Modelling and control for intelligent industrial systems: adaptive algorithms in robotics and industrial engineering
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2011
|
| Schriftenreihe: | Intelligent systems reference library
7 |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | Literaturverz. S. [351] - 375 |
| Beschreibung: | XXVIII, 379 S. graph. Darst. |
| ISBN: | 9783642178740 364217874X |
Internformat
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| 100 | 1 | |a Rigatos, Gerasimos G. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Modelling and control for intelligent industrial systems |b adaptive algorithms in robotics and industrial engineering |c Gerasimos G. Rigatos |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2011 | |
| 300 | |a XXVIII, 379 S. |b graph. Darst. | ||
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| 490 | 1 | |a Intelligent systems reference library |v 7 | |
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Datensatz im Suchindex
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IMAGE 1
CONTENTS
INDUSTRIAL ROBOTS IN CONTACT-FREE OPERATION 1
1.1 DYNAMIC ANALYSIS OF RIGID LINK ROBOTS 1
1.2 KINEMATIC ANALYSIS OF RIGID LINK ROBOTS 5
1.3 DYNAMIC ANALYSIS OF FLEXIBLE-LINK ROBOTS 8
1.4 KINEMATIC ANALYSIS OF FLEXIBLE-LINK ROBOTS 9
1.5 CONTROL OF RIGID-LINK ROBOTS IN CONTACT-FREE OPERATION . . 13 1.6
CONTROL OF FLEXIBLE-LINK ROBOTS IN CONTACT-FREE OPERATION 14
1.6.1 INVERSE DYNAMICS CONTROL OF FLEXIBLE-LINK ROBOTS . . 14 1.6.2
ENERGY-BASED CONTROL OF FLEXIBLE LINK ROBOTS 16 1.6.3 ADAPTIVE NEURAL
CONTROL OF FLEXIBLE MANIPULATORS . . 19 1.6.4 APPROXIMATION OF THE
FLEXIBLE-LINKS DYNAMICS 22 1.7 SIMULATION OF FLEXIBLE-LINK ROBOT CONTROL
25
1.7.1 MODEL-BASED CONTROL OF FLEXIBLE-LINK ROBOTS 25 1.7.2 ENERGY-BASED
CONTROL 26
1.7.3 ADAPTIVE NEURAL CONTROL 28
INDUSTRIAL ROBOTS IN COMPLIANCE TASKS 31
2.1 IMPEDANCE CONTROL 31
2.2 HYBRID POSITION/FORCE CONTROL 34
2.2.1 STIFFNESS IDENTIFICATION IN COMPLIANCE TASKS 35 2.2.2 APPLICATION
OF ROBOT HYBRID POSITION/FORCE CONTROL 37
2.3 FORCE CONTROL OF FLEXIBLE-LINK ROBOTS 39
2.3.1 INTERACTION WITH THE COMPLIANT SURFACE 39
2.3.2 FORCE CONTROL FOR FLEXIBLE-LINK ROBOTS 40
2.4 SIMULATION OF FORCE CONTROL FOR FLEXIBLE-LINK ROBOTS 41
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1009480987
DIGITALISIERT DURCH
IMAGE 2
XXII CONTENTS
3 MOBILE ROBOTS AND AUTONOMOUS VEHICLES 45
3.1 KINEMATIC ANALYSIS OF MOBILE ROBOTS 45
3.2 CONTROL OF AUTONOMOUS GROUND VEHICLES 46
3.2.1 DIFFERENTIAL FLATNESS FOR FINITE DIMENSIONAL SYSTEMS 47
3.2.2 FLATNESS-BASED CONTROL OF THE AUTONOMOUS VEHICLE 48
3.3 KINEMATIC AND DYNAMIC MODELS OF SURFACE VESSELS 51 3.3.1 A GENERIC
KINEMATIC AND DYNAMIC SHIP MODEL 51 3.3.2 MODELS OF CURRENT, WIND AND
WAVE FORCES 53
3.3.3 SHIP MODEL FOR THE DYNAMIC POSITIONING PROBLEM . . 54 3.3.4 SHIP
ACTUATOR MODEL 54
