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• Contents
• Preface
• Acknowledgments
• Editors
• Contributors
• Chapter 1: Mechatronic Engineering
• 1.1 Introduction
• 1.2 Modeling and Design
• 1.3 Mechatronic Design Concept
• 1.3.1 Coupled Design
• 1.3.2 Mechatronic Design Quotient (MDQ)
• 1.3.3 Design Evolution
• 1.4 Mechatronic Instrumentation
• 1.5 Evolution of Mechatronics
• 1.6 Application Areas
• 1.7 Conclusion
• References
• Section I: Fundamentals
• Chapter 2: Modeling for Control of Rigid Bodies in 3-D Space
• 2.1 Introduction
• 2.2 Theory
• 2.2.1 Definitions and Assumptions
• 2.2.2 Equations of Motion for the Linear Model
• 2.2.3 Linear Momentum Force Systems
• 2.2.4 Generalization of the Equations of Moment of Momentum
• 2.2.5 Assembly of Equations
• 2.3 Modeling Sensors and Actuators into the Model
• 2.3.1 Modeling Actuators
• 2.3.2 Modeling Sensors and Feedback
• 2.4 Introduction to Software MBDS
• 2.4.1 A Simple Two-Mass Spring System with an Actuator and a Relative Velocity Sensor
• 2.4.2 Response of the System to a Simple Step Function
• 2.5 Conclusions
• References
• Chapter 3: Mechanics of Materials
• 3.1 Elastic Stress and Strain
• 3.1.1 Introduction
• 3.1.2 Load
• 3.1.3 Stress
• 3.1.4 Nonuniform Stress
• 3.1.5 Complementary Shear Stresses
• 3.1.6 Deformation
• 3.1.7 Strain
• 3.1.8 Elasticity and Yield
• 3.1.9 Hooke’s Law and Elastic Constants
• 3.2 Theory of Bending
• 3.2.1 Introduction
• 3.2.2 Definition
• 3.2.3 Sign Convention of Bending Moment and Shearing Force
• 3.2.4 Bending Moment and Shear Force Diagrams
• 3.2.5 Bending Stresses
• 3.3 Deflection of Transverse Loaded Slender Beams
• 3.3.1 Beam Deflection
• 3.3.2 Flexure Equation
• 3.3.3 Equilibrium and Determinacy
• 3.3.4 Bending Moments
• 3.3.5 Flexure Equation
• 3.3.6 Deflection of a Transverse Loaded Beam
• 3.3.7 Deflection of Statically Indeterminate Beams
• 3.3.8 Beams with Discontinuous Bending Moment Equations
• 3.3.9 Singularity Function Method (Often Called Macaulay’s Method)
• 3.4 Theory of Torsion
• 3.4.1 Introduction
• 3.4.2 Shear Strain/Stress Distribution
• 3.4.3 Torque T and Rate of Twist
• 3.4.4 Shear Stress from Torsion
• 3.5 Stress Transformation in Two Dimensions
• 3.5.1 Introduction
• 3.5.2 General State of Stress in Three Dimensions
• 3.5.3 General State of Stress in Two Dimensions
• 3.5.4 Analysis of Plane Stress in Two Dimensions
• 3.5.5 Calculation of Strains from Stresses
• 3.6 Strain Analysis and the Strain Gauge Rosettes
• 3.6.1 Introduction
• 3.6.2 Strain Gauge Rosettes
• 3.6.3 Conversion from Principal Strains to Principal Stresses
• 3.7 Mechanical Properties of Materials
• 3.7.1 Introduction
• 3.7.2 Tension and Compression Tests
• 3.7.3 Stress–Strain Behavior of Ductile Materials
• 3.7.4 Poisson’s Ratio
• 3.8 Conclusions
• References
• Chapter 4: Control of Mechatronic Systems
• 4.1 What Is a Mechatronic System?
