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Competition-Based Neural Networks with Robotic Applications / / by Shuai Li, Long Jin
| Competition-Based Neural Networks with Robotic Applications / / by Shuai Li, Long Jin |
| Autore | Li Shuai |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XV, 121 p. 44 illus.) |
| Disciplina | 006.32 |
| Collana | SpringerBriefs in Applied Sciences and Technology |
| Soggetto topico |
Computational intelligence
Automatic control Robotics Automation Artificial intelligence Neural networks (Computer science) Computational Intelligence Control, Robotics, Automation Artificial Intelligence Mathematical Models of Cognitive Processes and Neural Networks |
| ISBN | 981-10-4947-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Competition Aided with Discrete -- Time Dynamic Feedback -- Competition Aided with Continuous -- Time Nonlinear Model -- Competition Aided with Finite -- time Neural Network -- Competition based on Selective Positive-negative Feedback -- Distributed Competition in Dynamic Networks -- Competition-based Distributed Coordination Control of Robots. |
| Record Nr. | UNINA-9910299586703321 |
Li Shuai
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Kinematic control of redundant robot arms using neural networks / / [edited by] Shuai Li, Hong Kong Polytechnic University, Long Jin, Hong Kong Polytechnic University, Mohammed Aquil Mirza, Hong Kong Polytechnic University
| Kinematic control of redundant robot arms using neural networks / / [edited by] Shuai Li, Hong Kong Polytechnic University, Long Jin, Hong Kong Polytechnic University, Mohammed Aquil Mirza, Hong Kong Polytechnic University |
| Autore | Li Shuai |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , 2019 |
| Descrizione fisica | 1 online resource |
| Disciplina | 629.895632 |
| Soggetto topico |
Robots - Kinematics - Data processing
Manipulators (Mechanism) - Automatic control Redundancy (Engineering) - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-55700-3
1-119-55698-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
List of Figures xiii -- List of Tables xix -- Preface xxi -- Acknowledgments xxv -- Part I Neural Networks for Serial Robot Arm Control 1 -- 1 Zeroing Neural Networks for Control 3 -- 1.1 Introduction 3 -- 1.2 Scheme Formulation and ZNN Solutions 4 -- 1.2.1 ZNN Model 4 -- 1.2.2 Nonconvex Function Activated ZNN Model 8 -- 1.3 Theoretical Analyses 9 -- 1.4 Computer Simulations and Verifications 12 -- 1.4.1 ZNN for Solving (1.13) at t = 1 12 -- 1.4.2 ZNN for Solving (1.13) with Different Bounds 15 -- 1.5 Summary 16 -- 2 Adaptive Dynamic Programming Neural Networks for Control 17 -- 2.1 Introduction 17 -- 2.2 Preliminaries on Variable Structure Control of the Sensor-Actuator System 18 -- 2.3 Problem Formulation 19 -- 2.4 Model-Free Control of the Euler-Lagrange System 20 -- 2.4.1 Optimality Condition 21 -- 2.4.2 Approximating the Action Mapping and the Critic Mapping 21 -- 2.5 Simulation Experiment 23 -- 2.5.1 The Model 23 -- 2.5.2 Experiment Setup and Results 24 -- 2.6 Summary 25 -- 3 Projection Neural Networks for Robot Arm Control 27 -- 3.1 Introduction 27 -- 3.2 Problem Formulation 29 -- 3.3 A Modified Controller without Error Accumulation 30 -- 3.3.1 Existing RNN Solutions 30 -- 3.3.2 Limitations of Existing RNN Solutions 32 -- 3.3.3 The Presented Algorithm 33 -- 3.3.4 Stability 34 -- 3.4 Performance Improvement Using Velocity Compensation 36 -- 3.4.1 A Control Law with Velocity Compensation 36 -- 3.4.2 Stability 37 -- 3.5 Simulations 41 -- 3.5.1 Regulation to a Fixed Position 41 -- 3.5.2 Tracking of Time-Varying References 42 -- 3.5.3 Comparisons 47 -- 3.6 Summary 50 -- 4 Neural Learning and Control Co-Design for Robot Arm Control 51 -- 4.1 Introduction 51 -- 4.2 Problem Formulation 52 -- 4.3 Nominal Neural Controller Design 53 -- 4.4 A Novel Dual Neural Network Model 54 -- 4.4.1 Neural Network Design 54 -- 4.4.2 Stability 56 -- 4.5 Simulations 62 -- 4.5.1 Simulation Setup 62 -- 4.5.2 Simulation Results 63 -- 4.5.2.1 Tracking Performance 63 -- 4.5.2.2 With vs.Without Excitation Noises 64.
