Foreword: Automation Is for Humans and for Our Environment
Foreword: Automation Is the Science of Integration
Foreword: Automation Technology: The Near Limitless Potential
Foreword: The Dawn of the Smart Manufacturing Era Enables High-Quality Automation
Foreword: Automation of Surgical Robots
Preface
Special Thanks
Contents
About the Editor
Advisory Board
Advisory Board Members of the Previous Edition
Contributors
Part I Development and Impacts of Automation
1 Automation: What It Means to Us Around the World, Definitions, Its Impact, and Outlook
1.1 The Meaning of Automation
1.1.1 Definitions, Formalism, and Automation Examples
Robotics and Automation
Early Automation
Industrial Revolution
Modern Automation
1.1.2 Domains of Automation
1.2 Brief History of Automation
1.2.1 First Generation: Before Automatic Control (BAC)
1.2.2 Second Generation: Automatic Control Before Computer Control (ABC)
1.2.3 Third Generation: Automatic Computer/Cyber Control (ACC)
1.2.4 Perspectives of Automation Generations
1.3 Automation Cases
1.4 Flexibility, Degrees, and Levels of Automation
1.4.1 Degree of Automation
1.4.2 Levels of Automation, Intelligence, and Human Variability
1.5 Worldwide Surveys: What Does Automation Mean to People?
1.5.1 How Do We Define Automation?
1.5.2 When and Where Did We Encounter Automation First in Our Life?
1.5.3 What Do We Think Is the Major Impact/Contribution of Automation to Humankind?
1.5.4 What Do We Think Is the Major Risk of Automation to Humankind?
1.5.5 What Do We Think Is the Best Example of Automation?
1.6 Emerging Trends
1.6.1 Automation Trends of the Twentieth and Twenty-First Centuries
1.6.2 Bioautomation
1.6.3 Collaborative Control Theory and Collaborative Automation
1.6.4 Risks of Automation
1.6.5 Need for Dependability, Survivability, Security, Continuity of Operation, and Creativity
1.6.6 Quantum Computing and Quantum Automation
1.7 Conclusion
References
Further Reading
2 Historical Perspective of Automation
2.1 A History of Automatic Control
2.1.1 Antiquity and the Early Modern Period
2.1.2 Stability Analysis in the Nineteenth Century
2.1.3 Ship, Aircraft, and Industrial Control Before World War II
2.1.4 Electronics, Feedback, and Mathematical Analysis
2.1.5 World War II and Classical Control: Infrastructure
2.1.6 World War II and Classical Control: Theory
2.1.7 The Emergence of Modern Control Theory
2.1.8 The Digital Computer
2.1.9 The Socio-technological Context Since 1945
2.1.10 Conclusion and Emerging Trends
2.1.11 Further Reading
2.2 Advances in Industrial Automation: Historical Perspectives
2.3 Advances in Robotics and Automation: Historical Perspectives
References
3 Control for mobile phones
3.1 Introduction
3.2 Impact of Automation and Control: Value Chain Considerations
3.3 A Historical Case Study of Automation and Its Impact: Commercial Aviation
3.4 Social, Organizational, and Individual Concerns
3.4.1 Unintended Consequences of Automation Technology
3.4.2 Sustainability and the Limits of Growth
3.4.3 Inequities of Impact
3.4.4 Ethical Challenges with Automation
3.5 Emerging Developments in Automation: Societal Implications
3.5.1 Smart Cities
3.5.2 Assistive, Wearable, and Embedded Devices
3.5.3 Autonomous Weapon Systems
3.5.4 Automotive Autonomy
3.5.5 Renewable Energy and Smart Grids
3.6 Some Approaches for Responsible Innovation
3.6.1 Ethics Guidelines and Ethically Aligned Designs
3.6.2 Conceptual Approaches
3.6.3 Ethical Training
3.6.4 Standards, Policies, and Enforcement
3.7 Conclusions and Emerging Trends
References
4 Economic Effects of Automation
4.1 Introduction
4.2 Basic Concepts to Assess the Effectsof Automation
4.3 Production and Distribution in Economic Theory
4.3.1 Preliminary Elements of Production
4.3.2 Measurement and Characteristics of Production Factors
4.3.3 The Neoclassical Function of Production and the Distribution to Production Factors
4.4 Microeconomic Effects of Automation in Enterprises
4.4.1 Effects on the Production Function
4.4.2 Effects on Worker Incentives and Controls
4.4.3 Effects on Cost Structure and Labor Demand in the Short Term
4.5 Macroeconomic Effects of Automation in the Short Period
4.5.1 Demand for Labor
4.5.2 Labor Offer
4.5.3 Equilibrium and Disequilibrium in the Labor Market
4.6 Macroeconomic Effects of Automation in the Long Period
4.6.1 A Brief Historical Excursus
4.6.2 First Era of Machines: An Intersectoral Model with Consumption and Capital
The Model in Terms of Quantity: Potential Growth without Technical Progress
The Model in Terms of Prices: Wages and Profits
Use of the Model to Explain the Events of the Second Half of the Twentieth Century
4.6.3 Second Era of Machines: Automation and Artificial Intelligence (AI)
Proliferation of Services, Diversification of Consumption, and Digital Revolution
Endogenous Technical Progress
Long-Term Trends: Complete Automation and Diffusion of Digital Goods
The Transient: Inequality and Unemployment
Artificial Intelligence
4.7 Final Comments
4.7.1 Psychological Benefits of Labor
4.7.2 Use of Free Time
References
5 Trends in Automation
5.1 Introduction
5.2 Environment
5.2.1 Market Requirements
Global Versus Local
Enterprise Cash Flow Optimization
5.2.2 Applications
Industrial Value Chain
Plants: Continuous Versus Discrete
Service Industries
Plant Lifecycle
5.3 Current Trends
5.3.1 Digitalization
Industrial Internet of Things (IIoT)
Industrial Internet of Services
Cyber-Physical Systems Architectures
5.3.2 Communication
5.3.3 Collaborative Robots
5.3.4 Industrial AI
Consumer Versus Industrial AI
Merging AI with Conventional Algorithms
5.3.5 Virtual Models and Digital Twin
5.4 Outlook
5.4.1 Autonomous Industrial Systems
5.4.2 Collaborative Systems
5.4.3 New Applications
5.5 Conclusions
References
Part II Automation Theory and Scientific Foundations
6 Linear Control Theory for Automation
6.1 Systems and Control
6.2 Open Loop Control, Closed Loop Control
6.3 Quality Specifications
6.4 Types of Signals and Models of Systems
6.5 Description of SISO Continuous-Time Linear Systems
6.5.1 Description in the Time Domain
6.5.2 Description in the Frequency Domain
6.5.3 Description in the Laplace Operator Domain
Laplace Transformation
The Transfer Function
6.6 Description of SISO Discrete-Time Linear Systems
6.6.1 Description in the Time Domain
6.6.2 Description in the z – Operator Domain
z-transforms of sampled signals
The Pulse Transfer Function
6.6.3 Description in the Frequency Domain
6.7 Resulting Transfer Functions of Closed Loop Control Systems
6.8 Stability
6.9 Static and Dynamic Response
6.10 Controller Design
6.10.1 Continuous PID Controller Design
6.10.2 Discrete Time PID Controller Design
6.11 Responses of MIMO Systems and “Abilities”
6.11.1 Transfer Function Models
6.11.2 State-Space Models
6.11.3 Matrix Fraction Description
6.12 Feedback System – Stability Issue
6.13 Performances for MIMO LTI Systems
6.13.1 Control Performances
Signal Norm
System Norms
6.13.2 H2 Optimal Control
State-Feedback Problem
State-Estimation Problem
Output-Feedback Problem
6.13.3 H∞ Optimal Control
State-Feedback Problem
State-Estimation Problem
Output-Feedback Problem
6.14 Robust Stability and Performance
6.15 LMI in Control Engineering
6.16 Model-Based Predictive Control
6.17 Summary
References
7 Nonlinear Control Theory for Automation
7.1 Introduction
7.2 Autonomous Dynamical Systems
7.3 Stability and Related Concepts
7.3.1 Stability of Equilibria
7.3.2 Lyapunov Functions
7.4 Asymptotic Behavior
7.4.1 Limit Sets
7.4.2 Steady-State Behavior
7.5 Dynamical Systems with Inputs
7.5.1 Input-to-State Stability (ISS)
7.5.2 Cascade Connections
7.5.3 Feedback Connections
7.5.4 The Steady-State Response
7.6 Stabilization of Nonlinear Systems via State Feedback
7.6.1 Relative Degree, Normal Forms
7.6.2 Feedback Linearization
7.6.3 Global Stabilization via Partial Feedback Linearization
