This book presents a novel design framework for the development of Digital Twin (DT) models for process- and motion-control applications. It is based on system-data acquisition using cutting-edge computing technologies, modelling of physical-system behavior through detailed simultaneous simulation of different aspects of the system, and optimal dynamic behavior-matching of the process. The design framework is enhanced with real-time data analytics to improve the performance of the DT’s behavior-matching with the real system or physical twin.
The methods of creating a DT detailed in Digital-Twin-Enabled Smart Control Engineering make possible the study of a system for real-time controller tuning and fault detection. They also facilitate life-cycle analysis for multiple critical and dangerous conditions that cannot be explored in the corresponding real system or physical twin. The authors show how a DT can be exploited to enable self-optimizing capabilities in feedback control systems.
The DT framework and the control-performance assessment, fault diagnosis and prognosis, remaining-useful-life analysis, and self-optimizing control abilities it allows are validated with both process- and motion-control systems and their DTs. Supporting MATLAB-based material for a case study and an expanded introduction to the basic elements of DTs can be accessed on an associated website.
This book helps university researchers from many areas of engineering to develop new tools for control design and reliability and life-cycle assessment and helps practicing engineers working with robotic, manufacturing and processing, and mechatronic systems to maintain and develop the mechanical tools they use.
Author(s): Jairo Viola, YangQuan Chen
Series: Synthesis Lectures on Engineering, Science, and Technology
Publisher: Springer
Year: 2023
Language: English
Pages: 119
City: Cham
Preface
Acknowledgements
Contents
Abbreviations
1 Digital Twin Background
1.1 Introduction
1.2 What Is a Digital Twin?
1.3 Digital Twin Requirements and Structure
1.4 Challenges on the Digital Twin Implementation
1.5 What Is Not a Digital Twin?
1.6 Digital Twin Applications
1.7 A Literature Review of Digital Twin
1.8 Summary
2 A Digital Twin Development Framework
2.1 Development Framework for Digital Twins Applications
2.2 Digital Twin Frameworks in the Literature
2.3 A Step-by-Step Digital Twin Construction Showcase: Temperature Control with a Thermal Infrared Camera
2.4 Summary
3 Digital Twin Enabling Capabilities
3.1 Introduction
3.2 Modeling, Analysis, and Design Methodology for Control Engineering Practice and Education
3.3 Control Performance Assessment
3.4 Parallel Control under Artificial, Computational, and Parallel Execution Approach
3.5 Fault Diagnosis, Prognosis, and Health Management
3.6 Self-Optimizing Control
3.7 Edge Computing Devices for Digital Twin
3.8 A Case Study: Fault Detection and Remaining Useful Life Analysis for Thermal Systems
3.9 Summary
4 Smart Control Engineering Enabled by Digital Twin
4.1 Introduction
4.2 What Is a Smart System?
4.3 Smart Control Engineering: A New Frontier
4.4 Case Study 1: Self-Optimizing Control Based on Globalized …
4.5 Case Study 2: Velocity and Position Control of a Smart Mechatronic System
4.6 Summary
5 Summary and Future Research Opportunities
5.1 Introduction
5.2 Digital Twin Multimodel Assessment
5.3 Digital Twin Standardization and Interoperability
5.4 Convergence, Stability, and Globalness Analysis of Self-Optimizing Control Algorithms
5.5 Accelerated Learning Using Faster Convergence Optimization Algorithms …
5.6 Parallel Computing and Digital Twin
5.7 Digital Twin for Control Education
Index
Index