4.6 Summary 66 -- 5 Robust Neural Controller Design for Robot Arm Control 67 -- 5.1 Introduction 67 -- 5.2 Problem Formulation 68 -- 5.3 Dual Neural Networks for the Nominal System 69 -- 5.3.1 Neural Network Design 69 -- 5.3.2 Convergence Analysis 71 -- 5.4 Neural Design in the Presence of Noises 72 -- 5.4.1 Polynomial Noises 72 -- 5.4.1.1 Neural Dynamics 73 -- 5.4.1.2 Practical Considerations 77 -- 5.4.2 Special Cases 78 -- 5.4.2.1 Constant Noises 78 -- 5.4.2.2 Linear Noises 80 -- 5.5 Simulations 81 -- 5.5.1 Simulation Setup 81 -- 5.5.2 Nominal Situation 81 -- 5.5.3 Constant Noises 82 -- 5.5.4 Time-Varying Polynomial Noises 86 -- 5.6 Summary 86 -- 6 Using Neural Networks to Avoid Robot Singularity 87 -- 6.1 Introduction 87 -- 6.2 Preliminaries 89 -- 6.3 Problem Formulation 90 -- 6.3.1 Manipulator Kinematics 90 -- 6.3.2 Manipulability 90 -- 6.3.3 Optimization Problem Formulation 91 -- 6.4 Reformulation as a Constrained Quadratic Program 91 -- 6.4.1 Equation Constraint: Speed Level Resolution 91 -- 6.4.2 Redefinition of the Objective Function 92 -- 6.4.3 Set Constraint 93 -- 6.4.4 Reformulation and Convexification 94 -- 6.5 Neural Networks for Redundancy Resolution 95 -- 6.5.1 Conversion to a Nonlinear Equation Set 95 -- 6.5.2 Neural Dynamics for Real-Time Redundancy Resolution 96 -- 6.5.3 Convergence Analysis 96 -- 6.6 Illustrative Examples 98 -- 6.6.1 Manipulability Optimization via Self Motion 98 -- 6.6.2 Manipulability Optimization in Circular Path Tracking 99 -- 6.6.3 Comparisons 102 -- 6.6.4 Summary 104 -- Part II Neural Networks for Parallel Robot Control 105 -- 7 Neural Network Based Stewart Platform Control 107 -- 7.1 Introduction 107 -- 7.2 Preliminaries 108 -- 7.3 Robot Kinematics 109 -- 7.3.1 Geometric Relation 109 -- 7.3.2 Velocity Space Resolution 111 -- 7.4 Problem Formulation as Constrained Optimization 112 -- 7.5 Dynamic Neural Network Model 113 -- 7.5.1 Neural Network Design 113 -- 7.6 Theoretical Results 115 -- 7.6.1 Optimality 115 -- 7.6.2 Stability 116. 7.6.3 Comparison with Other Control Schemes 117 -- 7.7 Numerical Investigation 118 -- 7.7.1 Simulation Setups 118 -- 7.7.2 Circular Trajectory 122 -- 7.7.3 Infinity-Sign Trajectory 127 -- 7.7.4 Square Trajectory 127 -- 7.8 Summary 129 -- 8 Neural Network Based Learning and Control Co-Design for Stewart Platform Control 131 -- 8.1 Introduction 131 -- 8.2 Kinematic Modeling of Stewart Platforms 133 -- 8.2.1 Geometric Relation 133 -- 8.2.2 Velocity Space Resolution 135 -- 8.3 Recurrent Neural Network Design 136 -- 8.3.1 Problem Formulation from an Optimization Perspective 136 -- 8.3.2 Neural Network Dynamics 138 -- 8.3.3 Stability 138 -- 8.3.4 Optimality 139 -- 8.4 Numerical Investigation 142 -- 8.4.1 Setups 142 -- 8.4.2 Circular Trajectory 143 -- 8.4.3 Square Trajectory 143 -- 8.5 Summary 145 -- Part III Neural Networks for Cooperative Control 147 -- 9 Zeroing Neural Networks for