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Kinematic Control of Redundant Robot Arms Using Neural Networks : a Theoretical Study.

"In this book, focusing on robot arm control aided with neural networks, we present and investigate different methods and schemes for the control of robot arms. The idea for this book on the redundancy resolution of robot manipulators via different methods and schemes was conceived during the r...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Li, Shuai
Otros Autores: Jin, Long, Mirza, Mohammed Aquil
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2019.
Temas:
Acceso en línea:Texto completo

MARC

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049 |a UAMI 
100 1 |a Li, Shuai. 
245 1 0 |a Kinematic Control of Redundant Robot Arms Using Neural Networks :  |b a Theoretical Study. 
260 |a Newark :  |b John Wiley & Sons, Incorporated,  |c 2019. 
300 |a 1 online resource (217 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
588 0 |a Print version record. 
505 0 |a Intro; Title Page; Copyright Page; Contents; List of Figures; List of Tables; Preface; Acknowledgments; Part I Neural Networks for Serial Robot Arm Control; Chapter 1 Zeroing Neural Networks for Control; 1.1 Introduction; 1.2 Scheme Formulation and ZNN Solutions; 1.2.1 ZNN Model; 1.2.2 Nonconvex Function Activated ZNN Model; 1.3 Theoretical Analyses; 1.4 Computer Simulations and Verifications; 1.4.1 ZNN for Solving (1.13) at t = 1; 1.4.2 ZNN for Solving (1.13) with Different Bounds; 1.5 Summary; Chapter 2 Adaptive Dynamic Programming Neural Networks for Control; 2.1 Introduction 
505 8 |a 2.2 Preliminaries on Variable Structure Control of the Sensor-Actuator System2.3 Problem Formulation; 2.4 Model-Free Control of the Euler-Lagrange System; 2.4.1 Optimality Condition; 2.4.2 Approximating the Action Mapping and the Critic Mapping; 2.5 Simulation Experiment; 2.5.1 The Model; 2.5.2 Experiment Setup and Result; 2.6 Summary; Chapter 3 Projection Neural Networks for Robot Arm Control; 3.1 Introduction; 3.2 Problem Formulation; 3.3 A Modified Controller without Error Accumulation; 3.3.1 Existing RNN Solutions; 3.3.2 Limitations of Existing RNN Solutions; 3.3.3 The Presented Algorithm 
505 8 |a 3.3.4 Stability3.4 Performance Improvement Using Velocity Compensation; 3.4.1 A Control Law with Velocity Compensation; 3.4.2 Stability; 3.5 Simulations; 3.5.1 Regulation to a Fixed Position; 3.5.2 Tracking of Time-Varying References; 3.5.3 Comparisons; 3.6 Summary; Chapter 4 Neural Learning and Control Co-Design for Robot Arm Control; 4.1 Introduction; 4.2 Problem Formulation; 4.3 Nominal Neural Controller Design; 4.4 A Novel Dual Neural Network Model; 4.4.1 Neural Network Design; 4.4.2 Stability; 4.5 Simulations; 4.5.1 Simulation Setup; 4.5.2 Simulation Results; 4.5.2.1 Tracking Performance 
505 8 |a 4.5.2.2 With vs. Without Excitation Noises4.6 Summary; Chapter 5 Robust Neural Controller Design for Robot Arm Control; 5.1 Introduction; 5.2 Problem Formulation; 5.3 Dual Neural Networks for the Nominal System; 5.3.1 Neural Network Design; 5.3.2 Convergence Analysis; 5.4 Neural Design in the Presence of Noises; 5.4.1 Polynomial Noises; 5.4.1.1 Neural Dynamics; 5.4.1.2 Practical Considerations; 5.4.2 Special Cases; 5.4.2.1 Constant Noises; 5.4.2.2 Linear Noises; 5.5 Simulations; 5.5.1 Simulation Setup; 5.5.2 Nominal Situation; 5.5.3 Constant Noises; 5.5.4 Time-Varying Polynomial Noises 
505 8 |a 5.6 SummaryChapter 6 Using Neural Networks to Avoid Robot Singularity; 6.1 Introduction; 6.2 Preliminaries; 6.3 Problem Formulation; 6.3.1 Manipulator Kinematics; 6.3.2 Manipulability; 6.3.3 Optimization Problem Formulation; 6.4 Reformulation as a Constrained Quadratic Program; 6.4.1 Equation Constraint: Speed Level Resolution; 6.4.2 Redefinition of the Objective Function; 6.4.3 Set Constraint; 6.4.4 Reformulation and Convexification; 6.5 Neural Networks for Redundancy Resolution; 6.5.1 Conversion to a Nonlinear Equation Set; 6.5.2 Neural Dynamics for Real-Time Redundancy Resolution 
500 |a 6.5.3 Convergence Analysis 
520 |a "In this book, focusing on robot arm control aided with neural networks, we present and investigate different methods and schemes for the control of robot arms. The idea for this book on the redundancy resolution of robot manipulators via different methods and schemes was conceived during the research discussion in the laboratory and at international scientific meetings. Most of the materials of this book are derived from the authors' papers published in journals and proceedings of the international conferences. In fact, in recent decades, the field of robotics has undergone the phases of exponential growth, generating many new theoretical concepts and applications. Our first priority is thus to cover each central topic in enough details to make the material clear and coherent; in other words, each part (and even each chapter) is written in a relatively self-contained manner"--  |c Provided by publisher. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Robots  |x Kinematics  |x Data processing. 
650 0 |a Manipulators (Mechanism)  |x Automatic control. 
650 0 |a Redundancy (Engineering)  |x Data processing. 
650 6 |a Robots  |x Cinématique  |x Informatique. 
650 6 |a Manipulateurs (Mécanismes)  |x Commande automatique. 
650 6 |a Redondance (Ingénierie)  |x Informatique. 
650 7 |a Manipulators (Mechanism)  |x Automatic control  |2 fast 
700 1 |a Jin, Long. 
700 1 |a Mirza, Mohammed Aquil. 
758 |i has work:  |a Kinematic control of redundant robot arms using neural networks (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCFrbFfyKqHqTHBp46RmdpP  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |a Li, Shuai.  |t Kinematic Control of Redundant Robot Arms Using Neural Networks : A Theoretical Study.  |d Newark : John Wiley & Sons, Incorporated, ©2019  |z 9781119556961 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5702865  |z Texto completo 
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938 |a YBP Library Services  |b YANK  |n 16047054 
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