Neural network control of robot manipulators and nonlinear systems:
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
London [u.a.]
Taylor & Francis
1999
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXIII, 442 S. graph. Darst. |
| ISBN: | 0748405968 |
Staff View
MARC
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| 100 | 1 | |a Lewis, Frank L. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Neural network control of robot manipulators and nonlinear systems |c F. L. Lewis ; S. Jagannathan ; A. Yes̨ildirek |
| 264 | 1 | |a London [u.a.] |b Taylor & Francis |c 1999 | |
| 300 | |a XXIII, 442 S. |b graph. Darst. | ||
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Record in the Search Index
| _version_ | 1804127676488744960 |
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| adam_text | NEURAL NETWORK CONTROL OF ROBOT MANIPULATORS AND NONLINEAR SYSTEMS F.L.
LEWIS AUTOMATION AND ROBOTICS RESEARCH INSTITUTE THE UNIVERSITY OF TEXAS
AT ARLINGTON S. JAG ANNATHAN SYSTEMS AND CONTROLS RESEARCH CATERPILLAR,
INC., MOSSVILLE A. YE§ILDIREK MANAGER, NEW PRODUCT DEVELOPMENT DEPSA,
PANAMA CITY *8 - ^ CONTENTS LIST OF TABLES OF DESIGN EQUATIONS XI LIST
OF FIGURES XVIII SERIES INTRODUCTION XIX PREFACE XXI 1 BACKGROUND ON
NEURAL NETWORKS 1 1.1 NEURAL NETWORK TOPOLOGIES AND RECALL 2 1.1.1
NEURON MATHEMATICAL MODEL 2 1.1.2 MULTILAYER PERCEPTRON 7 1.1.3
LINEAR-IN-THE-PARAMETER (LIP) NEURAL NETS 10 1.1.4 DYNAMIC NEURAL
NETWORKS 14 1.2 PROPERTIES OF NEURAL NETWORKS 24 1.2.1 CLASSIFICATION,
ASSOCIATION, AND PATTERN RECOGNITION 25 1.2.2 FUNCTION APPROXIMATION 30
1.3 NEURAL NETWORK WEIGHT SELECTION AND TRAINING . 32 1.3.1 DIRECT
COMPUTATION OF THE WEIGHTS 33 1.3.2 TRAINING THE ONE-LAYER NEURAL
NETWORK* GRADIENT DESCENT 35 1.3.3 TRAINING THE MULTILAYER NEURAL
NETWORK* BACKPROPAGATION TUNING 43 1.3.4 IMPROVEMENTS ON GRADIENT
DESCENT 53 1.3.5 HEBBIAN TUNING 56 1.3.6 CONTINUOUS-TIME TUNING 57 1.4
REFERENCES 60 1.5 PROBLEMS 63 2 BACKGROUND ON DYNAMIC SYSTEMS 67 2.1
DYNAMICAL SYSTEMS 67 2.1.1 CONTINUOUS-TIME SYSTEMS 68 2.1.2
DISCRETE-TIME SYSTEMS 71 2.2 SOME MATHEMATICAL BACKGROUND 75 2.2.1
VECTOR AND MATRIX NORMS 75 2.2.2 CONTINUITY AND FUNCTION NORMS 76 2.3
PROPERTIES OF DYNAMICAL SYSTEMS 77 V CONTENTS 2.3.1 STABILITY 78 2.3.2
PASSIVITY 80 2.3.3 OBSERVABILITY AND CONTROLLABILITY 83 2.4 FEEDBACK
LINEARIZATION AND CONTROL SYSTEM DESIGN 86 2.4.1 INPUT-OUTPUT FEEDBACK
LINEARIZATION CONTROLLERS 87 2.4.2 COMPUTER SIMULATION OF FEEDBACK
CONTROL SYSTEMS 92 2.4.3 FEEDBACK LINEARIZATION FOR DISCRETE-TIME
SYSTEMS 96 2.5 NONLINEAR STABILITY ANALYSIS AND CONTROLS DESIGN 97 2.5.1
LYAPUNOV ANALYSIS FOR AUTONOMOUS SYSTEMS 97 2.5.2 CONTROLLER DESIGN
USING LYAPUNOV TECHNIQUES 103 2.5.3 LYAPUNOV ANALYSIS FOR NON-AUTONOMOUS
SYSTEMS 106 2.5.4 EXTENSIONS OF LYAPUNOV TECHNIQUES AND BOUNDED