Robot Arm Motion Generation 149 -- 9.1 Introduction 149 -- 9.2 Preliminaries 151 -- 9.2.1 Problem Definition and Assumption 151 -- 9.2.1.1 Assumption 151 -- 9.2.2 Manipulator Kinematics 151 -- 9.3 Problem Formulation and Distributed Scheme 152 -- 9.3.1 Problem Formulation and Neural-Dynamic Design 152 -- 9.3.2 Distributed Scheme 153 -- 9.4 NTZNN Solver and Theoretical Analyses 153 -- 9.4.1 ZNN for Real-Time Redundancy Resolution 154 -- 9.4.2 Theoretical Analyses and Results 157 -- 9.5 Illustrative Examples 160 -- 9.5.1 Consensus to a Fixed Configuration 160 -- 9.5.2 Cooperative Motion Generation Perturbed by Noises 161 -- 9.5.3 ZNN-Based Solution Perturbed by Noises 162 -- 9.6 Summary 165 -- 10 Zeroing Neural Networks for Robot Arm Motion Generation 167 -- 10.1 Introduction 167 -- 10.2 Preliminaries, Problem Formulation, and Distributed Scheme 168 -- 10.2.1 Definition and Robot Arm Kinematics 168 -- 10.2.2 Problem Formulation 168 -- 10.2.3 Distributed Scheme 169 -- 10.3 NANTZNN Solver and Theoretical Analyses 169 -- 10.3.1 NANTZNN for Real-Time Redundancy Resolution 170 -- 10.3.2 Theoretical Analyses and Results 171. 10.4 Illustrative Examples 172 -- 10.4.1 Cooperative Motion Planning without Noises 174 -- 10.4.2 Cooperative Motion Planning with Noises 174 -- 10.5 Summary 175 -- Reference 177 -- Index 185. |
| Record Nr. | UNINA-9910467189203321 |
|
Li Shuai
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Kinematic control of redundant robot arms using neural networks / / [edited by] Shuai Li, Hong Kong Polytechnic University, Long Jin, Hong Kong Polytechnic University, Mohammed Aquil Mirza, Hong Kong Polytechnic University
| Kinematic control of redundant robot arms using neural networks / / [edited by] Shuai Li, Hong Kong Polytechnic University, Long Jin, Hong Kong Polytechnic University, Mohammed Aquil Mirza, Hong Kong Polytechnic University |
| Autore | Li Shuai |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , 2019 |
| Descrizione fisica | 1 online resource |
| Disciplina | 629.895632 |
| Collana | THEi Wiley ebooks. |
| Soggetto topico |
Robots - Kinematics - Data processing
Manipulators (Mechanism) - Automatic control Redundancy (Engineering) - Data processing |
| ISBN |
1-119-55699-6
1-119-55700-3 1-119-55698-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
List of Figures xiii -- List of Tables xix -- Preface xxi -- Acknowledgments xxv -- Part I Neural Networks for Serial Robot Arm Control 1 -- 1 Zeroing Neural Networks for Control 3 -- 1.1 Introduction 3 -- 1.2 Scheme Formulation and ZNN Solutions 4 -- 1.2.1 ZNN Model 4 -- 1.2.2 Nonconvex Function Activated ZNN Model 8 -- 1.3 Theoretical Analyses 9 -- 1.4 Computer Simulations and Verifications 12 -- 1.4.1 ZNN for Solving (1.13) at t = 1 12 -- 1.4.2 ZNN for Solving (1.13) with Different Bounds 15 -- 1.5 Summary 16 -- 2 Adaptive Dynamic Programming Neural Networks for Control 