| Description |
1 online resource (319 pages) : illustrations |
| Series |
Lecture Notes in Electrical Engineering, 1876-1119 ; Volume 283 |
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Lecture notes in electrical engineering ; v. 283.
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| Contents |
Part IIntelligent Control Systems and Optimization -- 1 Adaptive Flux Observers and Rotor Speed Sensor Fault Detection in Induction Motors -- 1.1 Introduction -- 1.2 Physical Modeling -- 1.3 Observer Design -- 1.4 Speed Sensor Fault Detection -- 1.5 Simulation Results -- 1.6 Experimental Results -- 1.7 Conclusions -- References -- 2 On Visual Analytics in Plant Monitoring -- 2.1 Introduction -- 2.2 State of the Art -- 2.2.1 Visual Analytics -- 2.2.2 Visualization of Multidimensional Data -- 2.2.3 Principal Component Analysis -- 2.3 Visual Data Exploration -- 2.3.1 Requirements for the Automation Domain -- 2.3.2 Discrete State Encoding -- 2.4 Evaluation of Visualization Methods -- 2.4.1 Discrete State Encoding of a Production Process -- 2.4.2 Visualization of the Principal Components -- 2.5 Anomaly Detection in Production Plants -- 2.5.1 Hybrid Visualization and Anomaly Detection Approach -- 2.5.2 Discussion -- 2.6 Conclusions -- References -- 3 Global Optimization for 2D SLAM Problem -- 3.1 Introduction -- 3.2 Nonlinear Least Square Formulation -- 3.3 Number of Local Minima in MAP Joining SLAM -- 3.4 Greedy Random Adaptive Search Procedure -- 3.5 Randomized Greedy Algorithm -- 3.6 Global Optimal Solution to Map-Joining -- 3.7 Results and Discussion -- 3.8 Conclusions and Future Works -- References -- 4 Stochastic Models and Optimization Algorithms for Decision Support in Spacecraft Control Systems Preliminary Design -- 4.1 Introduction -- 4.2 Problem Description -- 4.3 Technological Control Contour Modelling -- 4.4 Command-Programming Control Contour Modelling -- 4.5 Models Generalization -- 4.6 Optimization Algorithms Description -- 4.7 Algorithms Performance Evaluation -- 4.8 Self-Configuring Genetic Algorithm Application in Spacecraft Control System Design -- 4.9 Conclusions -- References |
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5 A Heuristic Control Algorithm for Robust Internal Model Control with Arbitrary Reference Model -- 5.1 Introduction -- 5.2 Preliminary Results -- 5.3 Solvability Conditions -- 5.4 An Analytic Algorithm for the Computation of the Controller Parameters -- 5.5 Robust Control for Position Tracking of a Hydraulic Actuator -- 5.5.1 Actuator Model -- 5.5.2 Robust Tracking Controller -- 5.5.3 An Analytic Algorithm for the Computation of the Controller Parameters -- 5.5.4 Simulation Results -- 5.6 Conclusions -- References -- 6 A Multi-Signal Variant for the GPU-Based Parallelization of Growing Self-Organizing Networks -- 6.1 Introduction -- 6.2 Methods -- 6.2.1 Growing Self-Organizing Networks -- 6.2.2 The Multi-Signal Variant -- 6.2.3 Graphics Processing Units -- 6.2.4 GPU-Based Parallel Implementation of the Single-Signal Algorithm -- 6.2.5 GPU-Based Parallel Implementation of the Multi-Signal Variant -- 6.3 Experimental Validation -- 6.3.1 Methods of Comparison -- 6.3.2 Behavior of the Multi-Signal Algorithm -- 6.3.3 GPU-Based Implementation Performances -- 6.4 Conclusions and Future Developments -- References -- Part IIRobotics and Automation -- 7 Office Delivery Robot Controlled by Modular Behavior Selection Networks with Planning Capability -- 7.1 Introduction -- 7.2 Hybrid Architecture -- 7.2.1 Behavior Network Modules -- 7.2.2 Planning of Goal Sequences -- 7.3 Experiments -- 7.3.1 Experimental Setup -- 7.3.2 Analysis of Experimental Result -- 7.4 Concluding Remarks -- References -- 8 Worst-Case Performance Analysis in 1-norm for an Automated Heavy Vehicle Platoon -- 8.1 Introduction -- 8.2 State-Space Model of Vehicle Platoons -- 8.3 Identification of Nominal Vehicle Models -- 8.3.1 Identification of the Smallest Unfalsified Parameter Sets for SISO Transfer Functions -- 8.3.2 Unfalsified ARX Model Set of Minimal Prediction Error in xx |
