**
Contents**
Contributors
Preface
**1. Electrostatic Inchworm
Motors Driven by High-Voltage Si Photovoltaic Cells for
Millimeter Scale Multi-Legged Microrobots**
1.1. Introduction
1.2. Multi-Legged Microrobot
1.3. Electrostatic Inchworm Motors
1.4. High-Voltage Si PV Cells
1.5. Experimental Results
1.6. Conclusions
Acknowledgements
References
**2. Adaptive Trajectory Tracking Control and Dynamic
Redundancy Resolution of Nonholonomic Mobile Manipulators**
2.1. Introduction
2.2. System Description
2.3. Redundancy Resolution by Extended Formulation
2.4. Control Design
2.4.1. Passive Control (PC) Design
2.4.2. Adaptive Passive Control (APC) Design
2.5. Simulation Results
2.6. Conclusions
Acknowledgements
References
**3. An Automated On-line Novel Visual Percept Detection
Method for Mobile Robot and Video Surveillance**
3.1. Overview
3.2. Introduction
3.3. A Percept Learning System
3.3.1. Feature Generation
3.3.2. Similarity Measure
3.3.3. Percept Formation
3.3.4. Fast Search by Database Tree
3.4. An On-line Novelty Detection Method
3.4.1. Threshold Selection
3.4.2. Eight-Connected Structure Element Filter
3.4.3. Tree Insertion Operation
3.5. Experiments and Results
3.5.1. Experiment I: An Indoor Environment
3.5.2. Experiment II: An Outdoor Environment
3.6. Conclusions
Acknowledgements
References
**4. Dynamics and Control of a Centrifuge Flight Simulator
and a Simulator for Spatial Disorientation**
4.1. Introduction
4.2. Kinematics and Dynamics of the Centrifuge
4.2.1. Forward Geometric Model of the Centrifuge
4.2.2. Forward Kinematics Related to the Centrifuge Velocities
and Accelerations
4.2.3. Centrifuge Dynamics
4.3. Acceleration Forces and Link Angles of the Centrifuge
4.3.1. Calculation of the Simulator Pilot Acceleration Force
Components
4.3.2. Calculation of the Centrifuge Roll and Pitch Angles
4.4. The Control Algorithm of the Centrifuge Movement
4.4.1. Calculation of the Centrifuge Arm Angular Acceleration q
̈_1
4.4.2. Smoothing the Acceleration Force G Profile
4.4.3. Calculation of the Desired and Maximal Possible Values of
q ̈_1, q ̈_2 and q ̈_3
4.4.4. Centrifuge Control Algorithm (Algorithm 4.2)
4.5. Programming Instruction of the Centrifuge Movement
4.6. Results: Verification for the Proposed Control Algorithm
4.7. Kinematics and Dynamics of the SDT
4.7.1. Forward Geometric Model of the SDT
4.7.2. Forward Kinematics Related to the SDT Velocities and
Accelerations
4.7.3. SDT Dynamics
4.8. Acceleration Forces and Link Angles of the SDT
4.8.1. Calculation of the SDT Simulator Pilot Acceleration Force
Components
4.8.2. Calculation of the Roll and Pitch Angles of the SDT
4.9. The Control Algorithm of the SDT Movement
4.9.1. Calculation of the Maximum Possible Value of q ̈_1
4.9.2. Calculation of the Maximum Possible Values of q ̈_2, q
̈_3 and q ̈_4
4.9.3. Algorithm for Calculating the Maximum Possible Values of
q ̈_1, q ̈_2, q ̈_3 and q ̈_4 Based on Approximate Forward
Dynamics
4.10. Results: Verification for the Proposed Control Algorithm
4.11. Conclusions
Acknowledgements
References
**5. PCBN Tool Wear Modes and Mechanisms in Finish Hard
Turning**
5.1. Introduction
5.2. PCBN Tool Materials
5.3. Cutting Tool Wear
5.4. PCBN Tool Wear Mechanisms
5.4.1. Abrasion
5.4.2. Diffusion and Adhesion
5.4.3. Built Up Layer (Chemical Reaction)
5.5. Factors that Influence PCBN Tool Wear
5.5.1. PCBN Tool Material Composition
5.5.2. Tool Edge Geometry
5.5.3. Machine Tool Requirements
5.6. Summary
References
**6. Simulation Study of a Constant Time Hybrid Approach
for Large Scale Terrain Mapping Using Satellite Stereo Imagery**
6.1. Introduction
6.2. Related Work
6.3. Problem Formulation
6.3.1. Simulation Environment
6.3.2. Representation of the Environment
6.3.3. Bundle Adjustment Technique
6.3.4. Graph Weight Computation
6.4. Loop Closure
6.5. Theoretical Justification of the Hybrid Approach -
Integrating RSLAM with Particle Filters
6.5.1. Desired Asymptotic Properties
6.5.2. Properties of Maximum Likelihood (ml) Estimators in
Bundle Adjustment
6.5.3. Advantages and Asymptotic Properties of Particle Filter
6.6. Landmark-Formation in Stereo Environment
6.6.1. SURF Based Feature Detection
6.6.2. Landmark Characterization
6.7. Data Association Based on Landmark Matching
6.8. Matching Landmarks Using Fuzzy Similarity
6.8.1. Fuzzy Landmarks
6.8.2. Fuzzy Set Terminologies
6.8.3. Similarity Metric: Fuzzy Similarity
6.8.4. The Algorithm for Fuzzy Landmark Matching
6.9. Map Building
6.9.1. Disparity Computation
