Application of neural networks to adaptive control of nonlinear systems:
This book investigates the ability of a neural network (NN) to learn how to control an unknown (nonlinear, in general) system, using data acquired on-line, that is during the process of attempting to exert control. Two algorithms are developed to train the neural network for real-time control applic...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Taunton
Research Studies Press [u.a.]
1997
|
| Schriftenreihe: | Control Systems Centre <Manchester>: UMIST Control Systems Centre series
4 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | This book investigates the ability of a neural network (NN) to learn how to control an unknown (nonlinear, in general) system, using data acquired on-line, that is during the process of attempting to exert control. Two algorithms are developed to train the neural network for real-time control applications. The first algorithm is known as Learning by Recursive Least Squares (LRLS) algorithm and the second algorithm is known as Integrated Gradient and Least Squares (IGLS) algorithm. The ability of these algorithms to train the NN controller for real-time control is demonstrated on practical applications and the local convergence and stability requirements of these algorithms are analysed. In addition, network topology, learning algorithms (particularly supervised learning) and neural network control strategies are presented. |
| Beschreibung: | XXV, 198 S. graph. Darst. |
| ISBN: | 0863802141 0471972630 |
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| 490 | 1 | |a Control Systems Centre <Manchester>: UMIST Control Systems Centre series |v 4 | |
| 520 | 3 | |a This book investigates the ability of a neural network (NN) to learn how to control an unknown (nonlinear, in general) system, using data acquired on-line, that is during the process of attempting to exert control. Two algorithms are developed to train the neural network for real-time control applications. The first algorithm is known as Learning by Recursive Least Squares (LRLS) algorithm and the second algorithm is known as Integrated Gradient and Least Squares (IGLS) algorithm. The ability of these algorithms to train the NN controller for real-time control is demonstrated on practical applications and the local convergence and stability requirements of these algorithms are analysed. In addition, network topology, learning algorithms (particularly supervised learning) and neural network control strategies are presented. | |
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| 650 | 4 | |a Neural networks (Computer science) | |
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Datensatz im Suchindex
| _version_ | 1804127977369239552 |
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| adam_text | APPLICATION OF NEURAL NETWORKS TO ADAPTIVE CONTROL OF NONLINEAR SYSTEMS
G. W. NG CONTROL SYSTEMS CENTRE, UMIST, UK RESEARCH STUDIES PRESS LTD.
TAUNTON, SOMERSET, ENGLAND JOHN WILEY & SONS INC. NEW YORK * CHICHESTER
* TORONTO * BRISBANE * SINGAPORE CONTENTS LIST OF FIGURES XVN LIST OF
TABLES X X NOTATION 1 INTRODUCTION 1.1 ADAPTIVE CONTROL USING NEURAL
NETWORKS - WHY AND WHY NOW ? 1 O 1.2 HISTORICAL PERSPECTIVE * 1.3
OBJECTIVE AND CONTRIBUTIONS OF THIS MONOGRAPH 8 1.4 OUTLINE OF THIS
MONOGRAPH 9 2 NETWORK STRUCTURES AND LEARNING ALGORITHMS 11 2.1
INTRODUCTION 2.2 BASIC UNITS 13 2.2.1 ACTIVATION FUNCTION 14 2.3 NETWORK
TOPOLOGY 15 2.3.1 FEEDFORWARD NEURAL NETWORK 16 2.3.2 RECURRENT NEURAL
NETWORK 17 2.3.3 SINGLE LAYER AND MULTILAYER NEURAL NETWORKS 18 2.4
SUPERVISED LEARNING 20 2.4.1 EARLY LEARNING ALGORITHMS 20 2.4.2 FIRST
ORDER GRADIENT METHODS 22 2.4.3 SECOND ORDER GRADIENT METHODS 29 2.4.4
LRLS AND IGLS ALGORITHMS 33 2.4.5 CHEMOTAXIS ALGORITHM 33 V XIII 2.4.6