7.6.4 Global Stabilization via Backstepping
7.6.5 Semiglobal Practical Stabilization via High-Gain Partial-State Feedback
7.7 Observers and Stabilization via Output Feedback
7.7.1 Canonical Forms of Observable Nonlinear Systems
7.7.2 High-Gain Observers
7.7.3 The Nonlinear Separation Principle
7.7.4 Robust Feedback Linearization
7.8 Recent Progresses
References
8 Control of Uncertain Systems
8.1 Introduction
8.1.1 Motivation
8.1.2 Historical Background
8.2 General Scheme and Components
8.2.1 Stochastic Optimal Control Problem
8.2.2 Key Approaches
8.3 Challenges and Solutions
8.3.1 Model Predictive Control
8.3.2 Learning System Model Using Gaussian Process
8.3.3 Constrained Markov Decision Processes
8.3.4 Model-Free Reinforcement Learning for Decision-Making
Unconstrained Reinforcement Learning
Constrained Reinforcement Learning with Average Reward
8.3.5 Constrained Reinforcement Learning with Discounted Rewards
8.3.6 Case Study for a Constrained RL Setup
8.4 Application Areas
8.5 Conclusions, Challenges, and Trends
References
9 Artificial Intelligence and Automation
9.1 Artificial Intelligence (AI): The Study of Intelligent Agents
9.2 AI Techniques
9.2.1 Optimization
Continuous Function Minimization
Discrete Function Minimization
Example Applications
9.2.2 Knowledge Representation and Reasoning
Propositional Logic
First-Order Logic
Other Knowledge Representation Languages
Example Applications
9.2.3 Planning
Deterministic Planning in Single-Agent Systems
Probabilistic Planning in Single-Agent Systems
Planning in Multi-agent Systems
Example Applications
9.2.4 Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Example Applications
9.3 Combining AI Techniques
9.4 Case Study: Automated Warehousing
9.5 AI History
9.6 AI Achievements
9.7 AI Ethics
Further Reading
References
10 Cybernetics, Machine Learning, and Stochastic Learning Automata
10.1 Introduction
10.1.1 A General Overview
Machine Learning
Overview of ML and Connection to Cybernetics
Applications of Cybernetics in Machine Learning
Connection to Reinforcement Learning
10.1.2 Automation, Automaton, and Learning Automata
Learning Automata and Cybernetics
10.2 A Learning Automaton
10.3 Environment
10.4 Classification of Learning Automata
10.4.1 Deterministic Learning Automata
10.4.2 Stochastic Learning Automata
Fixed Structure Learning Automata
Variable Structure Learning Automata
Discretized Learning Automata
10.5 Estimator Algorithms
10.5.1 Rationale and Motivation
10.5.2 Continuous Estimator Algorithms
Pursuit Algorithm
TSE Algorithm
Generalized Pursuit Algorithm
10.5.3 Discrete Estimator Algorithms
Discrete Pursuit Algorithm
Discrete TSE Algorithm
Discretized Generalized Pursuit Algorithm
10.5.4 The Use of Bayesian Estimates in PAs
10.5.5 Stochastic Estimator Learning Algorithm (SELA)
10.6 Challenges in Analysis
10.6.1 Previous Flawed Proofs
10.6.2 The Rectified Proofs of the PAs
10.6.3 Proofs for Finite-Time Analyses
10.7 Hierarchical Schemes
10.8 Point Location Problems
10.9 Emerging Trends and Open Challenges
10.10 Conclusions
References
11 Network Science and Automation
11.1 Overview
11.2 Network Structure and Definitions
11.2.1 Notation
11.2.2 Networks as Graphs
11.2.3 Network Matrices and Their Properties
11.2.4 Real-World Networks
11.3 Main Results on Dynamics on Networks
11.3.1 Consensus Problem
11.3.2 Synchronization
11.3.3 Perturbative Analysis
11.3.4 Control
11.4 Applications
11.4.1 Distributed Sensing
11.4.2 Infrastructure Models and Cyberphysical Systems
11.4.3 Motion Coordination
11.5 Ongoing Research and Future Challenges
11.5.1 Networks with Adversarial and Malicious Nodes
11.5.2 Dynamics on Time-Varying and Adaptive Networks
11.5.3 Controllability of Brain Networks
Further Reading
References
12 What Can Be Automated? What Cannot Be Automated?
12.1 The Limits of Automation
12.2 The Limits of Mechanization
12.3 Expanding the Limit
12.4 The Current State of the Art
12.5 A General Principle
12.6 Editor's Notes for the New Edition
12.6.1 What Can We Automate, But We Prefer Not to Automate
12.6.2 What Should Not Be Automated?
References
Further Reading
Part III Automation Design: Theory, Elements, and Methods
13 Designs and Specification of Mechatronic Systems
13.1 From Mechanical to Mechatronic Systems
13.2 Mechanical Systems and Mechatronic Developments
13.2.1 Machine Elements and Mechanical Components
13.2.2 Electrical Drives and Servosystems
13.2.3 Power-Generating Machines
13.2.4 Power-Consuming Machines
13.2.5 Vehicles
13.2.6 Trains
13.3 Functions of Mechatronic Systems
13.3.1 Basic Mechanical Design
13.3.2 Distribution of Mechanical and Electronic Functions
13.3.3 Operating Properties
13.3.4 New Functions
13.3.5 Other Developments
13.4 Integration Forms of Processes with Electronics
13.5 Design Procedures for Mechatronic Systems
13.6 Computer-Aided Design of Mechatronic Systems
13.7 Model-Based Control Function Development
13.7.1 Model-in-the-Loop Simulation and Control Prototyping
13.7.2 Software-in-the-Loop and Hardware-in-the-Loop Simulation
13.8 Control Software Development
13.9 Internet of Things for Mechatronics
13.10 Mechatronic Developments for Vehicles
13.11 Mechatronic Brake Systems
13.11.1 Hydraulic Brake System
13.11.2 Antilock Control with Switching Valves (ABS)
13.11.3 Electromechanical Brake Booster
13.11.4 Electrohydraulic Brake System (EHB)
13.11.5 Electromechanical Brake (EMB)
13.12 Mechatronic Steering Systems
13.12.1 Electrical Power Steering (EPS)
13.12.2 Basic Designs of EPS Systems
13.12.3 Fault-Tolerant EPS Structures
13.13 Conclusion and Emerging Trends
References
14 Sensors, Machine Vision, and Sensor Networks
14.1 Sensors
14.1.1 Sensing Principles
14.1.2 Position Sensors
14.1.3 Velocity Sensors
14.1.4 Acceleration Sensors
14.1.5 Flow Sensors
14.1.6 Ultrasonic Sensors
14.1.7 Micro- and Nanosensors
14.1.8 Miscellaneous Sensors
14.2 Machine Vision
14.2.1 Image-based Automation Technology
14.2.2 Machine Vision System Components
14.2.3 Artificial Intelligence and Machine Vision
14.3 Sensor Networks
14.3.1 Sensor Network Systems
14.3.2 Multisensor Data Fusion Methods
Bayes' Rule
Probabilistic Grids
The Kalman Filter
Sequential Monte Carlo Methods
Interval Calculus
Fuzzy Logic
Evidential Reasoning
14.3.3 Sensor Network Design Considerations
14.3.4 Sensor Network Architectures
14.3.5 Sensor Network Protocols
14.3.6 Sensor Network Security
14.3.7 Sensor Network Applications
14.3.8 Industrial Internet of Things (IIoT)
14.4 Emerging Trends
14.4.1 Heterogeneous Sensors and Applications
14.4.2 Appropriate Quality-of-Service (QoS) Model
14.4.3 Integration with Other Networks
References
15 Intelligent and Collaborative Robots
15.1 The Industrial Robot Market
15.2 Emergence of Intelligent and Collaborative Robots
15.3 Intelligent and Collaborative Robots
15.3.1 Basic Technology for Industrial Robots
15.3.2 Intelligent Robot
Vision Sensors
Force Sensors
15.3.3 Collaborative Robot
15.4 Offline Programming and IoT
15.4.1 Offline Programming
15.4.2 IoT
Robot Maintenance Support Tool
IoT Platform
15.5 Applications of Intelligent and Collaborative Robots
15.5.1 Welding
15.5.2 Machining
15.5.3 Assembly
15.5.4 Picking, Packing, and Palletizing
15.5.5 Painting
15.6 Installation Guidelines
15.6.1 Range of Automation
15.6.2 Return on Investment
15.7 Conclusion
References
Further Reading
16 Control Architecture for Automation
16.1 Historical Background and the Motivation for a Change
16.1.1 Motion Control and Path Planning
16.1.2 Logic Controllers
16.2 General Scheme for Control Architectures for Automation
16.2.1 Typical Control Architectures and Systems
16.2.2 Industrial Communication and Fieldbuses
16.2.3 Proprietary and Partially Open Interfaces
16.2.4 Resources in the Network: Cloud and Edge for Storage and Compute
16.2.5 Engineering and Virtual Tryout
16.3 Challenges