17 -- 2.1 Introduction 17 -- 2.2 Preliminaries on Variable Structure Control of the Sensor-Actuator System 18 -- 2.3 Problem Formulation 19 -- 2.4 Model-Free Control of the Euler-Lagrange System 20 -- 2.4.1 Optimality Condition 21 -- 2.4.2 Approximating the Action Mapping and the Critic Mapping 21 -- 2.5 Simulation Experiment 23 -- 2.5.1 The Model 23 -- 2.5.2 Experiment Setup and Results 24 -- 2.6 Summary 25 -- 3 Projection Neural Networks for Robot Arm Control 27 -- 3.1 Introduction 27 -- 3.2 Problem Formulation 29 -- 3.3 A Modified Controller without Error Accumulation 30 -- 3.3.1 Existing RNN Solutions 30 -- 3.3.2 Limitations of Existing RNN Solutions 32 -- 3.3.3 The Presented Algorithm 33 -- 3.3.4 Stability 34 -- 3.4 Performance Improvement Using Velocity Compensation 36 -- 3.4.1 A Control Law with Velocity Compensation 36 -- 3.4.2 Stability 37 -- 3.5 Simulations 41 -- 3.5.1 Regulation to a Fixed Position 41 -- 3.5.2 Tracking of Time-Varying References 42 -- 3.5.3 Comparisons 47 -- 3.6 Summary 50 -- 4 Neural Learning and Control Co-Design for Robot Arm Control 51 -- 4.1 Introduction 51 -- 4.2 Problem Formulation 52 -- 4.3 Nominal Neural Controller Design 53 -- 4.4 A Novel Dual Neural Network Model 54 -- 4.4.1 Neural Network Design 54 -- 4.4.2 Stability 56 -- 4.5 Simulations 62 -- 4.5.1 Simulation Setup 62 -- 4.5.2 Simulation Results 63 -- 4.5.2.1 Tracking Performance 63 -- 4.5.2.2 With vs.Without Excitation Noises 64.
4.6 Summary 66 -- 5 Robust Neural Controller Design for Robot Arm Control 67 -- 5.1 Introduction 67 -- 5.2 Problem Formulation 68 -- 5.3 Dual Neural Networks for the Nominal System 69 -- 5.3.1 Neural Network Design 69 -- 5.3.2 Convergence Analysis 71 -- 5.4 Neural Design in the Presence of Noises 72 -- 5.4.1 Polynomial Noises 72 -- 5.4.1.1 Neural Dynamics 73 -- 5.4.1.2 Practical Considerations 77 -- 5.4.2 Special Cases 78 -- 5.4.2.1 Constant Noises 78 -- 5.4.2.2 Linear Noises 80 -- 5.5 Simulations 81 -- 5.5.1 Simulation Setup 81 -- 5.5.2 Nominal Situation 81 -- 5.5.3 Constant Noises 82 -- 5.5.4 Time-Varying Polynomial Noises 86 -- 5.6 Summary 86 -- 6 Using Neural Networks to Avoid Robot Singularity 87 -- 6.1 Introduction 87 -- 6.2 Preliminaries 89 -- 6.3 Problem Formulation 90 -- 6.3.1 Manipulator Kinematics 90 -- 6.3.2 Manipulability 90 -- 6.3.3 Optimization Problem Formulation 91 -- 6.4 Reformulation as a Constrained Quadratic Program 91 -- 6.4.1 Equation Constraint: Speed Level Resolution 91 -- 6.4.2 Redefinition of the Objective Function 92 -- 6.4.3 Set Constraint 93 -- 6.4.4 Reformulation and Convexification 94 -- 6.5 Neural Networks for Redundancy Resolution 95 -- 6.5.1 Conversion to a Nonlinear Equation Set 95 -- 6.5.2 Neural Dynamics for Real-Time Redundancy Resolution 96 -- 6.5.3 Convergence Analysis 96 -- 6.6 Illustrative Examples 98 -- 6.6.1 Manipulability Optimization via Self Motion 98 -- 6.6.2 Manipulability