HISTORY-STACK LEARNING 33 2.4.7 ALOPEX ALGORITHM 34 2.5 REINFORCEMENT
LEARNING OC 2.5.1 LINEAR REWARD-PENALTY LEARNING 35 2.5.2 ASSOCIATIVE
SEARCH LEARNING 3G 2.5.3 ADAPTIVE CRITIC LEARNING 37 2.6 UNSUPERVISED
LEARNING Q 7 2.6.1 HEBBIAN LEARNING 07 2.6.2 BOLTZMANN MACHINES LEARNING
3G 2.6.3 KOHONEN SELF-ORGANISING LEARNING 39 2.7 CONCLUSIONS ,N 3 NEURAL
NETWORKS CONTROL STRATEGIES 43 3.1 INTRODUCTION 40 3.2 TWOFOLD
CLASSIFICATION AQ 3.3 NON-HYBRID STRATEGY - CONTROL SIGNAL 4G 3.3.1
MIMIC HUMAN EXPERT 4G 3.3.2 MIMIC CONVENTIONAL CONTROLLER 4G 3.3.3
INDIRECT LEARNING ARCHITECTURE 5^ 3.4 NON-HYBRID STRATEGY - DESIRED
OUTPUT SIGNAL 52 3.4.1 DIRECT INVERSE CONTROL 52 3.4.2 FORWARD MODELLING
AND INVERSE CONTROL 54 3.4.3 NEURAL INTERNAL MODEL CONTROL 5G 3.4.4
NEURAL FEEDBACK LINEARISATION 5G 3.4.5 NEURAL PREDICTIVE CONTROL 62 3.5
HYBRID STRATEGY - CONTROL SIGNAL 63 3.5.1 INDIRECT LEARNING ARCHITECTURE
63 3.6 HYBRID STRATEGY - DESIRED OUTPUT SIGNAL 64 3.6.1 DIRECT INVERSE
CONTROL 64 3.6.2 FORWARD MODELLING AND INVERSE CONTROL 67 3.6.3 NEURAL
FEEDBACK LINEARISATION 69 3.7 HYBRID STRATEGY - FEEDBACK CONTROLLER
OUTPUT SIGNAL 70 3.7.1 FEEDBACK ERROR LEARNING 70 3.8 APPLICATIONS 7 O
3.9 CONCLUSIONS 7FI 4 ON-LINE BPM AND LRLS CONTROL ALGORITHMS 79 4.1
INTRODUCTION 7 Q 4.2 ON-LINE ADAPTIVE CONTROL USING MLP TRAINED BY BPM
ALGORITHM 81 XIV 4.2.1 PLANT JACOBIAN 82 4.2.2 SUMMARY OF THE ON-LINE
BPM CONTROL ALGORITHM 85 4.2.3 SIMULATION RESULTS 86 4.3 ONLINE ADAPTIVE
CONTROL USING LRLS ALGORITHM 89 4.3.1 UPDATING BY RLS ALGORITHM 89 4.3.2
COMPARISON OF BPM, DBD, MABP, LM AND LRLS ALGORITHMS ... 94 4.4
REAL-TIME APPLICATION ON COUPLED TANKS TEST RIG 97 4.4.1 SISO COUPLED
TANKS APPARATUS 97 4.4.2 REAL-TIME RESULTS FOR THE SISO COUPLED TANKS
100 4.5 CONCLUSIONS 100 5 LOCAL CONVERGENCE AND STABILITY ANALYSIS 103
5.1 INTRODUCTION 103 5.2 ANALYSIS OF THE ON-LINE BPM CONTROL ALGORITHM
105 5.2.1 CONVERGENCE OF THE MODEL 106 5.2.2 CONVERGENCE OF THE NN
CONTROLLER 111 5.3 ANALYSIS OF THE ON-LINE LRLS CONTROL ALGORITHM 113
5.4 LRLS AND BPM ALGORITHMS 119 5.5 SIMULATION RESULTS 121 5.6
CONCLUSIONS 124 6 ON-LINE IGLS CONTROL ALGORITHM 127 6.1 INTRODUCTION
127 6.2 THE NEW LEARNING ALGORITHM 127 6.2.1 SUMMARY OF THE IGLS CONTROL
ALGORITHM 129 6.2.2 REASONS FOR USING AN INTEGRATED GRADIENT AND LEAST
SQUARES ALGORITHM 130 6.3 CONVERGENCE AND STABILITY CONDITIONS 131 6.4
SIMULATION RESULTS 132 6.5 APPLICATION TO MULTIVARIABLE COUPLED TANKS
TEST RIG 135 6.5.1 MULTIVARIABLE COUPLED TANKS APPARATUS 135 6.5.2
REAL-TIME CONTROL RESULTS 136 6.6 CONCLUSIONS 137 7 SYSTEMS WITH UNKNOWN
AND VARYING TIME-DELAYS 139 7.1 INTRODUCTION 139 7.2 NN, SELF-TUNING
POLE ASSIGNMENT AND PID CONTROLLERS 140 7.2.1 SELF-TUNING POLE
ASSIGNMENT CONTROLLER 140 7.2.2 PID CONTROLLER 141 7.3 SIMULATION
RESULTS 142 XV 7.3.1 PERFORMANCE OF THE CONTROLLERS 143 7.3.2 EFFECT OF
DIFFERENT NUMBERS OF HIDDEN UNITS 143 7.3.3 MIMO SYSTEM WITH VARYING
TIME-DELAYS 144 7.4 COMPARISON OF CONTROLLERS IN REAL-TIME EXPERIMENTS
145 7.4.1 HEAT TRANSFER PROCESS TRAINER 14G 7.4.2 EXPERIMENTAL
PROCEDURES AND RESULTS 147 7.5 DISCUSSION AND ANALYSIS OF THE
CONTROLLERS 152 7.6 CONCLUSIONS IGO 8 CONCLUSIONS IGO 8.1 SUMMARY IGO
8.2 DIRECTIONS FOR FUTURE RESEARCH IGL APPENDIX 1, A.L WHAT DOES
APPROXIMATION THEORY SAY? 163 A.2 PROOF OF THEOREM 6.1 1 65 REFERENCES
-,71 INDEX 1 9 5 XVI
|
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| dewey-search | 629.8/9 |
| dewey-sort | 3629.8 19 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Book |
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| id | DE-604.BV013220309 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:41:55Z |
| institution | BVB |
| isbn | 0863802141 0471972630 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-009008083 |