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8.3.3 Solution via Linear Programming -- 8.3.4 Identification of ARX Vehicle Models -- 8.3.5 Identification of OE Models of Minimal Error in xx -- 8.3.6 Identification of OE Vehicle Models -- 8.4 Performance Analysis -- 8.4.1 Effects of Platoon Heterogeneity -- 8.4.2 Effects of Actuator Uncertainties -- 8.5 Experimental Results -- 8.6 Conclusions -- References -- 9 Visual SLAM Based on Single Omnidirectional Views -- 9.1 Introduction -- 9.2 SLAM -- 9.2.1 View-Based Map -- 9.2.2 Map Building Process -- 9.2.3 Observation Model -- 9.2.4 Initializing New Views in the Map -- 9.3 Computing a Transformation Between Omni-Directional Images -- 9.3.1 Detection of Interest Points -- 9.3.2 Matching of Interest Points -- 9.3.3 Computing the Transformation -- 9.4 Results -- 9.4.1 SLAM with Real Data -- 9.5 Conclusions -- References -- 10 Metrics for Path Planning of Reconfigurable Robots in Uneven Terrain -- 10.1 Introduction -- 10.2 Related Work -- 10.3 Brief Algorithm Overview -- 10.4 Map and Roughness Quantification -- 10.4.1 Roughness Quantification -- 10.5 Metrics for the Initial Path Search -- 10.6 Metrics for Planning in Rough Areas -- 10.6.1 System Safety Metric -- 10.6.2 Execution Time Metric -- 10.7 Experimental Results -- 10.8 Conclusions and Future Work -- References -- 11 A Combined Direct and Indirect Adaptive Control Scheme for a Wheeled Mobile Robot using Multiple Models -- 11.1 Introduction -- 11.2 Kinematics and Dynamics -- 11.3 Control Scheme -- 11.3.1 Kinematic Controller -- 11.3.2 Adaptive Dynamic Controller -- 11.3.3 Adaptive Dynamic Controller with Multiple Models -- 11.3.4 Proof of Stability for the Kinematic Controller -- 11.3.5 Switching Criterion -- 11.4 Simulations -- 11.5 Conclusions -- References -- 12 Real-Time Visual Servoing Based on New Global Visual Features -- 12.1 Introduction |
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12.2 Random Distribution of Limited Set of Pixels Luminance as Visual Features -- 12.3 The Control Law Generation -- 12.4 Mobile Robot Visual Control -- 12.5 Experimental Results -- 12.5.1 Experimental Environment -- 12.5.2 Interpretation -- 12.5.3 Robustness with Respect to Image Content -- 12.6 Conclusions -- References -- 13 Compliance Error Compensation in Robotic-Based Milling -- 13.1 Introduction -- 13.2 Problem Statement -- 13.3 Manipulator Model -- 13.3.1 Elastostatic Model -- 13.3.2 Stiffness Matrix -- 13.3.3 Reduced Mass Matrix -- 13.4 Machining Process -- 13.5 Compliance Error Compensation Technique -- 13.6 Experimental Verification -- 13.7 Conclusions -- References -- 14 A Modified LGMD Based Neural Network for Automatic Collision Detection -- 14.1 Introduction -- 14.2 The Proposed LGMD Neural Network -- 14.3 Experimental Results on the Proposed Model -- 14.3.1 Simulated Environment -- 14.3.2 LGMD Model Validation -- 14.3.3 Results on Simulated Data Set and on Real Recorded Data -- 14.3.4 Results on a Real Robot -- 14.4 Conclusions -- References -- 15 Vision Based Motion Estimation of Obstacles in Dynamic Unstructured Environments -- 15.1 Introduction -- 15.2 System Architecture -- 15.3 Pre-Processing Level -- 15.3.1 Polyline-Based Environment Perception -- 15.3.2 Ego-Motion Compensation -- 15.3.3 Data Association -- 15.4 Motion Estimation -- 15.4.1 Matching -- 15.4.2 Outliers Rejection -- 15.4.3 Error Minimization -- 15.4.4 Updating -- 15.4.5 Testing the Convergence -- 15.5 Experimental Results -- 15.6 Conclusions -- References -- 16 Real-Time Vision-Based Pedestrian Detection in a Truck's Blind Spot Zone Using a Warping Window Approach -- 16.1 Introduction -- 16.2 Related Work -- 16.3 Pedestrian Tracking Algorithm -- 16.3.1 Warping Window Approach -- 16.3.2 Pedestrian Detector -- 16.3.3 Tracking Framework |