Optimization in Circular Path Tracking 99 -- 6.6.3 Comparisons 102 -- 6.6.4 Summary 104 -- Part II Neural Networks for Parallel Robot Control 105 -- 7 Neural Network Based Stewart Platform Control 107 -- 7.1 Introduction 107 -- 7.2 Preliminaries 108 -- 7.3 Robot Kinematics 109 -- 7.3.1 Geometric Relation 109 -- 7.3.2 Velocity Space Resolution 111 -- 7.4 Problem Formulation as Constrained Optimization 112 -- 7.5 Dynamic Neural Network Model 113 -- 7.5.1 Neural Network Design 113 -- 7.6 Theoretical Results 115 -- 7.6.1 Optimality 115 -- 7.6.2 Stability 116. 7.6.3 Comparison with Other Control Schemes 117 -- 7.7 Numerical Investigation 118 -- 7.7.1 Simulation Setups 118 -- 7.7.2 Circular Trajectory 122 -- 7.7.3 Infinity-Sign Trajectory 127 -- 7.7.4 Square Trajectory 127 -- 7.8 Summary 129 -- 8 Neural Network Based Learning and Control Co-Design for Stewart Platform Control 131 -- 8.1 Introduction 131 -- 8.2 Kinematic Modeling of Stewart Platforms 133 -- 8.2.1 Geometric Relation 133 -- 8.2.2 Velocity Space Resolution 135 -- 8.3 Recurrent Neural Network Design 136 -- 8.3.1 Problem Formulation from an Optimization Perspective 136 -- 8.3.2 Neural Network Dynamics 138 -- 8.3.3 Stability 138 -- 8.3.4 Optimality 139 -- 8.4 Numerical Investigation 142 -- 8.4.1 Setups 142 -- 8.4.2 Circular Trajectory 143 -- 8.4.3 Square Trajectory 143 -- 8.5 Summary 145 -- Part III Neural Networks for Cooperative Control 147 -- 9 Zeroing Neural Networks for Robot Arm Motion Generation 149 -- 9.1 Introduction 149 -- 9.2 Preliminaries 151 -- 9.2.1 Problem Definition and Assumption 151 -- 9.2.1.1 Assumption 151 -- 9.2.2 Manipulator Kinematics 151 -- 9.3 Problem Formulation and Distributed Scheme 152 -- 9.3.1 Problem Formulation and Neural-Dynamic Design 152 -- 9.3.2 Distributed Scheme 153 -- 9.4 NTZNN Solver and Theoretical Analyses 153 -- 9.4.1 ZNN for Real-Time Redundancy Resolution 154 -- 9.4.2 Theoretical Analyses and Results 157 -- 9.5 Illustrative Examples 160 -- 9.5.1 Consensus to a Fixed Configuration 160 -- 9.5.2 Cooperative Motion Generation Perturbed by Noises 161 -- 9.5.3 ZNN-Based Solution Perturbed by Noises 162 -- 9.6 Summary 165 -- 10 Zeroing Neural Networks for Robot Arm Motion Generation 167 -- 10.1 Introduction 167 -- 10.2 Preliminaries, Problem Formulation, and Distributed Scheme 168 -- 10.2.1 Definition and Robot Arm Kinematics 168 -- 10.2.2 Problem Formulation 168 -- 10.2.3 Distributed Scheme 169 -- 10.3 NANTZNN Solver and Theoretical Analyses 169 -- 10.3.1 NANTZNN for Real-Time Redundancy Resolution 170 -- 10.3.2 Theoretical Analyses and Results 171. 10.4 Illustrative Examples 172 -- 10.4.1 Cooperative Motion Planning without Noises 174 -- 10.4.2 Cooperative Motion Planning with Noises 174 -- 10.5 Summary 175 -- Reference 177 -- Index 185. |
| Record Nr. | UNINA-9910533474203321 |
|
Li Shuai
