL OF THE BATCH REACTOR 202
12.7.2 LIMITING THE NUMBER OF POSSIBLE INPUT VECTORS 202
12.7.3 CONTROL 203
12.7.4 COST FUNCTION 204
12.7.5 RESULTS 204
12.7.6 COMPARISON OF PREDICTIVE CONTROL ALGORITHMS BASED ON A GENETIC
ALGORITHM, A REACHABILITY ANALYSIS AND AN EXPLICIT ENUMERATION 208
12.7.7 DISCUSSION 212
REFERENCES 213
13 SELF-ADAPTIVE PREDICTIVE CONTROL WITH AN ONLINE LOCAL-LINEAR-MIODEL
IDENTIFICATION 215
13.1 TIME-VARYING DYNAMICAL CHARACTERISTICS 215
IMAGE 6
XII CONTENTS
13.2 SELF-ADAPTIVE PREDICTIVE CONTROL MECHANISM 216
13.2.1 LIENARIZED MODEL FORMULATION 216
13.2.2 PARAMETER ESTIMATION 217
13.2.3 PREDICTIVE FUNCTIONAL CONTROL ALGORITHM 219
13.3 CASE STUDY: IMPLEMENTATION ON THE BATCH REACTOR 224
13.3.1 MODIFIED MATHEMATICAL MODEL OF THE BATCH REACTOR 224
13.3.2 KINETIC MODEL OF THE EXOTHERMIC CHEMICAL REACTIONS 225 13.3.3
PARAMETER ESTIMATION 227
13.3.4 PREDICTIVE FUNCTIONAL CONTROL ALGORITHM 229
13.3.5 RESULTS 229
13.3.6 DISCUSSION 232
REFERENCES 234
14 CONTROL USING AN INVERSE HYBRID FUZZY MODEL 235
14.1 THE CONTROL SCHEME 235
14.2 INVERSE HYBRID FUZZY MODEL 236
14.3 THE FEEDFORWARD PART OF THE CONTROL ALGORITHM 238
14.3.1 REACHABILITY MATRIX OF THE HYBRID FUZZY MODEL 238
14.3.2 ADJUSTMENT OF THE REFERENCE SIGNAL 239
14.3.3 DETERMINATION OF THE DISCRETE PART OF THE FEEDFORWARD-CONTROL
SIGNAL 240
14.3.4 DETERMINATION OF THE CONTINUOUS PART OF THE FEEDFORWARD-CONTROL
SIGNAL 241
14.4 THE FEEDBACK PART OF THE CONTROL ALGORITHM 243
14.4.1 LINEARIZATION OF THE HIBRID FUZZY MODEL 243
14.4.2 H - STEP PREDICTION OF THE LINEARIZED MODEL 244
14.4.3 INCREMENTAL MODEL 244
14.4.4 DETERMINATION OF THE FEEDBACK-CONTROL SIGNAL 246
14.5 CASE STUDY: IMPLEMENTATION ON THE BATCH REACTOR 248
14.5.1 MATHEMATICAL MODEL OF THE BATCH REACTOR 248
14.5.2 CONTROL 248
14.5.3 RESULTS 249
14.5.4 DISCUSSION 250
REFERENCES 252
PART V CONCLUSION
15 CONCLUSION 255
A MODEL PARAMETERS AND CONTROL PARAMETERS 257
|
| any_adam_object | 1 |
| author | Karer, Gorazd Škrjanc, Igor |
| author_facet | Karer, Gorazd Škrjanc, Igor |
| author_role | aut aut |
| author_sort | Karer, Gorazd |
| author_variant | g k gk i š iš |
| building | Verbundindex |
| bvnumber | BV040538281 |
| classification_rvk | ST 300 |
| ctrlnum | (OCoLC)819807917 (DE-599)DNB102565174X |
| dewey-full | 629.8028563 629.8312 629.836 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
| dewey-raw | 629.8028563 629.8312 629.836 |
| dewey-search | 629.8028563 629.8312 629.836 |
| dewey-sort | 3629.8028563 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Informatik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
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| id | DE-604.BV040538281 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T16:17:14Z |
| institution | BVB |
| isbn | 9783642339462 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025384245 |
| oclc_num | 819807917 |
| open_access_boolean | |
| owner | DE-11 |
| owner_facet | DE-11 |
| physical | XII, 258 S. Ill., graph. Darst. |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
| record_format | marc |
| series | Studies in computational intelligence |
| series2 | Studies in computational intelligence |
| spellingShingle | Karer, Gorazd Škrjanc, Igor Predictive approaches to control of complex systems Studies in computational intelligence Unüberwachtes Lernen (DE-588)4580265-8 gnd Komplexes System (DE-588)4114261-5 gnd Dynamisches System (DE-588)4013396-5 gnd Adaptivregelung (DE-588)4000457-0 gnd Soft Computing (DE-588)4455833-8 gnd Prädiktive Regelung (DE-588)4271836-3 gnd |
| subject_GND | (DE-588)4580265-8 (DE-588)4114261-5 (DE-588)4013396-5 (DE-588)4000457-0 (DE-588)4455833-8 (DE-588)4271836-3 |
| title | Predictive approaches to control of complex systems |
| title_auth | Predictive approaches to control of complex systems |
| title_exact_search | Predictive approaches to control of complex systems |
| title_full | Predictive approaches to control of complex systems Gorazd Karer ; Igor Skrjanc |
| title_fullStr | Predictive approaches to control of complex systems Gorazd Karer ; Igor Skrjanc |
| title_full_unstemmed | Predictive approaches to control of complex systems Gorazd Karer ; Igor Skrjanc |
| title_short | Predictive approaches to control of complex systems |
| title_sort | predictive approaches to control of complex systems |
| topic | Unüberwachtes Lernen (DE-588)4580265-8 gnd Komplexes System (DE-588)4114261-5 gnd Dynamisches System (DE-588)4013396-5 gnd Adaptivregelung (DE-588)4000457-0 gnd Soft Computing (DE-588)4455833-8 gnd Prädiktive Regelung (DE-588)4271836-3 gnd |
| topic_facet | Unüberwachtes Lernen Komplexes System Dynamisches System Adaptivregelung Soft Computing Prädiktive Regelung |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=4116236&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025384245&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV020822171 |
| work_keys_str_mv | AT karergorazd predictiveapproachestocontrolofcomplexsystems AT skrjancigor predictiveapproachestocontrolofcomplexsystems |