3.4 FEEDBACK LINEARIZATION FOR SHIP DYNAMIC POSITIONING 55 3.4.1 SHIP
CONTROL USING DYNAMIC FEEDBACK LINEARIZATION 55
3.4.2 ESTIMATION OF THE UNKNOWN ADDITIVE DISTURBANCES 56
3.5 BACKSTEPPING CONTROL FOR THE SHIP STEERING PROBLEM 57 3.5.1 THE SHIP
STEERING PROBLEM 57
3.5.2 NONLINEAR BACKSTEPPING 59
3.5.3 AUTOMATED SHIP STEERING USING BACKSTEPPING CONTROL 60
3.5.4 CALCULATION OF THE SISO BACKSTEPPING NONLINEAR CONTROLLER 61
4 ADAPTIVE CONTROL METHODS FOR INDUSTRIAL SYSTEMS 65
4.1 ADAPTIVE CONTROL OF INDUSTRIAL SYSTEMS WITH FULL STATE FEEDBACK 65
4.1.1 PROBLEM STATEMENT 65
4.1.2 TRANSFORMATION TO A REGULATION PROBLEM 67
4.1.3 APPROXIMATORS OF UNKNOWN SYSTEM DYNAMICS 68 4.1.4 LYAPUNOV
STABILITY ANALYSIS IN THE CASE OF FULL STATE FEEDBACK 69
4.2 ADAPTIVE CONTROL OF INDUSTRIAL SYSTEMS WITH OUTPUT FEEDBACK 71
4.2.1 TRANSFORMATION TO A REGULATION PROBLEM 71
4.2.2 APPROXIMATION OF UNKNOWN SYSTEM DYNAMICS 72 4.2.3 LYAPUNOV
STABILITY ANALYSIS IN THE CASE OF OUTPUT FEEDBACK 74
4.2.4 RICCATI EQUATION COEFFICIENTS AND #OO CONTROL ROBUSTNESS 76
4.3 APPLICATION TO THE CONTROL OF ELECTRIC MOTORS 77
4.3.1 THE DC MOTOR MODEL 77
4.3.2 STATE FEEDBACK CONTROLLER OF THE DC MOTOR MODEL. 79 4.3.3 STATE
FEEDBACK CONTROLLER FOR THE DC MOTOR 81
IMAGE 3
CONTENTS XXIII
4.3.4 OUTPUT FEEDBACK CONTROLLER FOR THE DC MOTOR 85 4.3.5 APPLICATION
TO THE FIELD-ORIENTED INDUCTION MOTOR 89
4.4 APPLICATION TO THE SHIP STEERING CONTROL PROBLEM 93
4.5 APPLICATION TO THE STABILIZATION OF ELECTROMECHANICAL SYSTEMS 96
5 ROBUST CONTROL METHODS FOR INDUSTRIAL SYSTEMS 101
5.1 ROBUST CONTROL WITH SLIDING-MODE CONTROL THEORY 101 5.1.1
SLIDING-MODE CONTROL 101
5.1.2 AN APPLICATION EXAMPLE OF SLIDING-MODE CONTROL . . 104 5.1.3
SLIDING-MODE CONTROL WITH BOUNDARY LAYER 105 5.2 ROBUST CONTROL WITH
INTERVAL POLYNOMIALS THEORY 107
5.2.1 BASICS OF KHARITONOV'S THEORY 107
5.2.2 EXTREMAL PROPERTIES OF KHARITONOV POLYNOMIALS 109 5.3 APPLICATION
TO THE STABILIZATION OF ELECTRIC POWER SYSTEMS 110
5.3.1 THE PROBLEM OF POWER SYSTEM STABILIZATION I LL
5.3.2 TRANSFER FUNCTION OF THE SINGLE-MACHINE INFINITE-BUS MODEL 113
5.3.3 KHARITONOV'S THEORY FOR POWER SYSTEM STABILIZATION 113
6 FILTERING AND ESTIMATION METHODS FOR INDUSTRIAL SYSTEMS 119
6.1 LINEAR STATE OBSERVERS 119
6.2 THE CONTINUOUS-TIME KAIMAN FILTER FOR LINEAR MODELS . . . 120 6.3
THE DISCRETE-TIME KAIMAN FILTER FOR LINEAR SYSTEMS 121 6.4 THE EXTENDED
KAIMAN FILTER FOR NONLINEAR SYSTEMS 122 6.5 SIGMA-POINT KAIMAN FILTERS
124
6.6 PARTICLE FILTERS 127
6.6.1 THE PARTICLE APPROXIMATION OF PROBABILITY DISTRIBUTIONS 127
6.6.2 THE PREDICTION STAGE 128
6.6.3 THE CORRECTION STAGE 128
6.6.4 THE RESAMPLING STAGE 130
6.6.5 APPROACHES TO THE IMPLEMENTATION OF RESAMPLING . . 130 6.7
APPLICATION OF ESTIMATION METHODS TO INDUSTRIAL SYSTEMS CONTROL 133
6.7.1 KAIMAN FILTER-BASED CONTROL OF ELECTRIC MOTORS 133 6.7.2 EXTENDED
