EDP Sciences 1776290479 20260415 fre

laboutique.edpsciences.fr-R002598 03 01 01 R002598 00 ED E107 00 01 01 Time Series Predictive Control in Robotics 01 eng 08 232 03 22 25393880 17 01 P15 EDP Sciences & Science Press 01 01 P15 EDP Sciences & Science Press 11 00 20240509 02 01 002720 03 9782759835102 15 9782759835102 03 01 D1 EDP Sciences 45 03 laboutique.edpsciences.fr-002720 03 01 01 002720 03 9782759835102 15 9782759835102 10 EA E107 10 00 01 R002598 ED E107 1 10 01 02 Current Natural Sciences 01 01 Time Series Predictive Control in Robotics 1 A01 01 A2269 Hui LIU LIU, Hui Hui LIU Hui LIU is Professor and Vice dean of the School of Traffic & Transportation Engineering, Central South University, China. His main research interests include computational intelligence, intelligent robotics in traffic & transportation engineering, and nonlinear signal modeling & forecasting. He holds double Ph.D degrees from China (Traffic & Transportation Engineering, from Central South University in 2011) and Germany (Automation Engineering, from University of Rostock in 2013), and obtained his professorship degree in Automation Engineering from University of Rostock in 2016. He has published more than 100 international research papers and authorized beyond 100 invention patents in the field of robotics, data science, and time series predictive control, as the first inventor. 01 eng 08 230 03 01 24 Izibook:Subject Ingénierie 24 Izibook:SubjectAndCategoryAndTags |Ingénierie| 20 Robotics;intelligent control;learning;time series prediction;interdisciplinary;technology;theories;navigation;manipulation;unmanned vehicles;manufacturing systems;predictive control 10 2011 TEC037000 01 620 06 05 06 03 00 <blockquote>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.</blockquote> 02 00 This book explores advancements in robotics, intelligent control, and learning, presenting a unique approach to robot control through time series prediction. It serves as a vital reference for engineers, scientists, and students in the field. 04 00 Preface..................................................... III<o:p></o:p>Abbreviations................................................ V<o:p></o:p>CHAPTER 1<o:p></o:p>Introduction................................................. 1<o:p></o:p>1.1Robotics and Control Technology ............................ 1<o:p></o:p>1.1.1 Robotics ......................................... 1<o:p></o:p>1.1.2 Robotics Control Technology .......................... 4<o:p></o:p>1.2 Time Series Forecasting in Robotics Control .................... 5<o:p></o:p>1.2.1 Time Series Forecasting Objectives...................... 5<o:p></o:p>1.2.2 Time Series Forecasting Methods ....................... 8<o:p></o:p>1.3 Predictive Control in Robotics .............................. 10<o:p></o:p>1.3.1 Uncertainty Problems in Predictive Control of Robotics ...... 10<o:p></o:p>1.3.2 Model Predictive Control ............................. 13<o:p></o:p>1.3.3 Significance and Purpose of Research .................... 14<o:p></o:p>1.4 Scopeof This Book ....................................... 15<o:p></o:p>References.................................................. 18<o:p></o:p>CHAPTER 2<o:p></o:p>Robot Navigation Position Time Series Predictive Control .............. 23<o:p></o:p>2.1 Introduction ............................................ 23<o:p></o:p>2.2 Robot Navigation Position Time Series Measurement ............. 24<o:p></o:p>2.3 Robot Navigation Position Time Series Uncertainty Analysis ....... 25<o:p></o:p>2.4 Robot Navigation Position Time Series Statistical Forecasting Method................................................ 252.4.1 ARIMA Forecasting Algorithm ........................ 26<o:p></o:p>2.4.2 ARIMA-GARCH Forecasting Algorithm ................. 30<o:p></o:p>2.5 Robot Navigation Position Time Series Intelligent Forecasting Method................................................ 352.5.1 RBF Neural Network Forecasting Algorithm .............. 