STABILITY . 109 2.6 REFERENCES H5 2.7 PROBLEMS H6 ROBOT DYNAMICS AND
CONTROL 123 3.0.1 COMMERCIAL ROBOT CONTROLLERS 123 3.1 KINEMATICS AND
JACOBIANS 124 3.1.1 KINEMATICS OF RIGID SERIAL-LINK MANIPULATORS 125
3.1.2 ROBOT JACOBIANS 128 3.2 ROBOT DYNAMICS AND PROPERTIES 129 3.2.1
JOINT SPACE DYNAMICS AND PROPERTIES 130 3.2.2 STATE VARIABLE
REPRESENTATIONS 134 3.2.3 CARTESIAN DYNAMICS AND ACTUATOR DYNAMICS 135
3.3 COMPUTED-TORQUE (CT) CONTROL AND COMPUTER SIM- ULATION 136 3.3.1
COMPUTED-TORQUE (CT) CONTROL 136 3.3.2 COMPUTER SIMULATION OF ROBOT
CONTROLLERS 138 3.3.3 APPROXIMATE COMPUTED-TORQUE CONTROL AND CLASSICAL
JOINT CONTROL 143 3.3.4 DIGITAL CONTROL 145 3.4 FILTERED-ERROR
APPROXIMATION-BASED CONTROL ... 147 3.4.1 A GENERAL CONTROLLER DESIGN
FRAMEWORK BASED ON APPROXI- MATION 154 3.4.2 COMPUTED-TORQUE CONTROL
VARIANT 156 3.4.3 ADAPTIVE CONTROL 156 3.4.4 ROBUST CONTROL 162 3.4.5
LEARNING CONTROL 165 3.5 CONCLUSIONS 167 3.6 REFERENCES 168 3.7 PROBLEMS
169 NEURAL NETWORK ROBOT CONTROL 173 4.1 ROBOT ARM DYNAMICS AND TRACKING
ERROR DYNAMICS 176 4.2 ONE-LAYER FUNCTIONAL-LINK NEURAL NETWORK CON-
TROLLER 179 4.2.1 APPROXIMATION BY ONE-LAYER FUNCTIONAL-LINK NN 180
CONTENTS VLL 4.2.2 NN CONTROLLER AND ERROR SYSTEM DYNAMICS 181 4.2.3
UNSUPERVISED BACKPROPAGATION WEIGHT TUNING 182 4.2.4 AUGMENTED
UNSUPERVISED BACKPROPAGATION TUNING* REMOV- ING THE PE CONDITION 187
4.2.5 FUNCTIONAL-LINK NN CONTROLLER DESIGN AND SIMULATION EXAMPLEL90 4.3
TWO-LAYER NEURAL NETWORK CONTROLLER 191 4.3.1 NN APPROXIMATION AND THE
NONLINEARITY IN THE PARAMETERS PROBLEM 194 4.3.2 CONTROLLER STRUCTURE
AND ERROR SYSTEM DYNAMICS 196 4.3.3 WEIGHT UPDATES FOR GUARANTEED
TRACKING PERFORMANCE . . . . 198 4.3.4 TWO-LAYER NN CONTROLLER DESIGN
AND SIMULATION EXAMPLE . 206 4.4 PARTITIONED NN AND SIGNAL PREPROCESSING
206 4.4.1 PARTITIONED NN 206 4.4.2 PREPROCESSING OF NEURAL NET INPUTS
209 4.4.3 SELECTION OF A BASIS SET FOR THE FUNCTIONAL-LINK NN 209 4.5
PASSIVITY PROPERTIES OF NN CONTROLLERS 212 4.5.1 PASSIVITY OF THE
TRACKING ERROR DYNAMICS 212 4.5.2 PASSIVITY PROPERTIES OF NN CONTROLLERS
213 4.6 CONCLUSIONS 216 4.7 REFERENCES 217 4.8 PROBLEMS 219 5 NEURAL
NETWORK ROBOT CONTROL: APPLICATIONS AND EXTENSIONS 221 5.1 FORCE CONTROL
USING NEURAL NETWORKS 222 5.1.1 FORCE CONSTRAINED MOTION AND ERROR
DYNAMICS 223 5.1.2 NEURAL NETWORK HYBRID POSITION/FORCE CONTROLLER 225
5.1.3 DESIGN EXAMPLE FOR NN HYBRID POSITION/FORCE CONTROLLER . . 232 5.2
ROBOT MANIPULATORS WITH LINK FLEXIBILITY, MOTOR DYNAMICS, AND JOINT
FLEXIBILITY 233 5.2.1 FLEXIBLE-LINK ROBOT ARMS 233 5.2.2 ROBOTS WITH
ACTUATORS AND COMPLIANT DRIVE TRAIN COUPLING 238 5.2.3 RIGID-LINK
ELECTRICALLY-DRIVEN (RLED) ROBOT ARMS 244 5.3 SINGULAR PERTURBATION
DESIGN 245 5.3.1 TWO-TIME-SCALE CONTROLLER DESIGN 246 5.3.2 NN