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Kinematic control of redundant robot arms using neural networks / / [edited by] Shuai Li, Hong Kong Polytechnic University, Long Jin, Hong Kong Polytechnic University, Mohammed Aquil Mirza, Hong Kong Polytechnic University
| Kinematic control of redundant robot arms using neural networks / / [edited by] Shuai Li, Hong Kong Polytechnic University, Long Jin, Hong Kong Polytechnic University, Mohammed Aquil Mirza, Hong Kong Polytechnic University |
| Autore | Li Shuai |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , 2019 |
| Descrizione fisica | 1 online resource |
| Disciplina | 629.895632 |
| Collana | THEi Wiley ebooks. |
| Soggetto topico |
Robots - Kinematics - Data processing
Manipulators (Mechanism) - Automatic control Redundancy (Engineering) - Data processing |
| ISBN |
1-119-55699-6
1-119-55700-3 1-119-55698-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
List of Figures xiii -- List of Tables xix -- Preface xxi -- Acknowledgments xxv -- Part I Neural Networks for Serial Robot Arm Control 1 -- 1 Zeroing Neural Networks for Control 3 -- 1.1 Introduction 3 -- 1.2 Scheme Formulation and ZNN Solutions 4 -- 1.2.1 ZNN Model 4 -- 1.2.2 Nonconvex Function Activated ZNN Model 8 -- 1.3 Theoretical Analyses 9 -- 1.4 Computer Simulations and Verifications 12 -- 1.4.1 ZNN for Solving (1.13) at t = 1 12 -- 1.4.2 ZNN for Solving (1.13) with Different Bounds 15 -- 1.5 Summary 16 -- 2 Adaptive Dynamic Programming Neural Networks for Control 17 -- 2.1 Introduction 17 -- 2.2 Preliminaries on Variable Structure Control of the Sensor-Actuator System 18 -- 2.3 Problem Formulation 19 -- 2.4 Model-Free Control of the Euler-Lagrange System 20 -- 2.4.1 Optimality Condition 21 -- 2.4.2 Approximating the Action Mapping and the Critic Mapping 21 -- 2.5 Simulation Experiment 23 -- 2.5.1 The Model 23 -- 2.5.2 Experiment Setup and Results 24 -- 2.6 Summary 25 -- 3 Projection Neural Networks for Robot Arm Control 27 -- 3.1 Introduction 27 -- 3.2 Problem Formulation 29 -- 3.3 A Modified Controller without Error Accumulation 30 -- 3.3.1 Existing RNN Solutions 30 -- 3.3.2 Limitations of Existing RNN Solutions 32 -- 3.3.3 The Presented Algorithm 33 -- 3.3.4 Stability 34 -- 3.4 Performance Improvement Using Velocity Compensation 36 -- 3.4.1 A Control Law with Velocity Compensation 36 -- 3.4.2 Stability 37 -- 3.5 Simulations 41 -- 3.5.1 Regulation to a Fixed Position 41 -- 3.5.2 Tracking of Time-Varying References 42 -- 3.5.3 Comparisons 47 -- 3.6 Summary 50 -- 4 Neural Learning and Control Co-Design for Robot Arm Control 51 -- 4.1 Introduction 51 -- 4.2 Problem Formulation 52 -- 4.3 Nominal Neural Controller Design 53 -- 4.4 A Novel Dual Neural Network Model 54 -- 4.4.1 Neural Network Design 54 -- 4.4.2 Stability 56 -- 4.5 Simulations 62 -- 4.5.1 Simulation Setup 62 -- 4.5.2 Simulation Results 63 -- 4.5.2.1 Tracking Performance 63 -- 4.5.2.2 With vs.Without Excitation Noises 64.