35<o:p></o:p>2.5.2 Elman Neural Network Forecasting Algorithm ............. 38<o:p></o:p>2.5.3 Extreme Learning Machine Forecasting Algorithm .......... 41<o:p></o:p>2.6 Robot Navigation Position Time Series Deep Learning Forecasting Method................................................ 442.6.1 LSTM Deep Neural Network Forecasting Algorithm ......... 45<o:p></o:p>2.6.2 ESN Deep Neural Network Forecasting Algorithm .......... 48<o:p></o:p>2.7 Comparative Analysis of Forecasting Performance ................ 51<o:p></o:p>2.8 Robot Anti-Collision Monitoring and Control Based on Navigation Position Forecasting ...................................... 522.9 Conclusions............................................. 53<o:p></o:p>References.................................................. 53<o:p></o:p>CHAPTER 3<o:p></o:p>Mobile Robot Power Time Series Predictive Control ................... 57<o:p></o:p>3.1Introduction ............................................ 57<o:p></o:p>3.2 Mobile Robot Power Time Series Measurement .................. 58<o:p></o:p>3.3 Mobile Robot Power Time Series Uncertainty Analysis ............ 59<o:p></o:p>3.4 Mobile Robot Power Time Series Statistical Forecasting Method ..... 60<o:p></o:p>3.4.1Experimental Design ................................ 60<o:p></o:p>3.4.2Modeling Steps .................................... 61<o:p></o:p>3.4.3Forecasting Results ................................. 63<o:p></o:p>3.5 Mobile Robot Power Time Series Intelligent Forecasting Method ..... 64<o:p></o:p>3.5.1Experimental Design ................................ 65<o:p></o:p>3.5.2Modeling Steps .................................... 68<o:p></o:p>3.5.3Forecasting Results ................................. 70<o:p></o:p>3.6 Mobile Robot Power Time Series Deep Learning Forecasting Method . 71<o:p></o:p>3.6.1Experimental Design ................................ 71<o:p></o:p>3.6.2Modeling Steps .................................... 73<o:p></o:p>3.6.3Forecasting Results ................................. 76<o:p></o:p>3.7Comparative Analysis of Forecasting Performance ................ 78<o:p></o:p>3.7.1Analysis of Statistical Methods ........................ 78<o:p></o:p>3.7.2Analysis of Intelligent Methods ........................ 78<o:p></o:p>3.7.3Analysis of Deep Learning Methods ..................... 79<o:p></o:p>3.8 Mobile Robot Delivery Process Control Based on Power Forecasting . . 80<o:p></o:p>3.9Conclusions............................................. 80<o:p></o:p>References.................................................. 81<o:p></o:p>CHAPTER 4<o:p></o:p>Robot Arm Time Series Predictive Control .......................... 83<o:p></o:p>4.1Introduction ............................................ 83<o:p></o:p>4.2 Robot Arm Time Series Measurement ......................... 84<o:p></o:p>4.3 Robot Arm Time Series Uncertainty Analysis ................... 85<o:p></o:p>4.4 Robot Arm Time Series Statistical Forecasting Method ............ 85<o:p></o:p>4.4.1Pandit–Wu Forecasting Algorithm ...................... 86<o:p></o:p>4.4.2KF-ARMA Forecasting Algorithm ...................... 88<o:p></o:p>4.5 Robot Arm Time Series Intelligent Forecasting Method............ 93<o:p></o:p>4.5.1 RELM Forecasting Algorithm ......................... 934.5.2XG Boost Forecasting Algorithm........................ 97<o:p></o:p>4.5.3 GRNN Forecasting Algorithm ......................... 101<o:p></o:p>4.6 Robot Arm Time-Series Deep Learning Forecasting Method ........ 104<o:p></o:p>4.6.1Autoencoder Deep Neural Network Forecasting Algorithm .... 104<o:p></o:p>4.6.2 Deep Belief Network Forecasting Algorithm ............... 107<o:p></o:p>4.7Comparative Analysis of Forecasting Performance ................ 110<o:p></o:p>4.7.1Analysis of Statistical Methods ........................ 110<o:p></o:p>4.7.2Analysis of Intelligent Methods ........................ 111<o:p></o:p>4.7.3Analysis of Deep Learning Methods ..................... 111<o:p></o:p>4.8 Robot Arm Positioning Control Based on Arm Forecasting ......... 