16.4 New Architectural Components and Solutions
16.4.1 Virtualization Techniques
16.4.2 Components for Communication
Time-Sensitive Networking
Wireless Technologies
Deterministic WLAN: Wi-Fi 6
5G: Fifth-Generation Cellular Networks
OPC Unified Architecture (OPC UA)
TSN-Based Converged Industrial Communication
16.4.3 Hardware-in-the-Loop Simulation
Motivation and Use of HiL Simulation
Setup of a HiL Simulation
Real-Time HiL Simulation
Usage of HiL Simulation
Automating the Test Procedure for HiL
16.4.4 Data Analytics and AI
Closed Loop Control Problems
Machine Vision
Data-Driven Models
Planning Problems
Data Analytics
16.5 Conclusion and Trends
References
17 Cyber-Physical Automation
17.1 Status of the Cyber-Physical Automation
17.1.1 Vertical Approach on Cyber-Physical Automation
17.1.2 Horizontal Approach on Cyber-Physical Automation
17.2 Challenges in Implementing Cyber-Physical Automation
17.2.1 The Problems of the Vertical Cyber-Physical Approach
17.2.2 The Need for Edge Computing and Workload Consolidation
17.2.3 The Need for Services Unification
17.3 Cyber-Physical Unified Services Framework
17.3.1 Cyber-Physical Architecture
17.3.2 Cyber-Physical Components
17.3.3 Communications
17.3.4 Cyber-Physical Infrastructure
Workload Consolidation
Infrastructure Anatomy
Edge Compute Device Tier Anatomy
Edge Server Tier Anatomy
17.3.5 Automation Software
17.3.6 Cyber-Physical Manageability
Stages and Functions Required by a Device Management Component
Functions of Device Manageability
Device Management: Features and Functionalities
Automatic Device Onboarding
Device Manageability Dashboard
Remote Login
Device Grouping/Hierarchy Management
Remote Script Execution
OTA
Campaigns
Device Configuration Provisioning
Rule Engine
Deployment Model Support
Manageability Standards Support
Cyber-Physical Components Management
Certificate Management
Protocols Supported
Scalability
17.3.7 Security
Secure Boot
Data Encryption
Execution Policies and Integrity Protection
Components
Credential Storage
Data Sanitization
17.3.8 Data Management and Analytics
17.3.9 Reliability and Safety
17.3.10 IT/OT Integration
17.4 Conclusion, Emerging Trends, and Challenges
References
18 Collaborative Control and E-work Automation
18.1 Background and Definitions
18.2 Theoretical Foundations for e-Work and CCT
18.2.1 e-Work
18.2.2 Integration and Communication
18.2.3 Distributed Decision Support
18.2.4 Active Middleware
18.3 Architectural Enablers for Collaborative e-Work
18.3.1 Internet-of-Things (Physical Architecture)
18.3.2 Internet-of-Services (Functional Architecture)
18.3.3 IoT-IoS Integration (Allocated Architecture)
18.4 Design Principles for Collaborative e-Work, Collaborative Automation, and CCT
18.4.1 Generic Framework
18.4.2 Design Principles
18.4.3 Emerging Thrusts
18.5 Conclusions and Challenges
18.6 Further Reading
References
19 Design for Human-Automation and Human-Autonomous Systems
19.1 Introduction
19.2 The Variety of Automation and Increasingly Autonomous Systems
19.3 Challenges with Automation and Autonomy
19.3.1 Changes in Feedback
19.3.2 Changes in Tasks and Task Structure
19.3.3 Operators' Cognitive and Emotional Response
19.4 Automation and System Characteristics
19.4.1 Authority and Autonomy at Information Processing Stages
19.4.2 Complexity, Observability, and Directability
19.4.3 Timescale and Multitasking Demands
19.4.4 Agent Interdependencies
19.4.5 Environment Interactions
19.5 Automation Design Methods and Application Examples
19.5.1 Human-Centered Design
19.5.2 Fitts' List and Function Allocation
19.5.3 Operator-Automation Simulation
19.5.4 Enhanced Feedback and Representation Aiding
19.5.5 Expectation Matching and Simplification
19.6 Future Challenges in Automation Design
19.6.1 Swarm Automation
19.6.2 Operator–Automation Networks
References
20 Teleoperation and Level of Automation
20.1 Introduction
20.2 Historical Background and Motivation
20.3 Levels of Automation and General Schemes
20.3.1 Levels of Automation
20.3.2 Bilateral Teleoperation
Local Station
Remote Station
Communications
Operation Principle
20.3.3 Cooperative Teleoperation Systems
20.4 Challenges and Solutions
20.4.1 Control Objectives and Algorithms
Bilateral Teleoperation Control
Cooperative Teleoperation Control
20.4.2 Communication Channels
20.4.3 Situation Awareness and Immersion
20.4.4 Teleoperation Aids
Relational Positioning
Virtual Contacts
Guiding
20.4.5 Teleoperation of Unmanned Aerial Vehicles/Drones
20.5 Application Fields
20.5.1 Industry and Construction
20.5.2 Agriculture
20.5.3 Mining
20.5.4 Underwater
20.5.5 Space
20.5.6 Healthcare and Surgery
20.5.7 Assistance
20.5.8 Humanitarian Demining
20.5.9 Education
20.6 Conclusions and Trends
References
21 Nature-Inspired and Evolutionary Techniques for Automation
21.1 Nature-Inspired and Evolutionary Techniques
21.1.1 Genetic Algorithm
21.1.2 Swarm Intelligences
Ant Colony Optimization
Particle Swarm Optimization
21.1.3 Other Nature-Inspired Optimization Algorithms
Differential Evolution
Estimation of Distribution Algorithm
Simulated Annealing
21.1.4 Evolutionary Multi-objective Optimization
21.1.5 Features of Evolutionary Search
Exploitation and Exploration
Hybrid Evolutionary Search
Enhanced EA via Learning
21.1.6 Evolutionary Design Automation
21.2 Evolutionary Techniques for Automation
21.2.1 Advanced Planning and Scheduling
21.2.2 Assembly Line System
21.2.3 Logistics and Transportation
21.3 AGV Dispatching in Manufacturing System
21.3.1 Network Modeling for AGV Dispatching
21.3.2 A Priority-Based GA
21.3.3 Case Study of AGV Dispatching
21.4 Robot-Based Assembly Line System
21.4.1 Assembly Line Balancing Problems
21.4.2 Robot-Based Assembly Line Model
21.4.3 Evolutionary Algorithm Approaches
21.4.4 Case Study of Robot-Based Assembly Line Model
21.5 Conclusions
References
22 Automating Prognostics and Prevention of Errors, Conflicts, and Disruptions
22.1 Definitions
22.2 Error Prognostics and Prevention Applications
22.2.1 Error Detection in Assembly and Inspection
22.2.2 Process Monitoring and Error Management
22.2.3 Hardware Testing Algorithms
22.2.4 Error Detection in Software Design
22.2.5 Error Detection and Diagnostics in Discrete-Event Systems
22.2.6 Error Detection and Disruption Prevention in Service Industries and Healthcare
22.2.7 Error Detection and Prevention Algorithms for Production and Service Automation
22.2.8 Error-Prevention Culture (EPC)
22.3 Conflict Prognostics and Prevention Applications
22.4 Integrated Error and Conflict Prognostics and Prevention
22.4.1 Active Middleware
22.4.2 Conflict and Error Detection Model
22.4.3 Performance Measures
22.5 Error Recovery, Conflict Resolutions, and Disruption Prevention
22.5.1 Error Recovery
22.5.2 Conflict Resolution
22.5.3 Disruption Prevention
22.6 Emerging Trends
22.6.1 Decentralized and Agent-Based Error and Conflict Prognostics and Prevention
22.6.2 Intelligent Error and Conflict Prognostics and Prevention
22.6.3 Graph and Network Theories
22.6.4 Financial Models for Prognostics Economy
22.7 Conclusions and Emerging Trends
References
Part IV Automation Design: Theory and Methods for Integration
23 Communication Protocols for Automation
23.1 Introduction
23.1.1 History
23.1.2 Requirements and Classification
23.1.3 Chapter Overview
23.2 Wired Industrial Communications
23.2.1 Classification According to Automation Hierarchy
23.2.2 Sensor/Actuator Networks
HART
ASi (IEC 62026-2)
IO-Link
23.2.3 Fieldbus Systems
PROFIBUS
DeviceNet
23.2.4 Industrial Ethernet-Based Networks
Local Soft Real-Time Approaches (Real-Time Class 1)
Deterministic Real-Time Approaches (Real-Time Class 2)
Isochronous Real-Time Approaches (Real-Time Class 3)
23.2.5 Time-Sensitive Networking (TSN)
23.2.6 Advanced Physical Layer (APL)
23.2.7 Internet of Things (IoT) Communication
OPC UA
Message Queue Telemetry Transport (MQTT)
