Time Series Predictive Control in Robotics

232 pages

English

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EDP Sciences - Hui Liu

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232 pages

English

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Description

This book presents the latest advances for the frontier cross disciplinary field of robotics, intelligent control and learning. Seven chapters are provided to cover the key common theories and technologies of robots, including the robot mapping and navigation, robot recharging and smart power management, robot arm manipulation, unmanned vehicle control, intelligent manufacturing systems, etc. The book proposes a unique new perspective using time series prediction to control robots. Especially with the fast increasing of various data in robotics, this new robot control mode using time series prediction has become very important. The book provides the complete cases for the most popular application scenes of robot predictive control. By this first monograph on the topic of robot time series predictive control in the world, author provides important references for the engineers, scientists and students in the field of robotics and artificial intelligence.

Preface..................................................... III

Abbreviations................................................ V

CHAPTER 1

Introduction................................................. 1

1.1Robotics and Control Technology ............................ 1

1.1.1 Robotics ......................................... 1

1.1.2 Robotics Control Technology .......................... 4

1.2 Time Series Forecasting in Robotics Control .................... 5

1.2.1 Time Series Forecasting Objectives...................... 5

1.2.2 Time Series Forecasting Methods ....................... 8

1.3 Predictive Control in Robotics .............................. 10

1.3.1 Uncertainty Problems in Predictive Control of Robotics ...... 10

1.3.2 Model Predictive Control ............................. 13

1.3.3 Significance and Purpose of Research .................... 14

1.4 Scopeof This Book ....................................... 15

References.................................................. 18

CHAPTER 2

Robot Navigation Position Time Series Predictive Control .............. 23

2.1 Introduction ............................................ 23

2.2 Robot Navigation Position Time Series Measurement ............. 24

2.3 Robot Navigation Position Time Series Uncertainty Analysis ....... 25

2.4 Robot Navigation Position Time Series Statistical Forecasting Method................................................ 25

2.4.1 ARIMA Forecasting Algorithm ........................ 26

2.4.2 ARIMA-GARCH Forecasting Algorithm ................. 30

2.5 Robot Navigation Position Time Series Intelligent Forecasting Method................................................ 35

2.5.1 RBF Neural Network Forecasting Algorithm .............. 35

2.5.2 Elman Neural Network Forecasting Algorithm ............. 38

2.5.3 Extreme Learning Machine Forecasting Algorithm .......... 41

2.6 Robot Navigation Position Time Series Deep Learning Forecasting Method................................................ 44

2.6.1 LSTM Deep Neural Network Forecasting Algorithm ......... 45

2.6.2 ESN Deep Neural Network Forecasting Algorithm .......... 48

2.7 Comparative Analysis of Forecasting Performance ................ 51

2.8 Robot Anti-Collision Monitoring and Control Based on Navigation Position Forecasting ...................................... 52