112<o:p></o:p>4.9 Conclusions............................................. 113<o:p></o:p>References.................................................. 113<o:p></o:p>CHAPTER 5<o:p></o:p>Unmanned Vehicle Time Series Predictive Control .................... 115<o:p></o:p>5.1Introduction ............................................ 115<o:p></o:p>5.2Unmanned Vehicle Time Series Measurement ................... 118<o:p></o:p>5.3Unmanned Vehicle Time Series Uncertainty Analysis ............. 119<o:p></o:p>5.4Unmanned Vehicle Time Series Statistical Forecasting Method ...... 119<o:p></o:p>5.4.1Kalman Filter Forecasting Algorithm .................... 119<o:p></o:p>5.4.2 Fuzzy Time Series Forecasting Algorithm ................. 122<o:p></o:p>5.5 Unmanned Vehicle Time Series Intelligent Forecasting Method ...... 124<o:p></o:p>5.5.1 Elman Neural Network Forecasting Algorithm ............. 125<o:p></o:p>5.5.2 NAR Neural Network Forecasting Algorithm .............. 128<o:p></o:p>5.5.3 ANFIS Neural Network Forecasting Algorithm ............. 130<o:p></o:p>5.6 Unmanned Vehicle Time Series Deep Learning Forecasting Method ... 134<o:p></o:p>5.6.1 RNNDeep Neural Network Forecasting Algorithm .......... 134<o:p></o:p>5.6.2 LSTM Deep Neural Network Forecasting Algorithm ......... 137<o:p></o:p>5.6.3 GRU Deep Neural Network Forecasting Algorithm .......... 139<o:p></o:p>5.7Comparative Analysis of Forecasting Performance ................ 141<o:p></o:p>5.7.1Analysis of Statistical Methods ........................ 141<o:p></o:p>5.7.2Analysis of Intelligent Methods ........................ 142<o:p></o:p>5.7.3Analysis of Deep Learning Methods ..................... 142<o:p></o:p>5.8Unmanned Vehicle Navigation Control Based on Multi-Source Position Time Series Fusion ................................ 1425.8.1Unmanned Vehicle Fusion Positioning ................... 142<o:p></o:p>5.8.2Unmanned Vehicle Navigation Control ................... 144<o:p></o:p>5.9Unmanned Vehicle Charging Control Based on Multi-Source Power Time Series Fusion ....................................... 1455.10Conclusions............................................. 146<o:p></o:p>References.................................................. 147<o:p></o:p>CHAPTER 6<o:p></o:p>Wearable Assistive Robot Time Series Predictive Control ............... 151<o:p></o:p>6.1 Introduction ............................................ 151<o:p></o:p>6.2 Wearable Assistive Robot Time Series Measurement .............. 152<o:p></o:p>6.3 Wearable Assistive Robot Time Series Uncertainty Analysis ........ 154<o:p></o:p>6.4 Wearable Assistive Robot Time Series Statistical Forecasting Method . 155<o:p></o:p>6.4.1 Experimental Design ................................ 155<o:p></o:p>6.4.2 Modeling Step ..................................... 160<o:p></o:p>6.4.3 Forecasting Results ................................. 162<o:p></o:p>6.5 Wearable Assistive Robot Time Series Intelligent Forecasting Method . 165<o:p></o:p>6.5.1 Experimental Design ................................ 165<o:p></o:p>6.5.2 Modeling Step ..................................... 167<o:p></o:p>6.5.3 Forecasting Results ................................. 171<o:p></o:p>6.6Wearable Assistive Robot Time-Series Deep Learning Forecasting Method................................................ 1746.6.1Experimental Design ................................ 174<o:p></o:p>6.6.2Modeling Step ..................................... 176<o:p></o:p>6.6.3Forecasting Results ................................. 179<o:p></o:p>6.7Comparative Analysis of Forecasting Performance ................ 180<o:p></o:p>6.8Wearable Assistive Robot Motion Control Based on Forecasting ..... 181<o:p></o:p>6.9Conclusions............................................. 182<o:p></o:p>References.................................................. 183<o:p></o:p>CHAPTER 7<o:p></o:p>Intelligent Manufacturing Performance Prediction and Application ........ 