CONTROLLER FOR FLEXIBLE-LINK ROBOT USING SINGULAR PER- TURBATIONS 249
5.4 BACKSTEPPING DESIGN 258 5.4.1 BACKSTEPPING DESIGN 258 5.4.2 NN
CONTROLLER FOR RIGID-LINK ELECTRICALLY-DRIVEN ROBOT USING BACKSTEPPING
262 5.5 CONCLUSIONS 267 5.6 REFERENCES 270 5.7 PROBLEMS 272 VIII
CONTENTS 6 NEURAL NETWORK CONTROL OF NONLINEAR SYSTEMS 277 6.1 SYSTEM
AND TRACKING ERROR DYNAMICS 278 6.1.1 TRACKING CONTROLLER AND ERROR
DYNAMICS 279 6.1.2 WELL-DEFINED CONTROL PROBLEM 281 6.2 CASE OF KNOWN
FUNCTION G(X) 281 6.2.1 PROPOSED NN CONTROLLER 282 6.2.2 NN WEIGHT
TUNING FOR TRACKING STABILITY 283 6.2.3 ILLUSTRATIVE SIMULATION EXAMPLE
286 6.3 CASE OF UNKNOWN FUNCTION G(X) 287 6.3.1 PROPOSED NN CONTROLLER
287 6.3.2 NN WEIGHT TUNING FOR TRACKING STABILITY 289 6.3.3 ILLUSTRATIVE
SIMULATION EXAMPLES 296 6.4 CONCLUSIONS 301 6.5 REFERENCES 303 7 NN
CONTROL WITH DISCRETE-TIME TUNING 305 7.1 BACKGROUND AND ERROR DYNAMICS
306 7.1.1 NEURAL NETWORK APPROXIMATION PROPERTY 306 7.1.2 STABILITY OF
SYSTEMS 308 7.1.3 TRACKING ERROR DYNAMICS FOR A CLASS OF NONLINEAR
SYSTEMS . 308 7.2 ONE-LAYER NEURAL NETWORK CONTROLLER DESIGN . . 310
7.2.1 STRUCTURE OF THE ONE-LAYER NN CONTROLLER AND ERROR SYSTEM DYNAMICS
311 7.2.2 ONE-LAYER NEURAL NETWORK WEIGHT UPDATES 312 7.2.3 PROJECTION
ALGORITHM 316 7.2.4 IDEAL CASE: NO DISTURBANCES OR NN RECONSTRUCTION
ERRORS . . 321 7.2.5 ONE-LAYER NEURAL NETWORK WEIGHT TUNING MODIFICATION
FOR RELAXATION OF PERSISTENCY OF EXCITATION CONDITION 321 7.3 MULTILAYER
NEURAL NETWORK CONTROLLER DESIGN . . 327 7.3.1 STRUCTURE OF THE NN
CONTROLLER AND ERROR SYSTEM DYNAMICS . 330 7.3.2 MULTILAYER NEURAL
NETWORK WEIGHT UPDATES 331 7.3.3 PROJECTION ALGORITHM 338 7.3.4
MULTILAYER NEURAL NETWORK WEIGHT TUNING MODIFICATION FOR RELAXATION OF
PERSISTENCY OF EXCITATION CONDITION 340 7.4 PASSIVITY PROPERTIES OF THE
NN 350 7.4.1 PASSIVITY PROPERTIES OF THE TRACKING ERROR SYSTEM 350 7.4.2
PASSIVITY PROPERTIES OF ONE-LAYER NEURAL NETWORKS AND THE CLOSED-LOOP
SYSTEM 352 7.4.3 PASSIVITY PROPERTIES OF MULTILAYER NEURAL NETWORKS 353
7.5 CONCLUSIONS 354 7.6 REFERENCES 354 7.7 PROBLEMS 356 IX DISCRETE-TIME
FEEDBACK LINEARIZATION BY NEURAL NETWORKS 359 8.1 SYSTEM DYNAMICS AND
THE TRACKING PROBLEM 360 8.1.1 TRACKING ERROR DYNAMICS FOR A CLASS OF
NONLINEAR SYSTEMS . 360 8.2 NN CONTROLLER DESIGN FOR FEEDBACK
LINEARIZATION 362 8.2.1 NN APPROXIMATION OF UNKNOWN FUNCTIONS 363 8.2.2
ERROR SYSTEM DYNAMICS 364 8.2.3 WELL-DEFINED CONTROL PROBLEM 366 8.2.4
PROPOSED CONTROLLER 367 8.3 SINGLE-LAYER NN FOR FEEDBACK LINEARIZATION
367 8.3.1 WEIGHT UPDATES REQUIRING PERSISTENCE OF EXCITATION 368 8.3.2
PROJECTION ALGORITHM 375 8.3.3 WEIGHT UPDATES NOT REQUIRING PERSISTENCE
OF EXCITATION . . . 376 8.4 MULTILAYER NEURAL NETWORKS FOR FEEDBACK
LINEARIZA- TION 383 8.4.1 WEIGHT UPDATES REQUIRING PERSISTENCE OF