4.6 Summary 66 -- 5 Robust Neural Controller Design for Robot Arm Control 67 -- 5.1 Introduction 67 -- 5.2 Problem Formulation 68 -- 5.3 Dual Neural Networks for the Nominal System 69 -- 5.3.1 Neural Network Design 69 -- 5.3.2 Convergence Analysis 71 -- 5.4 Neural Design in the Presence of Noises 72 -- 5.4.1 Polynomial Noises 72 -- 5.4.1.1 Neural Dynamics 73 -- 5.4.1.2 Practical Considerations 77 -- 5.4.2 Special Cases 78 -- 5.4.2.1 Constant Noises 78 -- 5.4.2.2 Linear Noises 80 -- 5.5 Simulations 81 -- 5.5.1 Simulation Setup 81 -- 5.5.2 Nominal Situation 81 -- 5.5.3 Constant Noises 82 -- 5.5.4 Time-Varying Polynomial Noises 86 -- 5.6 Summary 86 -- 6 Using Neural Networks to Avoid Robot Singularity 87 -- 6.1 Introduction 87 -- 6.2 Preliminaries 89 -- 6.3 Problem Formulation 90 -- 6.3.1 Manipulator Kinematics 90 -- 6.3.2 Manipulability 90 -- 6.3.3 Optimization Problem Formulation 91 -- 6.4 Reformulation as a Constrained Quadratic Program 91 -- 6.4.1 Equation Constraint: Speed Level Resolution 91 -- 6.4.2 Redefinition of the Objective Function 92 -- 6.4.3 Set Constraint 93 -- 6.4.4 Reformulation and Convexification 94 -- 6.5 Neural Networks for Redundancy Resolution 95 -- 6.5.1 Conversion to a Nonlinear Equation Set 95 -- 6.5.2 Neural Dynamics for Real-Time Redundancy Resolution 96 -- 6.5.3 Convergence Analysis 96 -- 6.6 Illustrative Examples 98 -- 6.6.1 Manipulability Optimization via Self Motion 98 -- 6.6.2 Manipulability Optimization in Circular Path Tracking 99 -- 6.6.3 Comparisons 102 -- 6.6.4 Summary 104 -- Part II Neural Networks for Parallel Robot Control 105 -- 7 Neural Network Based Stewart Platform Control 107 -- 7.1 Introduction 107 -- 7.2 Preliminaries 108 -- 7.3 Robot Kinematics 109 -- 7.3.1 Geometric Relation 109 -- 7.3.2 Velocity Space Resolution 111 -- 7.4 Problem Formulation as Constrained Optimization 112 -- 7.5 Dynamic Neural Network Model 113 -- 7.5.1 Neural Network Design 113 -- 7.6 Theoretical Results 115 -- 7.6.1 Optimality 115 -- 7.6.2 Stability 116. 7.6.3 Comparison with Other Control Schemes 117 -- 7.7 Numerical Investigation 118 -- 7.7.1 Simulation Setups 118 -- 7.7.2 Circular Trajectory 122 -- 7.7.3 Infinity-Sign Trajectory 127 -- 7.7.4 Square Trajectory 127 -- 7.8 Summary 129 -- 8 Neural Network Based Learning and Control Co-Design for Stewart Platform Control 131 -- 8.1 Introduction 131 -- 8.2 Kinematic Modeling of Stewart Platforms 133 -- 8.2.1 Geometric Relation 133 -- 8.2.2 Velocity Space Resolution 135 -- 8.3 Recurrent Neural Network Design 136 -- 8.3.1 Problem Formulation from an Optimization Perspective 136 -- 8.3.2 Neural Network Dynamics 138 -- 8.3.3 Stability 138 -- 8.3.4 Optimality 139 -- 8.4 Numerical Investigation 142 -- 8.4.1 Setups 142 -- 8.4.2 Circular Trajectory 143 -- 8.4.3 Square Trajectory 143 -- 8.5 Summary 145 -- Part III Neural Networks for Cooperative Control 147 -- 9 Zeroing Neural Networks for Robot Arm Motion Generation 149 -- 9.1 Introduction 149 -- 9.2 Preliminaries 151 -- 9.2.1 Problem Definition and Assumption 151 -- 9.2.1.1 Assumption 151 -- 9.2.2 Manipulator