KAIMAN FILTER-BASED CONTROL OF ELECTRIC MOTORS 134
6.7.3 UNSCENTED KAIMAN FILTER-BASED CONTROL OF ELECTRIC MOTORS 137
6.7.4 PARTICLE FILTER-BASED CONTROL OF ELECTRIC MOTORS 138
IMAGE 4
XXIV CONTENTS
7 SENSOR FUSION-BASED CONTROL FOR INDUSTRIAL SYSTEMS 141 7.1 SENSOR
FUSION-BASED CONTROL OF INDUSTRIAL ROBOTS 141 7.1.1 THE SENSOR FUSION
PROBLEM 141
7.1.2 APPLICATION OF EKF AND PF FOR SENSOR FUSION 143 7.1.3 SIMULATION
OF EKF AND PF-BASED SENSOR FUSION FOR INDUSTRIAL ROBOT CONTROL 145
7.2 SENSOR FUSION-BASED CONTROL FOR MOBILE ROBOTS 155 7.2.1 SIMULATION
OF EKF-BASED CONTROL FOR MOBILE ROBOTS 155
7.2.2 SIMULATION OF PARTICLE FILTER-BASED MOBILE ROBOT CONTROL 161
7.2.3 SIMULATION OF EKF AND PF-BASED PARALLEL PARKING CONTROL 162
7.2.4 PERFORMANCE ANALYSIS OF EKF AND PF-BASED MOBILE ROBOT CONTROL 163
7.3 SENSOR FUSION-BASED DYNAMIC SHIP POSITIONING 165 7.3.1 EKF AND
PF-BASED SENSOR FUSION FOR THE SHIP MODEL 165
7.3.2 SIMULATION OF EKF AND PF-BASED SHIP DYNAMIC POSITIONING 168
8 DISTRIBUTED FILTERING AND ESTIMATION FOR INDUSTRIAL SYSTEMS 175
8.1 THE PROBLEM OF DISTRIBUTED STATE ESTIMATION OVER SENSOR NETWORKS 175
8.2 DISTRIBUTED EXTENDED KAIMAN FILTERING 177
8.2.1 CALCULATION OF LOCAL EXTENDED KAIMAN FILTER ESTIMATIONS 177
8.2.2 EXTENDED INFORMATION FILTERING FOR STATE ESTIMATES FUSION 180
8.3 DISTRIBUTED SIGMA-POINT KAIMAN FILTERING 181
8.3.1 CALCULATION OF LOCAL UNSCENTED KAIMAN FILTER ESTIMATIONS 181
8.3.2 UNSCENTED INFORMATION FILTERING FOR STATE ESTIMATES FUSION 185
8.4 DISTRIBUTED PARTICLE FILTER 186
8.4.1 DISTRIBUTED PARTICLE FILTERING FOR STATE ESTIMATION FUSION 186
8.4.2 FUSION OF THE LOCAL PROBABILITY DENSITY FUNCTIONS . . 188 8.5
SIMULATION TESTS 190
8.5.1 MULTI-UAV CONTROL WITH EXTENDED INFORMATION FILTERING 190
8.5.2 MULTI-UAV CONTROL WITH DISTRIBUTED PARTICLE FILTERING 194
IMAGE 5
CONTENTS XXV
9 FAULT DETECTION AND ISOLATION FOR INDUSTRIAL SYSTEMS 197 9.1 FAULT
DIAGNOSIS WITH STATISTICAL METHODS 197
9.1.1 RESIDUAL GENERATION THROUGH NONLINEAR SYSTEM MODELLING 197
9.1.2 DETERMINATION OF THE NONLINEAR MODEL'S STRUCTURE . . 199 9.1.3
STAGES OF NONLINEAR SYSTEMS MODELING 202
9.2 FAULT THRESHOLD SELECTION WITH THE GENERALIZED LIKELIHOOD RATIO 203
9.2.1 THE LOCAL STATISTICAL APPROACH TO FAULT DIAGNOSIS . . 203 9.2.2
FAULT DETECTION WITH THE LOCAL STATISTICAL APPROACH 204
9.2.3 FAULT ISOLATION WITH THE LOCAL STATISTICAL APPROACH 206
9.2.4 FAULT THRESHOLD FOR RESIDUALS OF UNKNOWN DISTRIBUTION 208
10 APPLICATION OF FAULT DIAGNOSIS TO INDUSTRIAL SYSTEMS 213 10.1 FAULT
DIAGNOSIS OF THE ELECTRIC POWER SYSTEM 213
10.1.1 CASCADING EVENTS IN THE ELECTRIC POWER GRID 213 10.1.2 ELECTRIC
POWER SYSTEMS DYNAMICS 216
10.1.3 THE MULTI-AREA MULTI-MACHINE ELECTRIC POWER SYSTEM 217
10.1.4 NONLINEAR MODELING OF THE ELECTRIC POWER SYSTEM. 219 10.2
FAULT DIAGNOSIS TESTS FOR THE ELECTRIC POWER SYSTEM 221 10.2.1
PARAMETERS OF THE NONLINEAR POWER SYSTEM MODEL . . 221