• 4.2 Overview of Control Systems
• 4.2.1 System Model
• 4.2.2 System Modeling Applied to Components of Mechatronic Systems
• 4.2.3 Performance Assessment of a Control System
• 4.3 Control Techniques
• 4.3.1 Feedback Proportional–Integral–Derivative (PID) Control
• 4.3.2 Feedforward Control
• 4.3.3 Servo Control Structures
• 4.3.4 Programmable Logic Controllers
• 4.4 Implementation of a Computer Control
• 4.5 Challenges in Control of Mechatronic Systems
• 4.5.1 Friction
• 4.5.2 Force Ripples
• 4.5.3 Hysteresis and Backlash
• 4.5.4 Saturation
• 4.5.5 Dead Zone
• 4.5.6 Reference Signal Changes
• 4.5.7 Low-Frequency Drift
• 4.5.8 High-Frequency Noise
• 4.5.9 Incorporating and Addressing Nonlinear Dynamics
• 4.6 Application Examples
• 4.6.1 Flight Simulators
• 4.6.2 Piezoelectric Control System for Biomedical Application
• 4.7 Conclusions
• Bibliography
• Chapter 5: Introduction to Sensors and Signal Processing
• 5.1 Introduction
• 5.2 Signals
• 5.2.1 Types of Time Signals and Waveforms
• 5.2.2 Harmonic Signals
• 5.2.3 Quantification of Energy in a Signal: RMS
• 5.2.4 Useful Relationships and Common Waveforms
• 5.3 Fourier Analysis
• 5.3.1 Introduction
• 5.3.2 Fourier Transform
• 5.3.3 Fourier Transform Application Example
• 5.3.4 Basics of the Discrete and Fast Fourier Transforms
• 5.4 Signal Processing
• 5.4.1 Aliasing
• 5.4.2 Quantization Errors
• 5.4.3 Leakage and Windowing
• 5.4.4 Convolution
• 5.4.5 Random Signals
• 5.4.6 Butterworth Filter
• 5.4.7 Smoothing Filters
• 5.5 Sensors
• 5.5.1 Accelerometers
• 5.5.2 Velocity Transducers
• 5.5.3 Displacement Transducers
• 5.5.4 Strain Gauges
• 5.5.5 Load Cells
• 5.5.6 Temperature Sensors
• 5.5.7 Flow Sensors
• 5.5.8 Pressure Transducers
• 5.5.9 Ultrasonic Sensors
• 5.5.10 Other Sensors
• 5.6 Logarithmic Scales
• 5.6.1 Decibel
• 5.6.2 Octave
• 5.7 Conclusions
• References
• Chapter 6: Bio-MEMS Sensors and Actuators
• 6.1 Introduction
• 6.2 Bio-MEMS Actuators
• 6.2.1 Artificial Muscles
• 6.2.2 Ciliary Actuators
• 6.2.3 Nanotweezers for Micromanipulation of Biomolecules
• 6.2.4 Application of Capillary Valves in Microfluidic Devices
• 6.2.5 Drug Delivery
• 6.2.6 Biomolecular Systems
• 6.3 Bio-MEMS Sensors
• 6.3.1 Triglyceride Biosensor
• 6.3.2 Bio-MEMS Sensor for C-Reactive Protein Detection
• 6.3.3 Glucose Detection
• 6.3.4 MEMS Force Sensor for Protein Delivery
• 6.3.5 Tissue Softness Characterization
• 6.3.6 Blood Cell Counter
• 6.3.7 Acoustic Sensor
• 6.4 Conclusions
• References
• Chapter 7: System Identification in Human Adaptive Mechatronics
• 7.1 From Manual Control to Human Adaptive Mechatronics
• 7.2 Human in the Loop
• 7.3 Classical HO Model
• 7.3.1 Quasi-Linear Structure
• 7.3.2 Crossover Model
• 7.4 Identification of Quasi-Linear Model
• 7.4.1 Signal and Spectra
• 7.4.2 Nonparametric Quasi-Linear Model
• 7.4.3 Parametric Quasi-Linear Model