6.10. Experimental Results
6.10.1. Effect of Topographic Labelling on System Performance
198
6.10.2. Example-1
6.10.3. Example-2
6.10.4. Example-3
6.10.5. Simulation with Proposed Model
6.10.6. Comparison with Existing Models
6.11. Future Work: Improvement of the Hybrid Approach Using
Auxiliary Particle Filter
6.12. Summary and Conclusion
Acknowledgements
References
**7. An Overview of Systems, Control and Optimisation (SCO)
in Recent European R&D Programmes and Projects (2013-2017)
**
under
the Emergence of New Concepts and Broad Industrial Initiatives
7.1. Introduction
7.1.1. The Broad R&D landscape for Systems, Control and
Optimisation (SCO)
7.1.2. Structure of the Paper and Profiling of Projects
7.1.3. Topics in Projects vs. Topics Supported by Key Scientific
Societies
7.1.4. Mapping Projects Content According to Inherent Single, or
Multiple Innovations
7.1.5. The Sample of R&D Projects Considered
7.2. Systems, Control, Control Systems, Control in Systems and
“No-control”
7.2.1. Terminology, Evolution, Explanations, Different Views
7.2.2. Another Consideration: Focus on Control vs. Focus on
Applications (as addressed in Projects)
7.2.3. The Important Role of Sensors and Actuators
7.2.4. Challenges for Systems and Control Other Than Explicit
Feedback Arrangements of Fig. 7.4
7.3. The European R&D scene: European Commission, National &
Other Programmes
7.3.1. Outline
7.3.2. The Main R&D&I Programmes in the European Union (EU)
7.3.4. National and Other Programmes
7.3.5. Renewal of R&D Programmes
7.3.6. Views and some criticism about the Control Domain
7.3.7. What Communities Deal with Systems and Control R&D?
7.4. Project Categories and Examples Regarding Systems, Control
and Optimisation
7.4.1. Preliminary Remarks
7.4.2. Project Examples, According to Their SCO Content, a
Bottom up View
7.4.3. SCO Topics in Large Scale and Broad Scope Projects
7.4.4. Aerospace Research and SCO Topics
7.4.5. Alternative and Other Interesting SCO Applications
7.5. Concluding Remarks and new challenges
Summary
References
Appendix
7.A1. Additional Information on Selective Project Groups in SCO
Topics
7.A2. Project Groups
7.A2.1. Algebraic and Geometric Methods (See Also under PDEs
Group Below)
7.A2.2. Automata-based System Design
7.A2.3. Dynamical Systems
7.A2.4. Non-linear Systems and Bifurcations
7.A2.5. Complex Systems
7.A2.6. Formal Methods
7.A2.7. Consensus Methods (Including Non-ICT / Non-engineering
Systems)
7.A2.8. PDEs, System Modelling (e.g. Control for Wave, -HD, -MHD
Equations)
7.A2.9. Symbolic Control
7.A2.10. Robotics
7.A2.11. Self Organisation & Self-assembling Systems
7.A2.12. Decision Making/Processes, Markov DP, POMDP,
Multi-agents Systems
7.A2.13. Control of Embryonic Stem Cells Systems - Regulatory
Systems
7.A2.14. Advanced Controller Synthesis - Novel Concepts and
Methods
**8. Model Detection Using Innovations Squared Mismatch
Method: Application to Probe Based Data Storage System**
8.1. Introduction
8.2. Real Time Plant Detection with Innovations Squared Mismatch
(ISM)
8.2.1. Preliminaries, Problem Formulation and MAP
8.2.2. Innovations Squared Mismatch (ISM)
8.3. Plant Detection with Known Dwell Interval; Sequence
Detection
8.4. Application of ISM and ISM-MLSD to Probe Based Data Storage
8.4.1. Validation of Equivalent Model and Detection Framework
8.4.2. Detection Performance from Experiments
8.4.3. Symbol by Symbol and Sequence Detection
8.5. Conclusions
References
**9. H∞ Tracking Adaptive Fuzzy Sliding Mode Design
Controller for a Class of Non Square Nonlinear Systems**
9.1. Introduction
9.2. Generalized Conventional Sliding Mode
9.3. Design of a Robust Adaptive Fuzzy Controller
9.4. Simulation Results
9.5. Conclusion
References
**10. Analytical Solution of Optimized Energy Consumption
of Induction Motor Operating in Transient Regime**
10.1. Minimization of a Cost-to-go Function under Constraints
10.1.1. Statement of Optimal Control Problem with a Final Free
State
10.1.2. Reformulation of the Optimal Control Problem
10.2. Optimal Control via the Hamilton-Jacobi-Bellman Equation
10.2.1. Determination of the HJB Equation
10.3. Determination of the HJB Equation in the Case of IM
Minimum Energy Control
10.3.1. Solving the HJB Equation
10.4. Implementation of the Optimal Solution Obtained by HJB
Equation
10.4.1. Determination of the Rotor Flux Optimum Trajectory
10.4.2. Implementation of the Flux Optimal Trajectory in the
VECTOR CONTROL Structure
10.5. Conclusion
References
**Index** |