187<o:p></o:p>7.1Introduction ............................................ 187<o:p></o:p>7.2 Data Acquisition ......................................... 189<o:p></o:p>7.2.1Data-Driven Method ................................ 190<o:p></o:p>7.2.2Model-Driven Method ............................... 190<o:p></o:p>7.3Prediction Modeling ...................................... 193<o:p></o:p>7.3.1Regression Algorithms ............................... 193<o:p></o:p>7.3.2Artificial Neural Network (ANN) ....................... 199<o:p></o:p>7.3.3Comparison Analysis ................................ 202<o:p></o:p>7.4Application ............................................. 204<o:p></o:p>7.4.1System Configuration ................................ 204<o:p></o:p>7.4.2 The Other Application ............................... 216<o:p></o:p>7.5Conclusions............................................. 217<o:p></o:p>References.................................................. 218<o:p></o:p> Preface..................................................... III<o:p></o:p>Abbreviations................................................ V<o:p></o:p>CHAPTER 1<o:p></o:p>Introduction................................................. 1<o:p></o:p>1.1Robotics and Control Technology ............................ 1<o:p></o:p>1.1.1Robotics ......................................... 1<o:p></o:p>1.1.2Robotics Control Technology .......................... 4<o:p></o:p>1.2 TimeSeries Forecasting in Robotics Control .................... 5<o:p></o:p>1.2.1 TimeSeries Forecasting Objectives...................... 5<o:p></o:p>1.2.2 TimeSeries Forecasting Methods ....................... 8<o:p></o:p>1.3Predictive Control in Robotics .............................. 10<o:p></o:p>1.3.1Uncertainty Problems in Predictive Control of Robotics ...... 10<o:p></o:p>1.3.2 ModelPredictive Control ............................. 13<o:p></o:p>1.3.3Significance and Purpose of Research .................... 14<o:p></o:p>1.4 Scopeof This Book ....................................... 15<o:p></o:p>References.................................................. 18<o:p></o:p>CHAPTER 2<o:p></o:p>RobotNavigation Position Time Series Predictive Control .............. 23<o:p></o:p>2.1Introduction ............................................ 23<o:p></o:p>2.2 RobotNavigation Position Time Series Measurement ............. 24<o:p></o:p>2.3 RobotNavigation Position Time Series Uncertainty Analysis ....... 25<o:p></o:p>2.4 RobotNavigation Position Time Series Statistical Forecasting Method................................................ 252.4.1 ARIMAForecasting Algorithm ........................ 26<o:p></o:p>2.4.2ARIMA-GARCH Forecasting Algorithm ................. 30<o:p></o:p>2.5 RobotNavigation Position Time Series Intelligent Forecasting Method................................................ 352.5.1 RBFNeural Network Forecasting Algorithm .............. 35<o:p></o:p>2.5.2 ElmanNeural Network Forecasting Algorithm ............. 38<o:p></o:p>2.5.3Extreme Learning Machine Forecasting Algorithm .......... 41<o:p></o:p>2.6 RobotNavigation Position Time Series Deep Learning Forecasting Method................................................ 442.6.1 LSTMDeep Neural Network Forecasting Algorithm ......... 45<o:p></o:p>2.6.2 ESNDeep Neural Network Forecasting Algorithm .......... 48<o:p></o:p>2.7Comparative Analysis of Forecasting Performance ................ 51<o:p></o:p>2.8 RobotAnti-Collision Monitoring and Control Based on Navigation PositionForecasting ...................................... 522.9Conclusions............................................. 53<o:p></o:p>References.................................................. 53<o:p></o:p>CHAPTER 3<o:p></o:p>MobileRobot Power Time Series Predictive Control ................... 57<o:p></o:p>3.1Introduction ............................................ 57<o:p></o:p>3.2 MobileRobot Power Time Series Measurement .................. 58<o:p></o:p>3.3 MobileRobot Power Time Series Uncertainty Analysis ............ 