• 7.4.4 Experiment and Model Identification Results
• 7.5 Identification through Optimal Control Theory
• 7.5.1 Linear Regulator Problem
• 7.5.2 LQG Controller without Time Delay
• 7.5.3 LQG Controller with Time Delay
• 7.5.4 Optimal Control Model for the Human Operator
• 7.5.5 Human Optimal Control Model (OCM)
• 7.5.6 Motor Noise Effect
• 7.5.7 Modified Optimal Control Model (MOCM)
• 7.5.8 Identification of Optimal Control Model
• 7.5.9 Data-Based HO Model Identification
• 7.6 Conclusions
• References
• Chapter 8: Intelligent Robotic Systems
• 8.1 Introduction
• 8.2 Biological Immune System
• 8.2.1 Jerne’s Idiotypic Network Theory
• 8.3 Artificial Immune System (AIS)
• 8.3.1 Network Theory Model
• 8.4 Multi-Robot Cooperation Problem
• 8.4.1 Fault Tolerance
• 8.4.2 Decision Conflicts
• 8.4.3 Interdependencies and Priorities
• 8.5 Multi-Robot Cooperation and Artificial Immune System
• 8.5.1 Binding Affinity
• 8.5.2 Robot and Antibody
• 8.5.3 Multi-Robot Cooperation and Modified Idiotypic Network Model
• 8.6 Genetic Algorithm
• 8.6.1 Operators of GA
• 8.6.2 Simple GA
• 8.7 Optimizing Binding Affinity Function Using GA
• 8.8 Results and Discussion
• 8.9 Conclusions
• References
• Section II: Applications
• Chapter 9: Automated Mechatronic Design Tool
• 9.1 Introduction
• 9.1.1 Mechatronic Design Theory
• 9.2 Evolutionary Mechatronic Tool
• 9.2.1 Genetic Programming
• 9.2.2 Bond Graphs
• 9.2.3 Integration of Bond Graphs and Genetic Programming
• 9.3 Controller Design Using Bond Graphs
• 9.4 Two-Loop Design Model
• 9.4.1 Hybrid Genetic Algorithm with Genetic Programming
• 9.4.2 Case Study: Iron Butcher Controller Design [15]
• 9.5 Niching Optimization Scheme
• 9.5.1 Niching Genetic Programming
• 9.5.2 Case Study: Model-Referenced Active Car Suspension [19]
• 9.5.3 Case Study: Hydraulic Engine Mount Design
• 9.6 Conclusions
• References
• Chapter 10: Design Evolution of Mechatronic Systems
• 10.1 Introduction
• 10.2 Modeling Multidomain Systems
• 10.2.1 Bond Graph Modeling
• 10.2.2 Linear Graphs
• 10.3 Design Evolution
• 10.3.1 Evolutionary Design Framework with BGs
• 10.3.2 Methodology
• 10.3.3 Solution Representation for the Evolution
• 10.3.4 Fitness Function
• 10.4 Application of Methodology to Industrial Systems
• 10.4.1 Illustrative Scenario 1
• 10.4.2 Illustrative Example of Application of LG Methodology
• 10.4.3 Illustrative Scenario 2
• 10.5 Conclusions
• References
• Chapter 11: Mechatronic Design of Unmanned Aircraft Systems
• 11.1 Introduction
• 11.2 Unmanned System Hardware
• 11.2.1 Sensors and Measurement Systems
• 11.2.2 Computers
• 11.2.3 Actuator Management
• 11.2.4 Communication Unit
• 11.2.5 Hardware Integration
• 11.3 Unmanned System Software
• 11.3.1 Onboard Real-Time Software System
• 11.3.2 Ground Control Software System
• 11.4 Case I: Design of a Coaxial Rotorcraft System
• 11.4.1 Hardware System