Kinematics 151 -- 9.3 Problem Formulation and Distributed Scheme 152 -- 9.3.1 Problem Formulation and Neural-Dynamic Design 152 -- 9.3.2 Distributed Scheme 153 -- 9.4 NTZNN Solver and Theoretical Analyses 153 -- 9.4.1 ZNN for Real-Time Redundancy Resolution 154 -- 9.4.2 Theoretical Analyses and Results 157 -- 9.5 Illustrative Examples 160 -- 9.5.1 Consensus to a Fixed Configuration 160 -- 9.5.2 Cooperative Motion Generation Perturbed by Noises 161 -- 9.5.3 ZNN-Based Solution Perturbed by Noises 162 -- 9.6 Summary 165 -- 10 Zeroing Neural Networks for Robot Arm Motion Generation 167 -- 10.1 Introduction 167 -- 10.2 Preliminaries, Problem Formulation, and Distributed Scheme 168 -- 10.2.1 Definition and Robot Arm Kinematics 168 -- 10.2.2 Problem Formulation 168 -- 10.2.3 Distributed Scheme 169 -- 10.3 NANTZNN Solver and Theoretical Analyses 169 -- 10.3.1 NANTZNN for Real-Time Redundancy Resolution 170 -- 10.3.2 Theoretical Analyses and Results 171. 10.4 Illustrative Examples 172 -- 10.4.1 Cooperative Motion Planning without Noises 174 -- 10.4.2 Cooperative Motion Planning with Noises 174 -- 10.5 Summary 175 -- Reference 177 -- Index 185. |
| Record Nr. | UNINA-9910813790403321 |
|
Li Shuai
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural & Bio-inspired Processing and Robot Control
| Neural & Bio-inspired Processing and Robot Control |
| Autore | Li Shuai |
| Pubbl/distr/stampa | Frontiers Media SA, 2019 |
| Descrizione fisica | 1 online resource (135 p.) |
| Collana | Frontiers Research Topics |
| Soggetto topico |
Neurosciences
Science: general issues |
| Soggetto non controllato |
bio-inspiration
Control Learning neurorobotics Robotics |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557680203321 |
|
Li Shuai
|
||
| Frontiers Media SA, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural Networks for Cooperative Control of Multiple Robot Arms / / by Shuai Li, Yinyan Zhang
| Neural Networks for Cooperative Control of Multiple Robot Arms / / by Shuai Li, Yinyan Zhang |
| Autore | Li Shuai |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (xv, 74 pages) : illustrations |
| Disciplina | 629.892 |
| Collana | SpringerBriefs in Computational Intelligence |
| Soggetto topico |
Automatic control
Robotics Mechatronics Neural networks (Computer science) Computer simulation Computational intelligence Computer science - Mathematics Control, Robotics, Mechatronics Mathematical Models of Cognitive Processes and Neural Networks Simulation and Modeling Computational Intelligence Computational Science and Engineering |
| ISBN | 981-10-7037-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Neural Networks Based Single Robot Arm Control for Visual Servoing -- Neural Networks for Robot Arm Cooperation with a Start Control Topology -- Neural Networks for Robot Arm Cooperation with a Hierarchical Control Topology -- Neural Networks for Robot Arm Cooperation with a Full Distributed Control Topology. |
| Record Nr. | UNINA-9910299561003321 |
|
Li Shuai
|
||
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||