23.3 Wireless Industrial Communications
23.3.1 Classification
23.3.2 Wireless Local Area Networks (WLAN)
23.3.3 Wireless Sensor/Actuator Networks
ZigBee
WirelessHART
ISA SP100.11a
WIA-PA
WIA-FA
Bluetooth Low Energy
23.3.4 Low-Power Wide Area Network (LPWAN)
SIGFOX
LoRaWAN
Weightless
NB-IoT
23.3.5 5G
23.4 Virtual Automation Networks
23.4.1 Motivation
23.4.2 Domains
23.4.3 Architectures for VAN Solutions
23.5 Wide Area Communications
23.5.1 Contextualization
23.5.2 Best Effort Communication in Automation
23.5.3 Real-Time Communication in Automation
23.6 General Overview About Industrial Protocol Features
23.7 Conclusions
23.8 Emerging Trends
References
Further Reading
Books
Various Communication Standards
24 Product Automation and Innovation
24.1 Historical Background of Automation
24.2 Definition of Product Automation
24.3 Fundamental Core Functions
24.4 Innovation of Product Automation in the IoT Age
24.4.1 Technology as a Driver to Change Life and Industry
24.4.2 Expansion of Automation Applications and Innovations
Automation Applications and Innovations in Industry
Home Automation Applications and Innovations
Automation Applications and Innovations for Autonomous Vehicle and Transport
Automation Applications and Innovations in Logistics and Delivery
Other Areas of Automation Applications and Innovations
24.4.3 Benefit and Value of Automation
24.4.4 Business Trends and Orientation
24.4.5 Products in Experience-Value Economy
24.4.6 New Requirements for Product Automation
24.5 Modern Functional Architecture of Automation
24.6 Key Technologies
24.6.1 Localization and Mapping
Sensor's Role for SLAM
SLAM Methods and Algorithms
Types of SLAM Implementation
24.6.2 Edge Intelligence
Needs of Edge Intelligence in Logistics
Edge Intelligence Implementation: A Case
24.6.3 OTA (Over-the-Air) Technology
Needs and Benefit of OTA
Technical Challenges
OTA Implementation: A Case
24.6.4 Anomaly Detection
Needs and Benefit of Anomaly Detection
Anomaly Detection Methods
Anomaly Detection Implementation: A Case
24.7 Product and Service Lifecycle Management in the IoT Age
24.7.1 Management Model in the Experience-Value Economy
24.7.2 PSS (Product-Service System) Discussions
24.7.3 How to Realize Valuable Customer Experience
24.7.4 Business Model Making
24.8 Conclusion and Next Topics
References
25 Process control
25.1 Overview
25.2 Enterprise View of Process Automation
25.2.1 Measurement and Actuation (Level 1)
25.2.2 Safety and Environmental/Equipment Protection (Level 2)
25.2.3 Regulatory Control (Level 3a)
25.2.4 Multivariable and Constraint Control (Level 3b)
25.2.5 Real-Time Optimization (Level 4)
25.2.6 Planning and Scheduling (Level 5)
25.3 Process Dynamics and Mathematical Models
25.4 Regulatory Control
25.4.1 Sensors
25.4.2 Control Valves
25.4.3 Controllers
25.4.4 PID Enhancements
25.5 Control System Design
25.5.1 Multivariable Control
25.6 Batch Process Automation
25.7 Automation and Process Safety
25.8 Emerging Trends
References
Further Reading
26 Service Automation
26.1 Service
26.1.1 Definition of Service
26.1.2 Service Properties
26.1.3 Service Industries
26.1.4 Life Cycle of Product-Service Systems
Service Innovation and Service Design
Outcomes for Operators
26.1.5 Service Business Models
26.2 Operational Considerations
26.2.1 Operation Driven by Market Situation
26.2.2 Long-Term Continuous Operation
Main Equipment Planned Shutdown
Failures Reducing Flexibility
26.2.3 Batch or Shift Operation
26.3 Service, Maintenance, and Repair Strategies
26.3.1 Key Performance Indicators
26.3.2 Corrective Maintenance
26.3.3 Preventive Maintenance
26.3.4 Condition-Based Maintenance
26.3.5 Predictive Maintenance
26.3.6 Prescriptive Maintenance
26.4 Technology and Solutions
26.4.1 Condition Assessment and Prediction
26.4.2 Remote Services: Internet of Things
26.4.3 Service Information Management in a Digital Twin
26.4.4 Service Support Tools
26.4.5 Toward Fully Automated Service
26.5 Conclusions and Emerging Challenges
References
27 Infrastructure and Complex Systems Automation
27.1 Background and Scope
27.2 Control Methods Large-Scale Complex Systems
27.2.1 Multilevel Methods
Levels of Description
Levels of Control
Levels of Organization
Towards More Collaborative Schemes
27.2.2 Decentralized Control
27.2.3 Computer Supported Decision-Making
The Role of Human in the Control System
Decision Support Systems
Digital Cognitive Systems
27.3 Modern Automation Architectures and Essential Enabling Technologies
27.3.1 Internet of Things
27.3.2 Computing Technologies
Cloud Computing
Edge Computing
Mobile Computing
27.3.3 Tools and Methods
Cyber-Physical Systems
Big Data Analytics
27.3.4 Networked Control
Networked Control Systems
Cloud Control Systems
Fog Computing and Control
27.4 Examples
27.4.1 Smart Cities as Large-Scale Systems
Smart Buildings
Water Treatment and Distribution Infrastructures
27.4.2 Environmental Protection
27.4.3 Other Infrastructure Automation Cases
27.5 Design and Security Issues
References
28 Computer-Aided Design, Computer-Aided Engineering, and Visualization
28.1 Introduction
28.2 Product Lifecycle Management in the Digital Enterprise
28.3 3D Modeling
28.4 Parametric Solid Modeling
28.5 Parametric Geometry Creation Process
28.6 Electronic Design Automation (EDA)
28.7 Geometry Automation Mechanisms in the Modern CAD Environment
28.8 User Characteristics Related to CAD Systems
28.9 Visualization
28.10 Emerging Visualization Technologies: Virtual/Augmented/Mixed Reality
28.10.1 Augmented Reality
28.10.2 Virtual Reality
28.11 Conclusions and Emerging Trends
References
29 SafetyWarnings for Automation
29.1 Introduction
29.2 Warning Roles
29.3 Types of Warnings
29.3.1 Static Versus Dynamic Warnings
29.3.2 Warning Sensory Modality
Visual Warnings
Auditory Warnings
Verbal Versus Nonverbal Warnings
Haptic/Tactile Warnings
Multimodal Warnings
29.4 Automated Warning Systems
29.5 Models of Warning Effectiveness
29.5.1 Warning Effectiveness Measures
29.5.2 The Warning Compliance Hypothesis
29.5.3 Information Quality
29.5.4 Information Integration
29.5.5 The Value of Warning Information
29.5.6 Team Decision-Making
29.5.7 Time Pressure and Stress
29.6 Design Guidelines and Requirements
29.6.1 Legal Requirements
29.6.2 Voluntary Safety Standards
29.6.3 Design Specifications
29.7 Challenges and Emerging Trends
References
Part V Automation Management
30 Economic Rationalization of Automation Projects and Quality of Service
30.1 Introduction
30.2 General Economic Rationalization Procedure
30.2.1 General Procedure for Automation Systems Project Rationalization
30.2.2 Pre-cost-Analysis Phase
Alternative Automated Manufacturing Methods
Evaluation of Technical Feasibility for Alternative Methods
Selection of Tasks to Automate
Noneconomic and Intangible Considerations
Determination of Costs and Benefits
Utilization Analysis
30.2.3 Cost-Analysis Phase
Period Evaluation, Depreciation, and Tax Data Requirements
Project Cost Analysis
Economic Rationalization
30.2.4 Considerations of the Economic Evaluation Procedure
30.3 Alternative Approach to the Rationalization of Automation Projects
30.3.1 Strategical Justification of Automation
30.3.2 Analytical Hierarchy Process (AHP)
The AHP-PROMETHEE Method
30.4 Final Additional Considerations in Automation Rationalization
30.4.1 Investment Risk Effects on the Minimum Acceptable Rate of Return
30.4.2 Equipment Depreciation and Salvage Value Profiles
30.5 Conclusions
30.6 Recommended Additional Reading
References
31 Reliability, Maintainability, Safety, and Sustainability
31.1 Introduction
31.2 Reliability
31.2.1 Non-repairable Systems
System Configurations
31.2.2 Repairable Systems
31.3 Maintainability
31.4 Safety
31.4.1 Fault-Tree Analysis
31.4.2 Failure Modes and Effects Analysis
31.5 Resilience
31.5.1 Definition
31.5.2 Resilience Quantification
31.6 Reliability, Maintainability, Safety, and Resilience (RMSR) Engineering
31.6.1 Resilience Quantifications of Supply Chain Network
31.6.2 Degradation Modeling in Manufacturing Systems
Degradation Modeling and Reliability Prediction
Brownian Motion Degradation Model
Maximum Likelihood Estimation of the Brownian Motion Parameters
Reliability Prediction
31.6.3 Product Reliability
Burn-in Testing
Accelerated Testing
31.7 Conclusion and Future Trends
References
32 Education and Qualification for Control and Automation
32.1 Education for Control and Automation: Its Influence on Society in the Twenty-First Century
32.2 Automation Education in K-12
32.2.1 Systems and Control Education in K-12
32.2.2 Automation in K-12 Education
32.3 Control and Automation in Higher Education
32.3.1 First-Year College Students: Building an Early Understanding of Control Through Modeling Skills
32.3.2 A First Course in Systems and Control Engineering