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| physical | XXV, 198 S. graph. Darst. |
| publishDate | 1997 |
| publishDateSearch | 1997 |
| publishDateSort | 1997 |
| publisher | Research Studies Press [u.a.] |
| record_format | marc |
| series | Control Systems Centre <Manchester>: UMIST Control Systems Centre series |
| series2 | Control Systems Centre <Manchester>: UMIST Control Systems Centre series |
| spelling | Ng, Gee Wah Verfasser aut Application of neural networks to adaptive control of nonlinear systems G. W. Ng Taunton Research Studies Press [u.a.] 1997 XXV, 198 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Control Systems Centre <Manchester>: UMIST Control Systems Centre series 4 This book investigates the ability of a neural network (NN) to learn how to control an unknown (nonlinear, in general) system, using data acquired on-line, that is during the process of attempting to exert control. Two algorithms are developed to train the neural network for real-time control applications. The first algorithm is known as Learning by Recursive Least Squares (LRLS) algorithm and the second algorithm is known as Integrated Gradient and Least Squares (IGLS) algorithm. The ability of these algorithms to train the NN controller for real-time control is demonstrated on practical applications and the local convergence and stability requirements of these algorithms are analysed. In addition, network topology, learning algorithms (particularly supervised learning) and neural network control strategies are presented. Adaptive control systems Neural networks (Computer science) Nonlinear control theory Adaptivregelung (DE-588)4000457-0 gnd rswk-swf Nichtlineares System (DE-588)4042110-7 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Adaptivregelung (DE-588)4000457-0 s Neuronales Netz (DE-588)4226127-2 s Nichtlineares System (DE-588)4042110-7 s DE-604 Control Systems Centre <Manchester>: UMIST Control Systems Centre series 4 (DE-604)BV009763376 4 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009008083&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ng, Gee Wah Application of neural networks to adaptive control of nonlinear systems Control Systems Centre <Manchester>: UMIST Control Systems Centre series Adaptive control systems Neural networks (Computer science) Nonlinear control theory Adaptivregelung (DE-588)4000457-0 gnd Nichtlineares System (DE-588)4042110-7 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4000457-0 (DE-588)4042110-7 (DE-588)4226127-2 |
| title | Application of neural networks to adaptive control of nonlinear systems |
| title_auth | Application of neural networks to adaptive control of nonlinear systems |
| title_exact_search | Application of neural networks to adaptive control of nonlinear systems |
| title_full | Application of neural networks to adaptive control of nonlinear systems G. W. Ng |
| title_fullStr | Application of neural networks to adaptive control of nonlinear systems G. W. Ng |
| title_full_unstemmed | Application of neural networks to adaptive control of nonlinear systems G. W. Ng |
| title_short | Application of neural networks to adaptive control of nonlinear systems |
| title_sort | application of neural networks to adaptive control of nonlinear systems |
| topic | Adaptive control systems Neural networks (Computer science) Nonlinear control theory Adaptivregelung (DE-588)4000457-0 gnd Nichtlineares System (DE-588)4042110-7 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Adaptive control systems Neural networks (Computer science) Nonlinear control theory Adaptivregelung Nichtlineares System Neuronales Netz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009008083&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV009763376 |
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