2.9 Conclusions............................................. 53

References.................................................. 53

CHAPTER 3

Mobile Robot Power Time Series Predictive Control ................... 57

3.1Introduction ............................................ 57

3.2 Mobile Robot Power Time Series Measurement .................. 58

3.3 Mobile Robot Power Time Series Uncertainty Analysis ............ 59

3.4 Mobile Robot Power Time Series Statistical Forecasting Method ..... 60

3.4.1Experimental Design ................................ 60

3.4.2Modeling Steps .................................... 61

3.4.3Forecasting Results ................................. 63

3.5 Mobile Robot Power Time Series Intelligent Forecasting Method ..... 64

3.5.1Experimental Design ................................ 65

3.5.2Modeling Steps .................................... 68

3.5.3Forecasting Results ................................. 70

3.6 Mobile Robot Power Time Series Deep Learning Forecasting Method . 71

3.6.1Experimental Design ................................ 71

3.6.2Modeling Steps .................................... 73

3.6.3Forecasting Results ................................. 76

3.7Comparative Analysis of Forecasting Performance ................ 78

3.7.1Analysis of Statistical Methods ........................ 78

3.7.2Analysis of Intelligent Methods ........................ 78

3.7.3Analysis of Deep Learning Methods ..................... 79

3.8 Mobile Robot Delivery Process Control Based on Power Forecasting . . 80

3.9Conclusions............................................. 80

References.................................................. 81

CHAPTER 4

Robot Arm Time Series Predictive Control .......................... 83

4.1Introduction ............................................ 83

4.2 Robot Arm Time Series Measurement ......................... 84

4.3 Robot Arm Time Series Uncertainty Analysis ................... 85

4.4 Robot Arm Time Series Statistical Forecasting Method ............ 85

4.4.1Pandit–Wu Forecasting Algorithm ...................... 86

4.4.2KF-ARMA Forecasting Algorithm ...................... 88

4.5 Robot Arm Time Series Intelligent Forecasting Method............ 93

4.5.1 RELM Forecasting Algorithm ......................... 93

4.5.2XG Boost Forecasting Algorithm........................ 97

4.5.3 GRNN Forecasting Algorithm ......................... 101

4.6 Robot Arm Time-Series Deep Learning Forecasting Method ........ 104

4.6.1Autoencoder Deep Neural Network Forecasting Algorithm .... 104

4.6.2 Deep Belief Network Forecasting Algorithm ............... 107

4.7Comparative Analysis of Forecasting Performance ................ 110

4.7.1Analysis of Statistical Methods ........................ 110

4.7.2Analysis of Intelligent Methods ........................ 111

4.7.3Analysis of Deep Learning Methods ..................... 111

4.8 Robot Arm Positioning Control Based on Arm Forecasting ......... 112

4.9 Conclusions............................................. 113

References.................................................. 113

CHAPTER 5

Unmanned Vehicle Time Series Predictive Control .................... 115

5.1Introduction ............................................ 115

5.2Unmanned Vehicle Time Series Measurement ................... 118

5.3Unmanned Vehicle Time Series Uncertainty Analysis ............. 119

5.4Unmanned Vehicle Time Series Statistical Forecasting Method ...... 119

5.4.1Kalman Filter Forecasting Algorithm .................... 119

5.4.2 Fuzzy Time Series Forecasting Algorithm ................. 122

5.5 Unmanned Vehicle Time Series Intelligent Forecasting Method ...... 124

5.5.1 Elman Neural Network Forecasting Algorithm ............. 125

5.5.2 NAR Neural Network Forecasting Algorithm .............. 128

5.5.3 ANFIS Neural Network Forecasting Algorithm ............. 130

5.6 Unmanned Vehicle Time Series Deep Learning Forecasting Method ... 134

5.6.1 RNNDeep Neural Network Forecasting Algorithm .......... 134

5.6.2 LSTM Deep Neural Network Forecasting Algorithm ......... 137

5.6.3 GRU Deep Neural Network Forecasting Algorithm .......... 139

5.7Comparative Analysis of Forecasting Performance ................ 141

5.7.1Analysis of Statistical Methods ........................ 141

5.7.2Analysis of Intelligent Methods ........................ 142

5.7.3Analysis of Deep Learning Methods ..................... 142

5.8Unmanned Vehicle Navigation Control Based on Multi-Source Position Time Series Fusion ................................ 142

5.8.1Unmanned Vehicle Fusion Positioning ................... 142

5.8.2Unmanned Vehicle Navigation Control ................... 144

5.9Unmanned Vehicle Charging Control Based on Multi-Source Power Time Series Fusion ....................................... 145

5.10Conclusions............................................. 146

References.................................................. 147

CHAPTER 6

Wearable Assistive Robot Time Series Predictive Control ............... 151

6.1 Introduction ............................................ 151

6.2 Wearable Assistive Robot Time Series Measurement .............. 152

6.3 Wearable Assistive Robot Time Series Uncertainty Analysis ........ 154

6.4 Wearable Assistive Robot Time Series Statistical Forecasting Method . 155

6.4.1 Experimental Design ................................ 155

6.4.2 Modeling Step ..................................... 160

6.4.3 Forecasting Results ................................. 162

6.5 Wearable Assistive Robot Time Series Intelligent Forecasting Method . 165

6.5.1 Experimental Design ................................ 165

6.5.2 Modeling Step ..................................... 167

6.5.3 Forecasting Results ................................. 171

6.6Wearable Assistive Robot Time-Series Deep Learning Forecasting Method................................................ 174

6.6.1Experimental Design ................................ 174

6.6.2Modeling Step ..................................... 176

6.6.3Forecasting Results ................................. 179

6.7Comparative Analysis of Forecasting Performance ................ 180

6.8Wearable Assistive Robot Motion Control Based on Forecasting ..... 181

6.9Conclusions............................................. 182

References.................................................. 183

CHAPTER 7

Intelligent Manufacturing Performance Prediction and Application ........ 187

7.1Introduction ............................................ 187

7.2 Data Acquisition ......................................... 189

7.2.1Data-Driven Method ................................ 190

7.2.2Model-Driven Method ............................... 190

7.3Prediction Modeling ...................................... 193

7.3.1Regression Algorithms ............................... 193

7.3.2Artificial Neural Network (ANN) ....................... 199

7.3.3Comparison Analysis ................................ 202

7.4Application ............................................. 204

7.4.1System Configuration ................................ 204

7.4.2 The Other Application ............................... 216

7.5Conclusions............................................. 217

References.................................................. 218

Sujets

Publié par	EDP Sciences
Date de parution	09 mai 2024
Nombre de lectures	1
EAN13	9782759835102
Langue	English
Poids de l'ouvrage	24 Mo

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