32.3.3 Integrating Research, Teaching, and Learning in Control at a University Level
32.3.4 Remote and Virtual Labs
32.4 Conclusions
References
33 Interoperability and Standards for Automation
33.1 Introduction
33.2 Interoperability in Automation
33.2.1 The Need for Integration and Interoperability
33.2.2 Systems Interoperability
33.2.3 Enterprise Interoperability and Enterprise Integration
33.3 Integration and Interoperability Frameworks
33.3.1 Technical Interoperability
33.3.2 Semantic Interoperability
33.3.3 Organizational Interoperability
33.3.4 Technologies for Interoperability
33.4 Standards for Automation
33.4.1 Standards for Automation Project or System Management
33.4.2 Standards for Automated Systems Modeling, Integration, and Interoperability
33.4.3 Standards for E-Commerce and E-Business
33.4.4 Standards for Emerging Industry (Including I4.0, IoT, and CPS)
33.4.5 Standards for Industrial Data
33.4.6 Standards for Industrial Automation
33.4.7 Standards for Industrial and Service Robotics
33.4.8 Standards on Usability and Human-Computer Interaction
33.5 Overview and Conclusion
References
34 Automation and Ethics
34.1 Introduction
34.2 What Is Ethics, and Why Is It Used for Automation?
34.3 Dimensions of Ethics
34.3.1 Theories of Ethics
Relativistic Ethics Theories
Consequence-Based Ethics Theories
Duty-Based Ethics Theories
Character-Based Ethics Theories
34.3.2 Principles of Ethics
Asimov's Laws of Robotics—Consideration
34.3.3 Automation Ethical Concerns
34.3.4 Automation Failures and Their Ethical Aspects
Royal Majesty Grounding, 1995
Boeing 737Max Grounding, 2019
Therac-25 Linear Accelerator, 1980s
The Machine Stops AMF
34.3.5 Artificial Intelligence and Its Ethical Aspects
34.4 Protocols for Ethical Analysis
34.5 Codes of Ethics
34.6 Online Resources for Ethics of Automation
34.7 Sources for Automation and Ethics
Appendix A Code of Ethics Examples
Software Engineering Code of Ethics and Professional Practice
International Federation of Automatic Control—Code of Conduct
Appendix B: Steps of the Ethical Decision-Making
References
Part VI Industrial Automation
35 Machine Tool Automation
35.1 Introduction
35.2 The Advent of the NC Machine Tool
35.2.1 From Hand Tool to Powered Machine
35.2.2 Copy Milling Machine
35.2.3 NC Machine Tools
35.3 Development of Machining Center and Turning Center
35.3.1 Machining Center
Automatic Tool Changer (ATC)
Automatic Pallet Changer (APC)
35.3.2 Turning Center
Turret Tool Changer
35.3.3 Fully Automated Machining: FMS and FMC
Flexible Manufacturing System (FMS)
Flexible Manufacturing Cell (FMC)
35.4 NC Part Programming
35.4.1 Manual Part Programming
35.4.2 Computer-Assisted Part Programming: APT and EXAPT
35.4.3 CAM-Assisted Part Programming
35.5 Technical Innovation in NC Machine Tools
35.5.1 Functional and Structural Innovation by Multitasking and Multiaxis
Turning and Milling Integrated Machine Tool
Five-Axis Machining Center
Parallel Kinematic Machine Tool
Ultraprecision Machine Tool
35.5.2 Innovation in Control Systems Toward Intelligent CNC Machine Tools
35.5.3 Current Technologies of Advanced CNC Machine Tools
Open Architecture Control
Feedback of Cutting Information
Five-Axis Control
35.5.4 Autonomous and Intelligent Machine Tool
Digital Copy Milling for Real-Time Tool-Path Generation
Flexible Process and Operation Planning System
Adaptive Control Using Virtual Milling Simulation
Machining Error Correction Based on Predicted Machining Error in Milling Operation
35.5.5 Advanced Intelligent Technology for Machine Tools
Advanced Thermal Deformation Compensation
Optimum Spindle Speed Control for Free Chatter Vibration
Automatic Geometric Error Compensation in Five-Axis Machines
35.6 Metal Additive Manufacturing Machines or Metal 3D Printers
35.6.1 Rapid Growth of Additive Manufacturing
35.6.2 Laser Additive Manufacturing Machine Using Powder Bed Fusion
35.6.3 Five-Axis Milling Machining Center Combining Directed Energy Deposition
35.7 Key Technologies for Future Intelligent Machine Tool
References
Further Reading
36 Digital Manufacturing Systems
36.1 Introduction
36.2 Digital Manufacturing Based on Virtual Manufacturing and Smart Manufacturing Systems
36.2.1 Virtual Manufacturing
36.2.2 Smart Manufacturing Systems
36.2.3 Digital Manufacturing by Industrial Internet of Things (IIoT)-Based Automation
Key RFID Technologies
Applications of RFID in Digital Manufacturing
36.2.4 Case Studies of Digital Manufacturing
Design of Assembly Line and Processes for Motor Assembly (Kaz (Far East) Ltd., Formerly Honeywell Consumer Product (HK) Ltd.)
Virtual Manufacturing of Precision Optical Products
Digital Twin-Enabled Smart 3D Printing
IIoT-Based Cyber-Physical System for Physical Asset Management
Smart Robotic Warehouse Management
RFID-Based Intra-supply Chain Information System for Global Production Networks
RFID-Based Work-in-Progress Tracking
36.2.5 Conclusion
References
37 Flexible and Precision Assembly
37.1 Flexible and Precision Assembly Automation
37.2 Collaborative Assembly Robots
37.3 Feeding Parts
37.4 Grasping Parts
37.5 Flexible Fixturing
37.6 Aligning Small Parts
37.7 Fastening Small Parts
37.8 Assembly Automation Software Architecture
37.9 Conclusion, Challenges, and Emerging Trends
References
Additional Reading
38 Semiconductor Manufacturing Automation
38.1 Historical Background
38.2 Semiconductor Manufacturing Systems and Automation Requirements
38.2.1 Wafer Fabrication and Assembly Processes
38.2.2 Automation Requirements for Modern Fabs
38.2.3 Automation Requirements for Back-End Assembly
38.3 Equipment Integration Architecture and Control
38.3.1 Tool Architectures and Operational Requirements
38.3.2 Tool Science: Scheduling and Control
Scheduling Strategies
Schedule Quality
Controlling Wafer Delays
Workload Balancing for Tools
Advanced Tool Scheduling
38.3.3 Control Software Architecture, Design, and Development
38.4 Fab Integration Architectures and Operation
38.4.1 Fab Architecture and Automated Material-Handling Systems
38.4.2 Fab Communication Architecture and Networking
38.4.3 Fab Control Application Integration
38.4.4 Fab Control and Management
38.4.5 Smart Fabs and AI Application
38.5 Conclusions and Emerging Trends
References
39 Nanomanufacturing Automation
39.1 Introduction
39.2 AFM-Based Nanomanufacturing Technology
39.2.1 Framework of the CAD-Guided Automated Nano-assembly
39.2.2 Automatic Path Planning for Nanoobject Assembly
Modeling of the Nanoenvironments
Automated Manipulation of Nanoparticles
Automated Manipulation of Nanowire
39.2.3 Local Scan-Based Searching and Compensation Methods for Nanomanipulation
Spiral Local Scan Method for Structured Nanoobject Searching
Optimal Archimedean Spiral Local Scan for ROI Imaging
Extended NVS Control Theory for Overcoming AFM Tip Positioning Error
39.3 Nanomanufacturing Processes of CNT-Based Devices
39.3.1 Dielectrophoretic Force on Nanoobjects
39.3.2 Separating CNTs by Electronic Property Using Dielectrophoretic Effect
39.3.3 DEP Micro Chamber for Screening CNTs
39.3.4 Automated Robotic CNT Deposition Workstation
39.4 Experimental Demonstration of the Nanomanufacturing Techniques
39.4.1 Demonstration of the Local Scan-Based Nanomanipulation System
Spiral Local Scan-Based Positioning Error Compensation Test
Spiral Local Scan-Based Nanoparticle Manipulation
39.4.2 Fabrication and Testing of CNT-Based Infrared Detector
39.5 Conclusions and Emerging Trends
References
40 Production, Supply, Logistics, and Distribution
40.1 Historical Background
40.2 Machines and Equipment Automation for Production
40.2.1 Production Equipment and Machinery
40.2.2 Material Handling and Storage for Production and Distribution
40.2.3 Process Control Systems in Production
40.3 Computing and Communication Automation for Planning and Operations Decisions
40.3.1 Supply Chain Planning
40.3.2 Production Planning and Programming
40.3.3 Logistic Execution Systems
40.3.4 Customer-Oriented Systems
40.4 Automation Design Strategy
40.4.1 Labor Costs and Automation Economics
40.4.2 The Role of Simulation Software
40.4.3 Balancing Agility, Flexibility, and Productivity
40.5 Emerging Trends and Challenges
40.5.1 RFID, IoT, and IoS for Smart Manufacturing and Warehousing
40.5.2 AI and Smart Warehouses
40.5.3 Drones for Logistics and Distribution
References
Further Reading
41 Automation and Robotics in Mining and Mineral Processing
41.1 Background
41.2 Mining Methods and Application Examples
41.3 Processing Methods and Application Examples
41.3.1 Grinding Control
Instrumentation
Control Strategies
41.3.2 Flotation
Instrumentation
Flotation Control
41.4 Emerging Trends
41.4.1 Future Trends in Teleoperated Mining
Teleremote Equipment
Benefits of Teleoperated Mining