EXCITATION 384 8.4.2 WEIGHT UPDATES NOT REQUIRING PERSISTENCE OF
EXCITATION . . . 390 8.5 PASSIVITY PROPERTIES OF THE NN 402 8.5.1
PASSIVITY PROPERTIES OF THE TRACKING ERROR SYSTEM 405 8.5.2 PASSIVITY
PROPERTIES OF ONE-LAYER NEURAL NETWORK CONTROLLERS 406 8.5.3 PASSIVITY
PROPERTIES OF MULTILAYER NEURAL NETWORK CONTROLLERS 407 8.6 CONCLUSIONS
409 8.7 REFERENCES 409 8.8 PROBLEMS 411 STATE ESTIMATION USING
DISCRETE-TIME NEURAL NETWORKS 413 9.1 IDENTIFICATION OF NONLINEAR
DYNAMICAL SYSTEMS . . 415 9.2 IDENTIFIER DYNAMICS FOR MIMO SYSTEMS 415
9.3 MULTILAYER NEURAL NETWORK IDENTIFIER DESIGN ... 418 9.3.1 STRUCTURE
OF THE NN CONTROLLER AND ERROR SYSTEM DYNAMICS . 418 9.3.2 THREE-LAYER
NEURAL NETWORK WEIGHT UPDATES 420 9.4 PASSIVITY PROPERTIES OF THE NN 425
9.5 SIMULATION RESULTS 427 9.6 CONCLUSIONS 428 9.7 REFERENCES 428 9.8
PROBLEMS 430
|
| any_adam_object | 1 |
| author | Lewis, Frank L. Jagannathan, Suresh 1961- Yeşildirek, A. |
| author_GND | (DE-588)1049965701 |
| author_facet | Lewis, Frank L. Jagannathan, Suresh 1961- Yeşildirek, A. |
| author_role | aut aut aut |
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| building | Verbundindex |
| bvnumber | BV012979154 |
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| callnumber-label | TJ211 |
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| callnumber-subject | TJ - Mechanical Engineering and Machinery |
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| dewey-full | 629.892632 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
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| discipline | Informatik Fertigungstechnik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
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| id | DE-604.BV012979154 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:37:08Z |
| institution | BVB |
| isbn | 0748405968 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008843013 |
| oclc_num | 40716288 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM |
| owner_facet | DE-91G DE-BY-TUM |
| physical | XXIII, 442 S. graph. Darst. |
| publishDate | 1999 |
| publishDateSearch | 1999 |
| publishDateSort | 1999 |
| publisher | Taylor & Francis |
| record_format | marc |
| spelling | Lewis, Frank L. Verfasser aut Neural network control of robot manipulators and nonlinear systems F. L. Lewis ; S. Jagannathan ; A. Yes̨ildirek London [u.a.] Taylor & Francis 1999 XXIII, 442 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Manipulateurs (Mécanismes) REDES NEURAIS larpcal ROBÔS larpcal Robots - Commande, Systèmes de Robots - Dynamique Réseaux neuronaux (Informatique) SISTEMAS DE CONTROLE larpcal Neural networks (Computer science) Robots Control systems Roboter (DE-588)4050208-9 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Regelungssystem (DE-588)4134712-2 gnd