59<o:p></o:p>3.4 MobileRobot Power Time Series Statistical Forecasting Method ..... 60<o:p></o:p>3.4.1Experimental Design ................................ 60<o:p></o:p>3.4.2Modeling Steps .................................... 61<o:p></o:p>3.4.3Forecasting Results ................................. 63<o:p></o:p>3.5 MobileRobot Power Time Series Intelligent Forecasting Method ..... 64<o:p></o:p>3.5.1Experimental Design ................................ 65<o:p></o:p>3.5.2Modeling Steps .................................... 68<o:p></o:p>3.5.3Forecasting Results ................................. 70<o:p></o:p>3.6 MobileRobot Power Time Series Deep Learning Forecasting Method . 71<o:p></o:p>3.6.1Experimental Design ................................ 71<o:p></o:p>3.6.2Modeling Steps .................................... 73<o:p></o:p>3.6.3Forecasting Results ................................. 76<o:p></o:p>3.7Comparative Analysis of Forecasting Performance ................ 78<o:p></o:p>3.7.1Analysis of Statistical Methods ........................ 78<o:p></o:p>3.7.2Analysis of Intelligent Methods ........................ 78<o:p></o:p>3.7.3Analysis of Deep Learning Methods ..................... 79<o:p></o:p>3.8 MobileRobot Delivery Process Control Based on Power Forecasting . . 80<o:p></o:p>3.9Conclusions............................................. 80<o:p></o:p>References.................................................. 81<o:p></o:p>CHAPTER 4<o:p></o:p>Robot ArmTime Series Predictive Control .......................... 83<o:p></o:p>4.1Introduction ............................................ 83<o:p></o:p>4.2 RobotArm Time Series Measurement ......................... 84<o:p></o:p>4.3 RobotArm Time Series Uncertainty Analysis ................... 85<o:p></o:p>4.4 RobotArm Time Series Statistical Forecasting Method ............ 85<o:p></o:p>4.4.1Pandit–Wu Forecasting Algorithm ...................... 86<o:p></o:p>4.4.2KF-ARMA Forecasting Algorithm ...................... 88<o:p></o:p>4.5 RobotArm Time Series Intelligent Forecasting Method............ 93<o:p></o:p>4.5.1 RELMForecasting Algorithm ......................... 934.5.2XGBoost Forecasting Algorithm........................ 97<o:p></o:p>4.5.3 GRNNForecasting Algorithm ......................... 101<o:p></o:p>4.6 RobotArm Time-Series Deep Learning Forecasting Method ........ 104<o:p></o:p>4.6.1Autoencoder Deep Neural Network Forecasting Algorithm .... 104<o:p></o:p>4.6.2 DeepBelief Network Forecasting Algorithm ............... 107<o:p></o:p>4.7Comparative Analysis of Forecasting Performance ................ 110<o:p></o:p>4.7.1Analysis of Statistical Methods ........................ 110<o:p></o:p>4.7.2Analysis of Intelligent Methods ........................ 111<o:p></o:p>4.7.3Analysis of Deep Learning Methods ..................... 111<o:p></o:p>4.8 RobotArm Positioning Control Based on Arm Forecasting ......... 112<o:p></o:p>4.9Conclusions............................................. 113<o:p></o:p>References.................................................. 113<o:p></o:p>CHAPTER 5<o:p></o:p>UnmannedVehicle Time Series Predictive Control .................... 115<o:p></o:p>5.1Introduction ............................................ 115<o:p></o:p>5.2Unmanned Vehicle Time Series Measurement ................... 118<o:p></o:p>5.3Unmanned Vehicle Time Series Uncertainty Analysis ............. 119<o:p></o:p>5.4Unmanned Vehicle Time Series Statistical Forecasting Method ...... 119<o:p></o:p>5.4.1Kalman Filter Forecasting Algorithm .................... 119<o:p></o:p>5.4.2 FuzzyTime Series Forecasting Algorithm ................. 122<o:p></o:p>5.5Unmanned Vehicle Time Series Intelligent Forecasting Method ...... 124<o:p></o:p>5.5.1 ElmanNeural Network Forecasting Algorithm ............. 125<o:p></o:p>5.5.2 NARNeural Network Forecasting Algorithm .............. 128<o:p></o:p>5.5.3 ANFISNeural Network Forecasting Algorithm ............. 130<o:p></o:p>5.6Unmanned Vehicle Time Series Deep Learning Forecasting Method ... 