41.4.2 Future Trends in Automation of Mineral Processing
References
42 Automation in the Wood, Paper, and Fiber Industry
42.1 Background Development and Theory
42.2 Application Example, Guidelines, and Techniques
42.2.1 Timber Industry
42.2.2 Papermaking Industry
42.3 Fiber Industry
42.4 Modularity, Reuse, and Management of Variants and Versions as Enabler for Industry 4.0 or IoT
42.5 Emerging Trends and Open Challenges
References
Further Reading
43 Welding Automation
43.1 Principal Definitions
43.2 Welding Processes
43.2.1 Arc Welding
43.2.2 Resistance Welding
43.2.3 High-Energy Beam Welding
43.3 Basic Equipment and Control Parameters
43.3.1 Arc Welding Equipment
43.3.2 Resistance Welding Equipment
43.4 Welding Process Sensing, Monitoring, and Control
43.4.1 Sensors for Welding Systems
43.4.2 Monitoring and Control of Welding Process
43.5 Robotic Welding
43.5.1 Composition of Welding Robotic System
43.5.2 Programming of Welding Robots
43.6 Future Trends in Automated Welding
References
Further Reading
44 Automation in Food Manufacturing and Processing
44.1 The Food Industry
44.2 Automation and Safety
44.3 Hygienic Machine Design
44.3.1 Materials Selection
44.3.2 Joints and Seals
44.4 Automation Systems and Processes
44.4.1 Conveyor Equipment and Transport Systems
44.5 Inspection and Quality Control
44.5.1 Checkweighers [15]
44.5.2 Metal Detectors [17]
44.5.3 X-Ray Imaging Systems [18]
44.5.4 Machine Vision in the Food Industry [20]
44.6 Labeling
44.7 Packaging Systems [35]
44.7.1 Horizontal (HFFS) and Vertical Form-Fill-Seal (VFFS) Machines For Flexible Pouches
44.7.2 Horizontal Form-Fill-Seal (HFFS) Machines for Rigid and Semirigid Packages
44.7.3 Aseptic Packaging [37]
44.8 Palletizing
44.9 Dedicated Product-Dependent Handling, Assembly, and Processing Activities [38]
44.9.1 Orientation and Positioning
44.9.2 Fast Operational Speed (High-Speed Pick and Place)
44.9.3 Handling Products That Bruise
44.9.4 Handling Moist Food Products
44.9.5 Handling “Sticky” Products
44.10 Industry 4.0: Totally Integrated Automation
44.11 Conclusions
44.12 Further Reading
References
45 Smart Manufacturing
45.1 Introduction
45.2 Digital Twin in the Literature
45.3 Model-Based System Engineering
45.3.1 Examples of Digital Models
45.4 The Manufacturing of the Future
45.4.1 Manufacturing-as-a-Service
45.4.2 Sustainable Manufacturing
Circular Economy
Repurposing Products and Components
Product Reuse and Remanufacturing
Environment
Supply Chain
Business Aspects of Sustainability
45.4.3 Resilient Manufacturing
45.4.4 Extreme Manufacturing
The Openness Extreme
The Integration Extreme
45.5 Smart Enterprise
45.6 Manufacturing Concepts and Initiatives
45.6.1 Cyber-Physical Systems
45.6.2 Industry 4.0
45.6.3 Society 5.0
45.6.4 Made in China 2025
45.6.5 Smart Manufacturing
45.7 Evolution of Intelligent Manufacturing
45.8 Conclusions and Emerging Trends
45.8.1 Establishment of Problem Definition Networks
45.8.2 Development of Cyber-Platforms of Modeling and Innovation
45.8.3 Making Data Sharing a Reality
45.8.4 Introducing Smart Manufacturing Policies
45.8.5 Future Trends
References
Part VII Infrastructure and Service Automation
46 Automation in Data Science, Software, and Information Services
46.1 Preamble
46.1.1 Evolution of the Information, Software, and the Data Science Services Industry
46.1.2 The Opportunity for Automation
46.2 Distinct Business Segments
46.3 Automation Path in Information Services
46.3.1 Delivery of Data and Information
46.3.2 Business Process Outsourcing
46.3.3 Analytics
46.3.4 Printing and Display Solutions
46.3.5 Information Flow in Supply Chain
46.4 Automation Path in Information Technology Services
46.4.1 Computer-Aided Software Engineering
46.4.2 Independent Software Testing and Quality Assurance
46.4.3 Package and Bespoke Software Implementation and Maintenance
46.4.4 Network and Security Management
46.4.5 Hosting and Infrastructure Management
46.5 Automation Path in Data Science Services
46.5.1 Data Science
46.6 Impact Analysis
46.7 Emerging Trends
References
47 Power Grid and Electrical Power System Security
47.1 Background of Electric Power Systems
47.1.1 A Brief History
47.1.2 Definition of Power System Security
47.1.3 Multi-Scale Planning and Operation
47.2 Power System Planning
47.2.1 System Planning for Regulated Utilities
Demand Forecast
Identifying Resources to Meet Future Demand
Optimization Models of Long-Term Planning
47.2.2 System Planning Under Deregulation
47.2.3 Challenges to System Planning
47.3 Power System Operation
47.3.1 Unit Commitment and Economic Dispatch
Optimization Models of Unit Commitment and Economic Dispatch
47.3.2 Co-optimization of Energy and Reserve
Types of Operating Reserve Resources and Services
Co-optimization of Energy and Reserve Resources
47.3.3 Challenges to Short-Term Operations
47.4 Power System Cybersecurity
References
48 Construction Automation and Smart Buildings
48.1 Introduction
48.2 Motivations for Construction Automation and Smart Buildings
48.2.1 Building Representation
48.2.2 Safer Operations
48.2.3 Building Sustainability
48.3 Historical Advancements
48.4 Horizontal Construction Automation
48.5 Building Construction Automation
48.6 Cost and Schedule Automation
48.7 Construction Monitoring Automation
48.8 Design–Construction Coordination Automation
48.9 IoT for Building Energy Performance
48.10 Predictive Maintenance Planning Using BIM and IoT
48.11 Prefabricated Construction*0.5pt
48.12 3D Printing*0.5pt
48.13 Conclusions and Challenges
48.14 Further Reading
References
49 Agriculture Automation
49.1 Introduction
49.2 Sensors in Agriculture
49.2.1 Overview
49.2.2 Sensing in Field and Orchard Environments
Sensing for Navigation
Soil Sensing
Crop Sensing for Fertilization and Irrigation
Weeds Sensing
Crop Disease Sensing
49.2.3 Sensing in Greenhouse
Energy and Climate Control
Irrigation and Nutrient Supply
Pest Management
49.2.4 Sensing in Livestock
Environment and Animal Welfare
Animal Behavior and Health
49.3 Internet of Things in Agriculture
49.3.1 Overview
49.3.2 Greenhouse
49.3.3 Field Crops
Irrigation
Fertilization
Pest Management
49.3.4 Livestock
49.4 Robotics in Agriculture
49.4.1 Overview
49.4.2 Greenhouses and Field Crops
Cultivating Operations
Weeding
Spraying
Crop Monitoring
49.4.3 Orchard Crops
49.4.4 Livestock
49.5 Artificial Intelligence in Agriculture
49.5.1 Overview
49.5.2 Greenhouse
49.5.3 Field Crops
Pest, Disease, and Weed Management
Irrigation and Fertilization
49.5.4 Livestock
49.6 Emerging Trends
References
Additional Reading
50 Connected Vehicles and Driving Automation Systems
50.1 History, Background, and Terminology
50.1.1 History of Road and Vehicle Automation
50.1.2 History of Connected Vehicles
50.1.3 CV Technology
50.1.4 AV Technology
Sensing and Perception
Sensors
Simultaneous Localization and Mapping
Planning and Control
Deep Learning Models
AV Use Cases
50.1.5 Relationship Between Automation and Connectivity
50.1.6 Changing Threat Landscape
50.1.7 Cybersecurity Standards
50.1.8 Terminology
Driving Automation
DDT Fallback
Connected Vehicle Terminology
50.2 CV and AV Deployments in the U.S
50.3 CV and AV Deployments in Europe
50.4 ITS Architectures
50.4.1 European Union FRAME Architecture
50.4.2 United States ARC-IT
Safety and Security Aspects
50.4.3 TOGAF
SPACE (Shared Personalized Automated Connected vEhicles)
50.4.4 NIST Cybersecurity Framework
50.5 Government Roles
50.5.1 United Nations
50.5.2 United States: Federal and State Roles
50.5.3 European Union (EU)
50.6 Connected Vehicles (CVs)
50.6.1 Connected Vehicle Security: Secure Communications
USA – Security Credential Management System (SCMS)
SCMS Process
C-ITS Credential Management System (CCMS)
50.7 Conclusions, Challenges, and Further Research Needs
References
Further Reading
51 Aerospace Systems Automation
51.1 Manufacturing
51.1.1 Level of Automation
51.1.2 Current State of Automation in Aerospace
51.1.3 Part Fabrication
The Advent of CNC Machines
The Concept of Near-Net Shape
The Advent of 3D Printing
Castings
Superplastic Forming
51.1.4 Subassemblies
Paradigm for Design
Carbon Fiber Composites
51.1.5 Final Assembly
Airbus and A320
Boeing and 777X
An Engine OEM
51.1.6 Non Air Transport Markets
Business and General Aviation
Military
Emerging Markets: Urban Air Mobility
Automotive as a Benchmark
51.1.7 Manufacturing Closing Thoughts
51.2 Automation in Aircraft Systems and Operations
51.2.1 Guidelines for Automation Development
Control Automation
Warning and Alerting Systems
Information Automation
Human Factors Issues
Software and System Safety
System Integration
Certification and Equipage
51.3 Automation in Air Traffic Control Systems and Operations
51.3.1 Sequencing and Scheduling Automation
51.3.2 Conflict Detection and Resolution Automation