10.2.2 EFFICIENCY OF THE FAULT DIAGNOSIS METHOD 222 10.3 FAULT DIAGNOSIS
OF ELECTRIC MOTORS 224
10.3.1 FAILURES IN ROTATING ELECTRICAL MACHINES 224 10.3.2 FAULTS IN THE
DC MOTOR CONTROL LOOP 225
10.3.3 RESIDUAL GENERATION WITH THE USE OF KAIMAN FILTERING 225
10.3.4 RESIDUAL GENERATION WITH THE USE OF PARTICLE FILTERING 226
10.3.5 FAULT DIAGNOSIS IN CONTROL LOOPS 228
11 OPTIMIZATION METHODS FOR MOTION PLANNING OF MULTI-ROBOT SYSTEMS 231
11.1 DISTRIBUTED GRADIENT FOR MOTION PLANNING OF MULTI-ROBOT SYSTEMS 231
11.1.1 APPROACHES TO MULTI-ROBOT MOTION PLANNING 231 11.1.2 THE
DISTRIBUTED GRADIENT ALGORITHM 233
11.1.3 KINEMATIC MODEL OF THE MULTI-ROBOT SYSTEM 233 11.1.4 COHESION OF
THE MULTI-ROBOT SYSTEM 235
11.1.5 CONVERGENCE TO THE GOAL POSITION 237
11.1.6 STABILITY ANALYSIS USING LA SALLE'S THEOREM 237
IMAGE 6
XXVI CONTENTS
11.2 PARTICLE SWARM THEORY FOR MULTI-ROBOT MOTION PLANNING . . 239
11.2.1 THE PARTICLE SWARM THEORY 239
11.2.2 STABILITY OF THE PARTICLE SWARM ALGORITHM 240 11.3 EVALUATION
TESTS FOR THE STOCHASTIC SEARCH ALGORITHMS 242 11.3.1 CONVERGENCE
TOWARDS THE EQUILIBRIUM 242
11.3.2 TUNING OF THE STOCHASTIC SEARCH ALGORITHMS 249
12 OPTIMIZATION METHODS FOR TARGET TRACKING BY MULTI-ROBOT SYSTEMS 253
12.1 DISTRIBUTED MOTION PLANNING AND FILTERING IN MULTI-ROBOT SYSTEMS
253
12.1.1 TARGET TRACKING IN MOBILE SENSORS NETWORKS 253 12.1.2 THE PROBLEM
OF DISTRIBUTED TARGET TRACKING 255 12.1.3 TRACKING OF THE REFERENCE PATH
BY THE TARGET 257
12.1.4 CONVERGENCE OF THE MULTI-ROBOT SYSTEM TO THE TARGET 258
12.2 SIMULATION TESTS 259
12.2.1 TARGET TRACKING USING EXTENDED INFORMATION FILTERING 259
12.2.2 TARGET TRACKING USING UNSCENTED INFORMATION FILTERING 262
13 OPTIMIZATION METHODS FOR INDUSTRIAL AUTOMATION 269 13.1
MULTI-OBJECTIVE OPTIMIZATION FOR INDUSTRIAL AUTOMATION . . . 269 13.1.1
THE WAREHOUSE REPLENISHMENT PROBLEM 269 13.1.2 MULTI-OBJECTIVE
OPTIMIZATION PROBLEMS 270
13.1.3 THE PARETO-OPTIMALITY PRINCIPLES 270
13.1.4 REPLENISHMENT AS A PARETO OPTIMIZATION PROBLEM . . 272 13.1.5
APPROACHES TO OBTAIN PARETO-OPTIMAL SOLUTIONS 272 13.1.6 GRAPHICAL
REPRESENTATION OF PARETO OPTIMAL SOLUTION 275
13.2 GENETIC ALGORITHMS IN THE SEARCH OF PARETO-OPTIMAL SOLUTIONS 275
13.2.1 BASIC PRINCIPLES OF EVOLUTIONARY ALGORITHMS 275 13.2.2
EVOLUTIONARY ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION 276
13.2.3 CONTROL OF DIVERSITY OF THE PARETO-OPTIMAL SOLUTIONS 277
13.2.4 GENETIC ALGORITHM CONVERGENCE TO PARETO-OPTIMAL SOLUTIONS 280
13.3 A GENETIC ALGORITHM FOR THE WAREHOUSE REPLENISHMENT TASK 280
13.3.1 CONSTRAINTS OF GENETIC ALGORITHMS IN ORDERING PROBLEMS 280
IMAGE 7
CONTENTS XXVII
13.3.2 THE GENETIC ALGORITHM FOR REPLENISHMENT OPTIMIZATION 282
13.3.3 MATING PROCEDURE 282
13.3.4 MUTATION PROCEDURE 284
13.3.5 DEFINITION AND TUNING OF THE COST FUNCTION 285
13.4 RESULTS ON GENETIC ALGORITHM-BASED WAREHOUSE OPTIMIZATION 286