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16.4 Experiments and Results -- 16.4.1 Speed Analysis -- 16.4.2 Accuracy Analysis -- 16.4.3 Preliminary Bicycle Experiments -- 16.5 Conclusions and Future Work -- References -- 17 A Proposal of Risk Indexes at Signalised Intersections for ADAS Aimed to Road Safety -- 17.1 Introduction -- 17.2 State of the Art -- 17.3 Safety Analysis at Intersections -- 17.4 The Dilemma Zone and Role of Integrated I2V-ADAS -- 17.5 Driver's Behaviour and Risk Indexes -- 17.6 Conclusions -- References -- Part IIISignal Processing, Sensors, Systems Modeling and Control -- 18 A Component-Oriented Model for Wastewater Pumping Plants -- 18.1 Introduction -- 18.2 Functional Description -- 18.2.1 Screen Station -- 18.2.2 Pump Station -- 18.3 Model Derivation -- 18.3.1 Screening Process -- 18.3.2 Pumping Process -- 18.3.3 Complementary Modules -- 18.3.4 The Implementation Procedure -- 18.4 Simulation Results -- 18.5 Summary and Outlook -- References -- 19 A System Identification Framework for Modeling Complex Combustion Dynamics Using Support Vector Machines -- 19.1 Introduction -- 19.2 Support Vector Regression for Nonlinear Identification -- 19.3 HCCI System and Experiments -- 19.4 SVR Modeling Framework -- 19.4.1 Data Preprocessing -- 19.4.2 Regressor Variables -- 19.4.3 Model Selection -- 19.4.4 SVR Prediction Results -- 19.4.5 Multi-Step-Ahead Predictions -- 19.5 Conclusions and Future Work -- References -- Author Index -- Preface -- Organization -- Contents |
| Summary |
Annotation This book includes extended and revised versions of a set of selected papers from the Ninth International Conference on Informatics in Control Automation and Robotics (ICINCO 2012), held in Rome, Italy, from 28 to 31 July 2012. The conference was organized in four simultaneous tracks: Intelligent Control Systems and Optimization, Robotics and Automation, Systems Modeling, Signal Processing and Control and Industrial Engineering, Production and Management. ICINCO 2012 received 360 paper submissions, from 58 countries in all continents. From these, after a blind review process, only 40 were accepted as full papers, of which 20 were selected for inclusion in this book, based on the classifications provided by the Program Committee. The selected papers reflect the interdisciplinary nature of the conference as well as the logic equilibrium between the four abovementioned tracks. The diversity of topics is an important feature of this conference, enabling an overall perception of several important scientific and technological trends |
| Bibliography |
Includes bibliographical references at the end of each chapters and index |
| Notes |
Print version record |
| Subject |
Artificial intelligence.
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Artificial Intelligence
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artificial intelligence.
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Ingénierie.
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Artificial intelligence
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| Genre/Form |
proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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| Form |
Electronic book
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| Author |
Ferrier, Jean Louis
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| LC no. |
2013956806 |
| ISBN |
9783319035000 |
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3319035002 |
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3319034995 |
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9783319034997 |
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