134<o:p></o:p>5.6.1 RNNDeep Neural Network Forecasting Algorithm .......... 134<o:p></o:p>5.6.2 LSTMDeep Neural Network Forecasting Algorithm ......... 137<o:p></o:p>5.6.3 GRUDeep Neural Network Forecasting Algorithm .......... 139<o:p></o:p>5.7Comparative Analysis of Forecasting Performance ................ 141<o:p></o:p>5.7.1Analysis of Statistical Methods ........................ 141<o:p></o:p>5.7.2Analysis of Intelligent Methods ........................ 142<o:p></o:p>5.7.3Analysis of Deep Learning Methods ..................... 142<o:p></o:p>5.8Unmanned Vehicle Navigation Control Based on Multi-Source PositionTime Series Fusion ................................ 1425.8.1Unmanned Vehicle Fusion Positioning ................... 142<o:p></o:p>5.8.2Unmanned Vehicle Navigation Control ................... 144<o:p></o:p>5.9Unmanned Vehicle Charging Control Based on Multi-Source Power Time Series Fusion ....................................... 1455.10Conclusions............................................. 146<o:p></o:p>References.................................................. 147<o:p></o:p>CHAPTER 6<o:p></o:p>WearableAssistive Robot Time Series Predictive Control ............... 151<o:p></o:p>6.1Introduction ............................................ 151<o:p></o:p>6.2Wearable Assistive Robot Time Series Measurement .............. 152<o:p></o:p>6.3Wearable Assistive Robot Time Series Uncertainty Analysis ........ 154<o:p></o:p>6.4Wearable Assistive Robot Time Series Statistical Forecasting Method . 155<o:p></o:p>6.4.1Experimental Design ................................ 155<o:p></o:p>6.4.2Modeling Step ..................................... 160<o:p></o:p>6.4.3Forecasting Results ................................. 162<o:p></o:p>6.5Wearable Assistive Robot Time Series Intelligent Forecasting Method . 165<o:p></o:p>6.5.1Experimental Design ................................ 165<o:p></o:p>6.5.2Modeling Step ..................................... 167<o:p></o:p>6.5.3Forecasting Results ................................. 171<o:p></o:p>6.6Wearable Assistive Robot Time-Series Deep Learning Forecasting Method................................................ 1746.6.1Experimental Design ................................ 174<o:p></o:p>6.6.2Modeling Step ..................................... 176<o:p></o:p>6.6.3Forecasting Results ................................. 179<o:p></o:p>6.7Comparative Analysis of Forecasting Performance ................ 180<o:p></o:p>6.8Wearable Assistive Robot Motion Control Based on Forecasting ..... 181<o:p></o:p>6.9Conclusions............................................. 182<o:p></o:p>References.................................................. 183<o:p></o:p>CHAPTER 7<o:p></o:p>IntelligentManufacturing Performance Prediction and Application ........ 187<o:p></o:p>7.1Introduction ............................................ 187<o:p></o:p>7.2 DataAcquisition ......................................... 189<o:p></o:p>7.2.1Data-Driven Method ................................ 190<o:p></o:p>7.2.2Model-Driven Method ............................... 190<o:p></o:p>7.3Prediction Modeling ...................................... 193<o:p></o:p>7.3.1Regression Algorithms ............................... 193<o:p></o:p>7.3.2Artificial Neural Network (ANN) ....................... 199<o:p></o:p>7.3.3Comparison Analysis ................................ 202<o:p></o:p>7.4Application ............................................. 204<o:p></o:p>7.4.1System Configuration ................................ 204<o:p></o:p>7.4.2 TheOther Application ............................... 216<o:p></o:p>7.5Conclusions............................................. 217<o:p></o:p>References.................................................. 218<o:p></o:p> 21 00 06 01 https://laboutique.edpsciences.fr/produit/1405/9782759835102/time-series-predictive-control-in-robotics 01 00 03 02 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