51.3.3 Future Automation Needs
51.4 Conclusion
References
Web Resources
52 Space Exploration and Astronomy Automation
52.1 Scope and Background
52.2 Agents, Automation, and Autonomy
52.3 Functions and Constraints on Automation
52.4 Signals and Communications for Automation in Space Observation and Exploration
52.4.1 Radio Astronomy and Automation for Space Observation
52.4.2 Automation Requirements for Satellite Communications
52.5 Automation System Hardening, Protection, and Reliability
52.6 Multisystem Operations: Past, Present, and Future
52.6.1 Lunar Mission Coordination
52.6.2 Space Shuttle Human-Automation System Interactions
52.6.3 Distributed Astronomy and Human-Automation Interactions
52.6.4 Future Automation-Automation and Human-Automation Exploration
52.7 Additional Challenges and Concerns for Future Space Automation
52.7.1 Cybersecurity and Trusted Automation
52.7.2 Distributed Space-Based High-Performance Computing
52.7.3 Enhanced Awareness and “Projective Freshness”
52.8 Conclusion
References
53 Cleaning Automation
53.1 Introduction
53.2 Background Developments, Cleaning Automation Examples
53.2.1 Floor Cleaning Robots
53.2.2 Pool, Facade, Window, Hull, Solar Panel, Ventilation Duct, and Sewer Line Cleaning Robots
Pool Cleaning Robots
Facade, Window, and Solar Panel Cleaning Robots
Hull Cleaning Robots
Ventilation Duct and Sewer Line Cleaning Robots
53.3 Emerging Trends
Literature
Journals
Proceedings
Internet Links
54 Library Automation and Knowledge Sharing
54.1 In the Beginning: Book Catalogs and Card Catalogs
54.2 Foundations for the Digital Age: Indexes and the Beginnings of Online Search
54.3 Development of the MARC Format and Online Bibliographic Utilities
54.3.1 Integrated Library Systems
54.3.2 Integrated Library Systems: The Second Generation
54.3.3 The Shift to End-User Search Indexes
54.4 The First Generation of Digital Library Tools
54.4.1 OpenURL Linking and the Rise of Link Resolvers
54.4.2 Metasearching
54.4.3 Electronic Resource Management
54.5 Digital Repositories
54.6 Library Service Platforms: The Third Generation Library Automation System
54.6.1 From OPAC to Discovery
54.6.2 The Library Service Platform
54.7 Evolving Data Standards and Models, Linked Data
54.8 Two Future Challenges
References
Part VIII Automation in Medical and Healthcare Systems
55 Automatic Control in Systems Biology
55.1 Background, Basics, and Context
55.1.1 Systems Biology
55.1.2 Control of and in Biological Systems
55.2 Biophysical Networks
55.2.1 Circadian Processes: Timing and Rhythm
55.2.2 Signaling in the Insulin Pathway
55.3 Network Models for Structural Classification
55.3.1 Hierarchical Networks
55.3.2 Boolean Networks and Associated Structures
55.4 Dynamical Models
55.4.1 Stochastic Systems
55.4.2 Modeling Metabolism: Constraints and Optimality
Physico-Chemical Constraints in Metabolism
Functional Constraints, Optimality, and Design
55.5 Network Identification
55.5.1 Data-Driven Methods
55.5.2 Linear Approximations
55.5.3 Mechanistic Models, Identifiability, and Experimental Design
55.5.4 Sensitivity Analysis and Sloppiness
55.6 Control of and in Biological Processes
55.6.1 The Artificial Pancreas
55.6.2 The Antithetic Integral Feedback Network
55.6.3 Optogenetic Control of Gene Expression
55.7 Emerging Opportunities
References
56 Automation in Hospitals and Health Care
56.1 Need for Digital Transformation in Hospitals and Health Care
56.1.1 Background
56.1.2 Realization of Human-Centric Health Care
56.2 The Key Technologies
56.2.1 History of Major Technologies
56.2.2 Standardization of Health Care
56.2.3 Changes in Medical Institutions Toward Cutting-Edge Technologies
56.3 Use Cases of Application
56.3.1 Imaging AI
56.3.2 Smart Surgery
56.3.3 Robotics Solutions
56.3.4 Remote Patient Monitoring
56.4 Digital Platform
56.4.1 Architecture
56.4.2 Healthcare AI Platform
56.4.3 Platform in Hospitals
56.4.4 PHR and Beyond
56.5 Emerging Trends and Challenges
References
57 Medical Automation and Robotics
57.1 Surgical Robots
57.1.1 Medical Robotic Devices
57.1.2 Surgical Remote Manipulators
57.1.3 Navigation and Display
57.1.4 Kinematic Structure of Surgical Robots
57.1.5 Fundamental Requirements from a Surgical Robot
57.1.6 Main Advantages of Surgical Robotic Systems
57.2 Rehabilitation and Assistive Robots
57.3 Emerging Trends in Medical Automation
57.3.1 Additive Manufacturing
57.3.2 Incorporating Artificial Intelligence
References
58 Precision Medicine and Telemedicine
58.1 Introduction of Precision Medicine
58.2 Technologies and Applications Related to Precision Medicine
58.2.1 Genetic Sequencing
58.2.2 Transcriptomics
58.2.3 Epigenomics
58.2.4 Proteomics
58.2.5 Metabolomics
58.3 Precision Medicine from the Perspectives of Data Science, Big Data, and Artificial Intelligence
58.3.1 Data Science and Big Data for Biomedical Informatics
58.3.2 Medical Decision Support by AI
58.4 Telemedicine
References
59 Wearables, E-textiles, and Soft Robotics for Personalized Medicine
59.1 Background and Introduction
59.1.1 Revolutionizing Patient Care Through Personalized Medicine
59.1.2 Wearable Devices for Personalized Medicine
59.1.3 Smart Textiles for Personalized Medicine
59.1.4 Soft Robotics for Personalized Medicine
59.2 Design Principles for Biomedical Wearable Devices
59.3 Interfacing Humans and Wearables for Healthcare Applications
59.4 Close-Contact and Implantable Biomedical Wearables Devices
59.4.1 Electronic Tattoos and Smart Stickers
59.4.2 Smart Bandages
59.4.3 Wearable Bioelectronics
59.5 Loose-Contact Biomedical Wearable Devices
59.5.1 Smart Jewelry
59.5.2 e-Textiles
59.5.3 Powering Strategies for Loose-Contact Biomedical Wearables
59.5.4 Breathable and Waterproof Loose-Contact Wearables
59.6 Wearable Soft Robotics
59.6.1 Rehabilitation and Assistance Using Wearable Soft Robots
59.6.2 Soft Robotic Prostheses
59.6.3 Artificial Soft Robotic Organs
59.7 Big Data for Personalized Medicine
59.7.1 Wearable Data Analysis and Interpretation
59.8 Remaining Challenges and Emerging Areas
59.8.1 Emerging Trends in e-Tattoos and Smart Stickers
59.8.2 Emerging Trends in Smart Bandages
59.8.3 Emerging Trends in Wearable Bioelectronics
59.8.4 Challenges for Big Data and Emerging Trends
59.8.5 Challenges and Emerging Trends in Wearable Soft Robotics
References
60 Healthcare and Pharmaceutical Supply Chain Automation
60.1 Introduction
60.2 Background
60.3 General Schemes and Components of HPhSC Management
60.3.1 Supply and Production in HPhSC Network
60.3.2 Inventory Management in HPhSC Network
60.3.3 Distribution and Logistics in HPhSC Network
60.3.4 Healthcare Delivery in HPhSC Network
60.3.5 Healthcare Consumers in HPhSC Network
60.4 Challenges and Solutions in HPhSC Management
60.4.1 The HPhSC Concerns and the Role of Automation
Big Medical Data and Information Integration Issues
Drug Distribution and Logistics Issues
Drug Storage and Shortage Issues
Healthcare Security and Privacy Issues
Lack of Standardization Issues
Human Resource and Training Issues
60.4.2 The Automation Technologies Concerns and Limitations
IoTs Technology Challenges in HPhSC
AI and Robotics Challenges in HPhSC
Blockchain Technology Challenges in HPhSC
RFID Tags Challenges in HPhSC
AGV and Drone Challenges in HPhSC
60.5 Automation Application Areas in HPhSC Management
60.6 The Effect of the COVID-19 Pandemic on HPhSC Automation
60.6.1 The Role of Automation During the COVID-19 Pandemic
The Global Rise of 3-D Printing During the Pandemic
Unmanned Aerial Vehicle Applications During the Pandemic
Robotic and Automated Guided Vehicles Solutions During the Pandemic
60.6.2 COVID-19 Vaccine Distribution Challenges in HPhSC
60.7 Conclusions and Future Directions
References
Part IX Home, Office, and Enterprise Automation
61 Automation in Home Appliances
61.1 Background
61.1.1 History
61.1.2 Fundamental Technologies
Microprocessor Controls
Sensors
Connectivity
61.2 Applications of Home Automation
61.2.1 Refrigeration
61.2.2 Cooking
61.2.3 Cleaning
61.2.4 Lighting and HVAC
61.2.5 Security
61.2.6 Media Center
61.2.7 General Appliance Automation
Maintenance Automation
Energy Management Automation
61.2.8 Home Automation Robots
61.3 Enabling Technologies
61.3.1 Smart Home Hubs with Virtual Assistants
61.3.2 Internet of Things (IoT) Technologies and Protocols
IoT Architecture
IoT Protocols
IoT and Home Automation Platforms
61.4 Emerging Trends and Open Challenges
61.4.1 Trends
Display
Interconnectivity
Efficient Utility Usage
Data Analytics, Machine Learning, and AI
61.4.2 Challenges
Cost
Security
Interoperability
References
62 Service Robots and Automation for the Disabled and Nursing Home Care
62.1 Rising Demand
62.2 State of the Art
62.2.1 Mobility Aids
Robotic Walkers
Robotic Wheelchairs
62.2.2 Manipulation Aids