13.4.1 COST FUNCTION TUNING THROUGH WEIGHTS SELECTION 286 13.4.2
EVALUATION OF THE GENETIC ALGORITHM PERFORMANCE . . 291
14 MACHINE LEARNING METHODS FOR INDUSTRIAL SYSTEMS CONTROL 293
14.1 MODEL-FREE CONTROL OF FLEXIBLE-LINK ROBOTS 293
14.1.1 APPROACHES FOR MODEL-BASED CONTROL OF FLEXIBLE-LINK ROBOTS 293
14.1.2 APPROACHES FOR MODEL-FREE CONTROL OF FLEXIBLE-LINK ROBOTS 295
14.1.3 NEURAL CONTROL USING MULTI-FREQUENCY BASIS FUNCTIONS 296
14.2 NEURAL CONTROL USING WAVELET BASIS FUNCTIONS 297
14.2.1 WAVELET FRAMES 297
14.2.2 DYADIC GRID SCALING AND ORTHONORMAL WAVELET TRANSFORMS 298
14.2.3 THE SCALING FUNCTION AND THE MULTI-RESOLUTION REPRESENTATION 299
14.2.4 EXAMPLES OF ORTHONORMAL WAVELETS 300
14.2.5 THE HAAR WAVELET 301
14.3 NEURAL NETWORKS USING HERMITE ACTIVATION FUNCTIONS 302 14.3.1
IDENTIFICATION WITH FEED-FORWARD NEURAL NETWORKS . . 302 14.3.2 THE
GAUSS-HERMITE SERIES EXPANSION 304
14.3.3 NEURAL NETWORKS USING 2D HERMITE ACTIVATION FUNCTIONS 306
14.4 RESULTS ON FLEXIBLE-LINK CONTROL AND VIBRATIONS SUPPRESSION 308
14.4.1 THE FLEXIBLE-LINK ROBOT MODEL 308
14.4.2 CONTROL USING HERMITE POLYNOMIAL-BASED NEURAL NETWORKS 308
15 MACHINE LEARNING METHODS FOR INDUSTRIAL SYSTEMS FAULT DIAGNOSIS 313
15.1 AUTOMATA IN FAULT DIAGNOSIS TASKS 313
15.1.1 FAULT DIAGNOSIS OF SYSTEMS WITH EVENT-DRIVEN DYNAMICS 313
15.1.2 SYSTEM MODELLING WITH THE USE OF FINITE AUTOMATA 314
IMAGE 8
XXVIII CONTENTS
15.1.3 SYSTEM MODELLING WITH THE USE OF FUZZY AUTOMATA 316
15.1.4 MONITORING SIGNALS WITH THE USE OF FUZZY AUTOMATA 317
15.2 A FAULT DIAGNOSIS APPROACH BASED ON FUZZY AUTOMATA . . . 318
15.2.1 GENERATION OF THE TEMPLATES STRING 318
15.2.2 SYNTACTIC ANALYSIS USING FUZZY AUTOMATA 319 15.2.3 DETECTION OF
FAULT PATTERNS BY THE FUZZY AUTOMATA 322
15.3 SIMULATION TESTS OF FAULT DIAGNOSIS WITH FUZZY AUTOMATA 324
16 APPLICATIONS OF MACHINE VISION TO INDUSTRIAL S Y S T E M S . . . 327
16.1 MACHINE VISION AND IMAGING TRANSFORMATIONS 327
16.1.1 SOME BASIC TRANSFORMATIONS 327
16.1.2 PERSPECTIVE TRANSFORMATION 329
16.1.3 CAMERA MODEL 334
16.1.4 CAMERA CALIBRATION 336
16.1.5 STEREO IMAGING 337
16.2 MULTI CAMERAS-BASED VISUAL SERVOING FOR INDUSTRIAL ROBOTS 339
16.3 DISTRIBUTED FILTERING FOR SENSORLESS CONTROL 340
16.3.1 VISUAL SERVOING OVER A NETWORK OF SYNCHRONIZED CAMERAS 340
16.3.2 ROBOT'S STATE ESTIMATION THROUGH DISTRIBUTED FILTERING 341
16.4 DISTRIBUTED STATE ESTIMATION USING THE EIF 342
16.4.1 LOCAL STATE ESTIMATION WITH EXTENDED KAIMAN FILTERING 342
16.4.2 STATE ESTIMATION THROUGH A NONLINEAR TRANSFORMATION 344
16.4.3 DERIVATIVE-FREE KAIMAN FILTERING FOR NONLINEAR SYSTEMS 345
16.4.4 FUSING ESTIMATIONS FROM LOCAL DISTRIBUTED FILTERS . . 345 16.4.5
CALCULATION OF THE AGGREGATE STATE ESTIMATION 347 16.5 SIMULATION TESTS
OF THE VISION-BASED CONTROL SYSTEM 347 16.5.1 DYNAMICS AND CONTROL OF
THE ROBOTIC
MANIPULATOR 347