rswk-swf Robotik (DE-588)4261462-4 gnd rswk-swf Robotik (DE-588)4261462-4 s Neuronales Netz (DE-588)4226127-2 s DE-604 Roboter (DE-588)4050208-9 s Regelungssystem (DE-588)4134712-2 s Jagannathan, Suresh 1961- Verfasser (DE-588)1049965701 aut Yeşildirek, A. Verfasser aut GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008843013&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lewis, Frank L. Jagannathan, Suresh 1961- Yeşildirek, A. Neural network control of robot manipulators and nonlinear systems Manipulateurs (Mécanismes) REDES NEURAIS larpcal ROBÔS larpcal Robots - Commande, Systèmes de Robots - Dynamique Réseaux neuronaux (Informatique) SISTEMAS DE CONTROLE larpcal Neural networks (Computer science) Robots Control systems Roboter (DE-588)4050208-9 gnd Neuronales Netz (DE-588)4226127-2 gnd Regelungssystem (DE-588)4134712-2 gnd Robotik (DE-588)4261462-4 gnd |
| subject_GND | (DE-588)4050208-9 (DE-588)4226127-2 (DE-588)4134712-2 (DE-588)4261462-4 |
| title | Neural network control of robot manipulators and nonlinear systems |
| title_auth | Neural network control of robot manipulators and nonlinear systems |
| title_exact_search | Neural network control of robot manipulators and nonlinear systems |
| title_full | Neural network control of robot manipulators and nonlinear systems F. L. Lewis ; S. Jagannathan ; A. Yes̨ildirek |
| title_fullStr | Neural network control of robot manipulators and nonlinear systems F. L. Lewis ; S. Jagannathan ; A. Yes̨ildirek |
| title_full_unstemmed | Neural network control of robot manipulators and nonlinear systems F. L. Lewis ; S. Jagannathan ; A. Yes̨ildirek |
| title_short | Neural network control of robot manipulators and nonlinear systems |
| title_sort | neural network control of robot manipulators and nonlinear systems |
| topic | Manipulateurs (Mécanismes) REDES NEURAIS larpcal ROBÔS larpcal Robots - Commande, Systèmes de Robots - Dynamique Réseaux neuronaux (Informatique) SISTEMAS DE CONTROLE larpcal Neural networks (Computer science) Robots Control systems Roboter (DE-588)4050208-9 gnd Neuronales Netz (DE-588)4226127-2 gnd Regelungssystem (DE-588)4134712-2 gnd Robotik (DE-588)4261462-4 gnd |
| topic_facet | Manipulateurs (Mécanismes) REDES NEURAIS ROBÔS Robots - Commande, Systèmes de Robots - Dynamique Réseaux neuronaux (Informatique) SISTEMAS DE CONTROLE Neural networks (Computer science) Robots Control systems Roboter Neuronales Netz Regelungssystem Robotik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008843013&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT lewisfrankl neuralnetworkcontrolofrobotmanipulatorsandnonlinearsystems AT jagannathansuresh neuralnetworkcontrolofrobotmanipulatorsandnonlinearsystems AT yesildireka neuralnetworkcontrolofrobotmanipulatorsandnonlinearsystems |