62.2.3 Interaction Robots
62.2.4 Integrated Mobile Manipulators
Humanoid Robots with Legs
Wheel-Based Robots
62.2.5 Orthoses and Exoskeletons
62.2.6 Prostheses
62.3 Application Example: Robotic Home Assistant “Care-O-bot®”
62.3.1 History of Care-O-bot® Development
62.3.2 Key Technologies
Autonomous Navigation
Object Detection
Collision-Free Object Manipulation
62.3.3 Implemented Assistance Scenarios
Execution of Fetch-and-Carry Tasks
Supporting Users at the Meal Table
62.4 Application Example: An Exoskeleton for Hand Habilitation
62.5 Emerging Trends and Challenges
References
63 Automation in Education, Training, and Learning Systems
63.1 Overview of Instructional Design (ID) and Education/Learning Methods
63.2 Trends of Education/Learning Methods*2pt
63.2.1 Learner-Centered Education*2pt
63.2.2 Active Leaning
63.2.3 Flipped Classroom
63.2.4 Adaptive Learning and Self-Paced Learning
63.3 Learning Management System (LMS) Tools/Functions
63.3.1 History of Computer-Assisted Education/LMS on E-Learning
Definitions of E-Learning
E-Learning Peripheral Technologies
Value Creation by E-Learning
E-Learning Platforms and Standards
63.3.2 Utilization of LMS Through Online Education
Nuances to LMS: Proprietary Versus Open Sources
63.4 Open and Flexible Learning and Massive Open Online Courses (MOOC)
63.4.1 Open and Flexible Learning
63.4.2 International Trends and Platforms of MOOC
63.5 Learning Analytics Research for Educational Digital Transformation (DX)
63.6 Applicability of Artificial Intelligence and Advanced Educational Technologies
63.6.1 Adoption Perspective of Artificial Intelligence
Tutoring and Advising
Grading and Assessments
63.6.2 AI Used in Knowledge Management and Skills Development
63.6.3 Natural Language Processing
63.6.4 Applicability of Advanced Educational Technologies
Mobile Learning
63.7 Example: Empirical Research of Educational Programs for Business Producers
63.7.1 Curriculum Design Combined with PBL and AL Methods
63.7.2 PBL Theme and Four Types of Different Group Roles for AL
63.7.3 Learning Support as Educational Organizations Dealing with COVID-19
63.8 Example: Future Proposal of Hybrid Learning Platform
63.8.1 Future Proposal of Conceptual Design Framework of Hybrid Learning Platform
63.8.2 Online Lesson Environments as a Countermeasure Against COVID-19
63.8.3 Required Specifications of Hybrid Learning Platform Along ID ADDIE Models
Analysis Phase
Design Phase
Development Phase
Implementation Phase
Evaluation Phase
63.9 Conclusions and Emerging Trends
References
64 Blockchain and Financial E-services
64.1 Introduction
64.2 The Financial Services Before 2010
64.3 Fintech Development in the New Era
64.4 An Overview of Blockchain
64.5 Bitcoin and Cryptocurrency Finance
64.6 Blockchain Interacts with RPA, AI, and Big Data
64.6.1 Robotic Process Automation
64.6.2 Artificial Intelligence
64.6.3 Big Data
64.7 Conclusion and Emerging Trends
References
65 Enterprise and Business Process Automation
65.1 Introduction
65.2 Setting the Stage for ERP
65.2.1 The Introduction of ERP
65.2.2 Structure of an ERP System
65.2.3 The Implications of an ERP Implementation
65.3 Evolution of ERP
65.3.1 The Internet as a Disruptive Force
65.3.2 Post 2000 ERP and SOA
65.3.3 Emergence of ERP III
65.3.4 ERP as a Management Challenge
65.4 Emerging Trends
65.4.1 Cloud-Based ERP Systems
65.4.2 Blockchain-Based ERP Systems
65.4.3 Artificial Intelligence-Based ERP Systems
65.5 Conclusion and Challenges
References
Further Reading
66 Decision Support and Analytics
66.1 Introduction
66.2 Characteristics of DSS
66.2.1 Management Information Needs
66.2.2 Communications-Driven and Group DSS
66.2.3 Data-Driven DSS
66.2.4 Document-Driven DSS
66.2.5 Knowledge-Driven DSS
66.2.6 Model-Driven DSS
66.2.7 Secondary Dimensions
66.3 Building Decision Support Systems
66.4 DSS Architecture
66.5 Recent Updates and Emerging Trends
66.6 Conclusion and Challenges
References
Further Reading
67 E-commerce
67.1 Introduction
67.2 Background
67.3 Theory
67.3.1 Definitions of e-Commerce
67.3.2 Frameworks for e-Commerce
67.3.3 E-Commerce Success Parameters
67.4 E-Commerce Models
67.4.1 B2C Model
67.4.2 B2B Model
Individual Trading
Collaboration
Marketplace
Proprietary Sales
Private Exchange
Aggregation
Intranet/EDI
Restricted Bid
Reverse Auction
67.4.3 C2C and C2B Models
67.5 E-Commerce Applications
67.5.1 Online Shopping
67.5.2 Online Banking
67.5.3 Electronic Learning
67.5.4 Online Customer Auctions
67.6 Conclusions, Emerging Trends, and Challenges in e-Commerce
67.6.1 Electronic Supply Networks
67.6.2 Big Data Analytics
67.6.3 Sharing Economy Business Models
67.6.4 Challenges in e-Commerce
67.6.5 Chapter Summary
References
Part X Automation Case Studies and Statistics
68 Case Study: Automation Education and Qualification Apprenticeships
68.1 Case Challenges and Background
68.2 Recommended Solution
68.3 Conclusion and Measures of Impact
References
69 Case Study: IBM – Automating Visual Inspection
69.1 Case Challenges
69.2 Solution Overview
69.2.1 History
69.2.2 Differences Between AI and Rule-Based Systems
69.3 Why Now?
69.3.1 Computing Hardware Advances
69.3.2 Software Advances
Establishment of Common Frameworks
Benchmarks Drive the State of the Art
Transfer Learning Advances AI for Model Training and Inference
69.3.3 Edge Compute Advances
69.3.4 Applying AI-Based Computer Vision to Visual Inspection
69.4 Applications of AI-Based Computer Vision to Automotive Manufacturing
69.4.1 Solution Architecture
69.4.2 Efficiency Gains and Cost Savings
Defect Detection In-Station
Improving Talent Utilization
Reducing Excess Processing
69.5 Special Considerations
69.5.1 Ease of Configuration
69.5.2 Ease of Deployment
69.5.3 Integration into Existing Human Workflows
69.5.4 Network Security
69.5.5 Edge Computing
69.5.6 Maturity of AI
References
70 Case Study: Infosys – Talent Management Processes Automation with AI
70.1 Infosys Brings Sentience to Its Talent Management Processes
70.1.1 Preamble
70.1.2 The Recruitment Challenge at Infosys
70.1.3 Transforming the Recruitment Functions from 2010 to 2020 (Pre-Covid)
70.1.4 COVID Period (2020 March Onward)
70.1.5 Implementation Challenges
70.1.6 Key Outcomes
70.1.7 Way Forward
Appendix 1: A Detailed Look at Major Activities and Their Intricacies
Sourcing: Raising Hiring Requests and Finding Resumes
Screening: Prescreening, Shortlisting, and Prescheduling
Interview: Scheduling, Panel Management, and Conducting Interviews
Selection: Offer and Acceptance
Allocation: Onboarding, Training, and Project Allocation
71 Case Study: Intel and Claro 360 – Making Spaces Safe During Pandemic
71.1 Case Challenges
71.2 Recommended Solution
71.2.1 Solution Areas
Public Safety
Healthcare
Work Transformation
Education
Retail
Smart Spaces
71.2.2 Smart Spaces Implementation Using Computer Vision and Artificial Intelligence
Social Distance Enforcement Execution
Capacity Limit Enforcement
One-Way Aisles
Line Monitoring
Other Vision Solutions
71.2.3 Smart Spaces Implementation: Intel 3rd Generation Xeon Scalable Processors Usage
Benefits on Using Intel 3rd Generation Xeon Scalable
AI Acceleration
Security, Integrity, and Confidentiality
Software and Hardware Configuration to Support the Solution
71.2.4 Retail Pandemic Reference Implementations
Economic, Management, and Social Considerations
Partner Ecosystem
71.2.5 Future Work and Impacts with Claro 360
Government Public Safety
Healthcare
Workplace Transformation
Retail
References
72 Case Study: Siemens – Flexible Robot Grasping with Deep Neural Networks
72.1 Introduction
72.2 Problem Definition and Requirements
72.3 Related Work
72.4 Mechatronic System Design
72.5 Software Design
72.6 Deployment and Evaluation
72.7 Conclusion
References
73 Case Study: 3M – Automation in Paint Repair
73.1 Case Challenges
73.2 Recommended Solution
References
74 Automation Statistics
74.1 Introduction
74.2 Automation Statistics
74.2.1 e-Commerce and Financial Automation (Figs. 74Fig174.1, 74Fig274.2, 74Fig374.3, 74Fig474.4, 74Fig574.5, 74Fig674.6, and 74Fig774.7)
74.2.2 Industrial Automation (Figs. 74Fig874.8, 74Fig974.9, 74Fig1074.10, 74Fig1174.11, 74Fig1274.12, 74Fig1374.13, 74Fig1474.14, 74Fig1574.15, 74Fig1674.16, and 74Fig1774.17)
74.2.3 Smart Automations (Figs. 74Fig1874.18, 74Fig1974.19, 74Fig2074.20, 74Fig2174.21, 74Fig2274.22, 74Fig2374.23, 74Fig2474.24, 74Fig2574.25, 74Fig2674.26, 74Fig2774.27, 74Fig2874.28, 74Fig2974.29, 74Fig3074.30, 74Fig3174.31, 74Fig3274.32, and 74Fig3374.33)
74.2.4 Publications Related to Topics in Automation (Figs. 74Fig3474.34, 74Fig3574.35, 74Fig3674.36, 74Fig3774.37, 74Fig3874.38, and 74Fig3974.39)
74.3 Automation Associations
74.4 Automation Laboratories Around the World
74.5 Automation Journals Around the World
References
Index