16.5.2 EVALUATION OF RESULTS ON VISION-BASED CONTROL 348 REFERENCES 351
INDEX 377 |
| any_adam_object | 1 |
| author | Rigatos, Gerasimos G. |
| author_facet | Rigatos, Gerasimos G. |
| author_role | aut |
| author_sort | Rigatos, Gerasimos G. |
| author_variant | g g r gg ggr |
| building | Verbundindex |
| bvnumber | BV037487935 |
| classification_rvk | ZQ 6250 |
| classification_tum | FER 982f |
| ctrlnum | (OCoLC)707101398 (DE-599)DNB1009480987 |
| dewey-full | 670.427 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 670 - Manufacturing |
| dewey-raw | 670.427 |
| dewey-search | 670.427 |
| dewey-sort | 3670.427 |
| dewey-tens | 670 - Manufacturing |
| discipline | Fertigungstechnik Werkstoffwissenschaften / Fertigungstechnik Elektrotechnik / Elektronik / Nachrichtentechnik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Book |
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| id | DE-604.BV037487935 |
| illustrated | Illustrated |
| indexdate | 2024-07-20T11:11:49Z |
| institution | BVB |
| isbn | 9783642178740 364217874X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-022639340 |
| oclc_num | 707101398 |
| open_access_boolean | |
| owner | DE-573 DE-91 DE-BY-TUM |
| owner_facet | DE-573 DE-91 DE-BY-TUM |
| physical | XXVIII, 379 S. graph. Darst. |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Springer |
| record_format | marc |
| series | Intelligent systems reference library |
| series2 | Intelligent systems reference library |
| spelling | Rigatos, Gerasimos G. Verfasser aut Modelling and control for intelligent industrial systems adaptive algorithms in robotics and industrial engineering Gerasimos G. Rigatos Berlin [u.a.] Springer 2011 XXVIII, 379 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Intelligent systems reference library 7 Literaturverz. S. [351] - 375 Mobiler Roboter (DE-588)4191911-7 gnd rswk-swf Fehlererkennung (DE-588)4133764-5 gnd rswk-swf Industrieroboter (DE-588)4026861-5 gnd rswk-swf Autonomes Fahrzeug (DE-588)7714938-5 gnd rswk-swf Adaptivregelung (DE-588)4000457-0 gnd rswk-swf Prozessoptimierung (DE-588)4176074-8 gnd rswk-swf Robuste Regelung (DE-588)4206985-3 gnd rswk-swf Fertigungstechnik (DE-588)4329079-6 gnd rswk-swf Automation (DE-588)4003957-2 gnd rswk-swf Zustandsschätzung (DE-588)4129826-3 gnd rswk-swf Fertigungstechnik (DE-588)4329079-6 s Automation (DE-588)4003957-2 s Industrieroboter (DE-588)4026861-5 s Mobiler Roboter (DE-588)4191911-7 s Autonomes Fahrzeug (DE-588)7714938-5 s Zustandsschätzung (DE-588)4129826-3 s Fehlererkennung (DE-588)4133764-5 s Prozessoptimierung (DE-588)4176074-8 s Robuste Regelung (DE-588)4206985-3 s Adaptivregelung (DE-588)4000457-0 s DE-604 Intelligent systems reference library 7 (DE-604)BV035704685 7 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3607965&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022639340&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rigatos, Gerasimos