機(jī)器人學(xué)：時(shí)間序列預(yù)測控制（英文版）
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- 作者：
- 出版時(shí)間：2024/3/1
- ISBN：9787030782595
- 出 版 社：科學(xué)出版社
- 中圖法分類：TP24
- 頁碼：
- 紙張：
- 版次：1
- 開本：B5
本書提出了機(jī)器人系統(tǒng)時(shí)間序列建模與健康監(jiān)測理論與方法，內(nèi)容分為6章。第1章介紹機(jī)器人系統(tǒng)概念性問題和機(jī)器人系統(tǒng)時(shí)序建模的關(guān)鍵基礎(chǔ)問題；第2章闡述機(jī)器人導(dǎo)航時(shí)序建模與健康監(jiān)測理論及應(yīng)用；第3章闡述機(jī)器人車載電量時(shí)序建模與健康監(jiān)測理論及應(yīng)用；第4章闡述機(jī)器人手臂時(shí)序建模與健康監(jiān)測理論及應(yīng)用；第5章闡述無人駕駛車輛時(shí)序預(yù)測與多源時(shí)序融合理論及應(yīng)用；第6章闡述可穿戴輔助機(jī)器人力-位置時(shí)序建模與健康監(jiān)測理論及應(yīng)用。本書內(nèi)容豐富，結(jié)構(gòu)清晰，敘述深入淺出，并且每章都有不同模型的性能對(duì)比分析與結(jié)論，方便讀者理解。
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劉輝帶領(lǐng)國際化的研究團(tuán)隊(duì)在"自動(dòng)化"和"智能交通"領(lǐng)域所取得的研究成果引起國際極大關(guān)注，曾被德國主流新聞媒體"波羅的海日?qǐng)?bào)"Ostsee-Zeitung以"人物專訪"的形式進(jìn)行整版報(bào)道，并被德國最具影響力的技術(shù)評(píng)論期刊之一"Laborjournal"評(píng)為全德國2014年度"實(shí)驗(yàn)室自動(dòng)化"領(lǐng)域最優(yōu)秀的四個(gè)技術(shù)成果之一。
Contents
Preface III
Abbreviations V
CHAPTER 1 Introduction 1
1.1 Robotics 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 Scope of 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 ARIMAForecastingAlgorithm 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
3.1 Introduction 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
3.4.1 Experimental Design 60
3.4.2 Modeling Steps 61
3.4.3 Forecasting Results 63
3.5 Mobile Robot Power Time Series Intelligent Forecasting Method 64
3.5.1 Experimenta lDesign 65
3.5.2 Modeling Steps 68
3.5.3 Forecasting Results 70
3.6 Mobile Robot Power Time Series Deep Learning Forecasting Method 71
3.6.1 Experimental Design 71
3.6.2 Modeling Steps 73
3.6.3 Forecasting Results 76
3.7 Comparative Analysis of Forecasting Performance 78
3.7.1 Analysis of Statistical Methods 78
3.7.2 Analysis of Intelligent Methods 78
3.7.3 Analysis of Deep Learning Methods 79
3.8 Mobile Robot Delivery Process Control Based on Power Forecasting 80
3.9 Conclusions 80
References 81
CHAPTER 4 Robot Arm Time Series Predictive Control 83
4.1 Introduction 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
4.4.1 Pandit–Wu Forecasting Algorithm 86
4.4.2 KF-ARMA Forecasting Algorithm 88
4.5 Robot Arm Time Series Intelligent Forecasting Method 93
4.5.1 RELM Forecasting Algorithm 93
4.5.2 XGBoost Forecasting Algorithm 97
4.5.3 GRNN Forecasting Algorithm 101
4.6 Robot Arm Time-Series Deep Learning Forecasting Method 104
4.6.1 Autoencoder Deep Neural Network Forecasting Algorithm 104
4.6.2 Deep Belief Network Forecasting Algorithm 107
4.7 Comparative Analysis of Forecasting Performance 110
4.7.1 Analysis of Statistical Methods 110
4.7.2 Analysis of Intelligent Methods 111
4.7.3 Analysis 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
5.1 Introduction 115
5.2 Unmanned Vehicle Time Series Measurement 118
5.3 Unmanned Vehicle Time Series Uncertainty Analysis 119
5.4 Unmanned Vehicle Time Series Statistical Forecasting Method 119
5.4.1 Kalman 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 RNN Deep 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
5.7 Comparative Analysis of Forecasting Performance 141
5.7.1 Analysis of Statistical Methods 141
5.7.2 Analysis of Intelligent Methods 142
5.7.3 Analysis of Deep Learning Methods 142
5.8 Unmanned Vehicle Navigation Control Based on Multi-Source PositionTimeSeriesFusion 142
5.8.1 Unmanned Vehicle Fusion Positioning 142
5.8.2 Unmanned Vehicle Navigation Control 144
5.9 Unmanned Vehicle Charging Control Based on Multi-Source Power TimeSeriesFusion 145
5.10 Conclusions 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 Experimenta lDesign 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 ExperimentalDesign 165
6.5.2 Modeling Step 167
6.5.3 Forecasting Results 171
6.6 Wearable Assistive Robot Time-Series Deep Learning Forecasting Method 174
6.6.1 Experimenta lDesign 174
6.6.2 Modeling Step 176
6.6.3 Forecasting Results 179
6.7 Comparative Analysis of Forecasting Performance 180
6.8 Wearable Assistive Robot Motion Control Based on Forecasting 181
6.9 Conclusions 182
References 183
CHAPTER 7 Intelligent Manufacturing Performance Prediction and Application 187
7.1 Introduction 187
7.2 Data Acquisition 189
7.2.1 Data-Driven Method 190
7.2.2 Model-Driven Method 190
7.3 Prediction Modeling 193
7.3.1 Regression Algorithms 193
7.3.2 Artificial Neural Network(ANN) 199
7.3.3 Comparison Analysis 202
7.4 Application 204
7.4.1 System Configuration 204
7.4.2 The Other Application 216
7.5 Conclusions 217
References 218
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