• 11.4.2 Software System
• 11.4.3 Experimental Results
• 11.5 Case II: Design of a UAV Cargo Transportation System
• 11.5.1 Hardware System
• 11.5.2 Software System
• 11.5.3 Experimental Results
• 11.6 Conclusion
• References
• Chapter 12: Self-Powered and Bio-Inspired Dynamic Systems
• 12.1 Introduction
• 12.2 Energy Harvesting
• 12.2.1 Energy Conversion Mechanisms
• 12.3 Self-Powered Dynamic Systems
• 12.3.1 Concept of Self-Powered Dynamic Systems
• 12.3.2 Theory of Self-Powered Systems
• 12.3.3 Renewable Energy for Dynamic Systems
• 12.3.4 Human-Powered Systems
• 12.4 Bio-Inspired Dynamic Systems
• 12.4.1 Piezoelecteric Energy Harvesting from Aeroelastic Vibrations
• 12.4.2 Fish Schooling Inspired Vertical Axis Wind Turbine Farm
• 12.4.3 Bio-Inspired Self-Propelled Vehicle
• 12.4.4 Bio-Inspired Flapping Wing Flying Robots
• 12.4.5 Bio-Inspired Flight Control System
• 12.4.6 Uncertainty Quantification
• 12.5 Conclusions
• References
• Chapter 13: Visual Servo Systems for Mobile Robots
• 13.1 Introduction
• 13.2 Mobile Robotic Visual Servo Systems
• 13.2.1 State of the Art of Mobile Robotic Systems
• 13.2.2 Typical Sensors
• 13.3 Visual Servoing
• 13.3.1 Basic Categories of Visual Servoing
• 13.3.2 Modeling of Visual Servo System
• 13.4 Case Study of Visual Servoing
• 13.4.1 System Modeling
• 13.4.2 Traditional Image-Based Visual Servoing
• 13.4.3 Adaptive Nonlinear Model Predictive Control
• 13.5 Conclusions
• References
• Chapter 14: Robotic Learning and Applications
• 14.1 Introduction
• 14.2 Markov Decision Process (MDP) and Q Learning
• 14.3 Case Study: Multi-Robot Transportation Using Machine Learning
• 14.3.1 Multi-Agent Infrastructure
• 14.3.2 Cooperation Based on Machine Learning
• 14.3.3 Simulation Results
• 14.3.4 Experimentation
• 14.4 Case Study: A Hybrid Visual Servo Controller Using Q Learning
• 14.4.1 Vision-Based Mobile Robot Motion Control
• 14.4.2 Hybrid Controller for Robust Visual Servoing
• 14.4.3 Experimental Results
• 14.5 Conclusions
• References
• Chapter 15: Neuromechatronics with In Vitro Microelectrode Arrays
• 15.1 Introduction
• 15.1.1 Evolution of Mechatronics
• 15.1.2 Neuromechatronics
• 15.1.3 Neuronal Networks
• 15.2 In Vitro Microelectrode Arrays (MEAs)
• 15.2.1 MEAs among Other Neural Recording Techniques
• 15.2.2 Functionality of MEAs
• 15.2.3 Strengths and Weaknesses of MEAs
• 15.2.4 MEA Systems and Software
• 15.3 Dynamics of Microelectrode Array Recordings
• 15.3.1 Spikes
• 15.3.2 Bursts
• 15.3.3 Network Bursts
• 15.4 Detection of Network Dynamics
• 15.4.1 Spike Detection
• 15.4.2 Spike Sorting
• 15.4.3 Burst Detection
• 15.4.4 Network Burst Detection
• 15.4.5 General Analysis Methods
• 15.4.6 Identifying Functional Motifs
• 15.5 Embodied Neural Networks
• 15.5.1 Supervised Learning
• 15.5.2 Unsupervised Learning
• 15.6 Conclusion
• References
• Back Cover