G. Modelling and control for intelligent industrial systems adaptive algorithms in robotics and industrial engineering Intelligent systems reference library Mobiler Roboter (DE-588)4191911-7 gnd Fehlererkennung (DE-588)4133764-5 gnd Industrieroboter (DE-588)4026861-5 gnd Autonomes Fahrzeug (DE-588)7714938-5 gnd Adaptivregelung (DE-588)4000457-0 gnd Prozessoptimierung (DE-588)4176074-8 gnd Robuste Regelung (DE-588)4206985-3 gnd Fertigungstechnik (DE-588)4329079-6 gnd Automation (DE-588)4003957-2 gnd Zustandsschätzung (DE-588)4129826-3 gnd |
| subject_GND | (DE-588)4191911-7 (DE-588)4133764-5 (DE-588)4026861-5 (DE-588)7714938-5 (DE-588)4000457-0 (DE-588)4176074-8 (DE-588)4206985-3 (DE-588)4329079-6 (DE-588)4003957-2 (DE-588)4129826-3 |
| title | Modelling and control for intelligent industrial systems adaptive algorithms in robotics and industrial engineering |
| title_auth | Modelling and control for intelligent industrial systems adaptive algorithms in robotics and industrial engineering |
| title_exact_search | Modelling and control for intelligent industrial systems adaptive algorithms in robotics and industrial engineering |
| title_full | Modelling and control for intelligent industrial systems adaptive algorithms in robotics and industrial engineering Gerasimos G. Rigatos |
| title_fullStr | Modelling and control for intelligent industrial systems adaptive algorithms in robotics and industrial engineering Gerasimos G. Rigatos |
| title_full_unstemmed | Modelling and control for intelligent industrial systems adaptive algorithms in robotics and industrial engineering Gerasimos G. Rigatos |
| title_short | Modelling and control for intelligent industrial systems |
| title_sort | modelling and control for intelligent industrial systems adaptive algorithms in robotics and industrial engineering |
| title_sub | adaptive algorithms in robotics and industrial engineering |
| topic | Mobiler Roboter (DE-588)4191911-7 gnd Fehlererkennung (DE-588)4133764-5 gnd Industrieroboter (DE-588)4026861-5 gnd Autonomes Fahrzeug (DE-588)7714938-5 gnd Adaptivregelung (DE-588)4000457-0 gnd Prozessoptimierung (DE-588)4176074-8 gnd Robuste Regelung (DE-588)4206985-3 gnd Fertigungstechnik (DE-588)4329079-6 gnd Automation (DE-588)4003957-2 gnd Zustandsschätzung (DE-588)4129826-3 gnd |
| topic_facet | Mobiler Roboter Fehlererkennung Industrieroboter Autonomes Fahrzeug Adaptivregelung Prozessoptimierung Robuste Regelung Fertigungstechnik Automation Zustandsschätzung |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3607965&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=022639340&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV035704685 |
| work_keys_str_mv | AT rigatosgerasimosg modellingandcontrolforintelligentindustrialsystemsadaptivealgorithmsinroboticsandindustrialengineering |