Modeling, Identification, and Control for Cyber- Physical Systems Towards Industry 4.0

1st Edition - January 9, 2024
Editors: Paolo Mercorelli, Weicun Zhang, Hamidreza Nemati, YuMing Zhang
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 5 2 0 7 - 1
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 5 2 0 8 - 8

Modeling, Identification, and Control for Cyber-Physical Systems Towards Industry 4.0 studies and analyzes the role of algorithms in identifying and controlling such a system to… Read more

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Modeling, Identification, and Control for Cyber-Physical Systems Towards Industry 4.0 studies and analyzes the role of algorithms in identifying and controlling such a system towards Industry 4.0, which is the digital transformation of manufacturing and related industries and value creation processes. This book focuses on the conception and implementation of intelligent algorithms. It will help readers who work on sensors, virtual sensors, actuators and virtual actuators embedded systems, network infrastructures, servers with computing and storage capacity, autonomous computing software, real-time data processing, and database graphical user interfaces wireless networking technologies.

Cyber-Physical Systems are network components that coordinate physical actions with each other. These autonomous systems perceive their surroundings using virtual sensors and actively influence them via virtual actuators. Adaptable and continuously evolving, these systems free up skilled workers to perform complex tasks, avoiding productivity loss and re-work.

Cover image
Title page
Table of Contents
Copyright
Dedication
Epigraph
Contributors
Biography
Paolo Mercorelli
Prof. Weicun Zhang
Hamidreza Nemati
YuMing Zhang
Preface
Objectives
Chapter 1: Industry 4.0 more than a challenge in modeling, identification, and control for cyber-physical systems
Abstract
1.1. Introduction
1.2. Theoretical background
1.3. Method and implementation
1.4. Conclusions
References
Part I: Manufacturing as a challenge in Industry 4.0 process
Introduction
References
Chapter 2: Advanced ice-clamping control in the context of Industry 4.0
Abstract
2.1. Introduction
2.2. Model
2.3. Advanced ice-camping structure
2.4. Measured results and performance evaluation
2.5. Towards intelligent clamping
2.6. Towards Industry 4.0
2.7. Conclusions
Appendix.
References
Chapter 3: Temperature control in Peltier cells comparing sliding mode control and PID controllers
Abstract
3.1. Introduction
3.2. Sliding mode control law derivation
3.3. Experimental validation
3.4. Controller extension
3.5. Controller comparison
References
Chapter 4: A Digital Twin for part quality prediction and control in plastic injection molding
Abstract
Funding
4.1. Introduction
4.2. Plastic injection molding
4.3. Data acquisition and management
4.4. Control-oriented modeling of final part quality
4.5. Case study: tamper-evident closure quality prediction
4.6. Conclusions and outlook
References
Part II: Motion control and autonomous robots as a challenge in Industry 4.0 process
Introduction
Introduction and challenges with autonomous robots and motion control
Getting the software right
Gathering enough real-world data
Creating precision motion control
Reducing false positives
Installation must be simple
References
Chapter 5: SLAM algorithms for autonomous mobile robots
Abstract
5.1. Introduction
5.2. SLAM classification
5.3. liDAR-SLAM
5.4. Visual SLAM algorithm
5.5. Conclusions
Appendix.
References
Chapter 6: Optimization of motion control smoothness based on Eband algorithm
Abstract
6.1. Introduction
6.2. Eband algorithm implementation principle
6.3. Improved Eband algorithm
6.4. Experiment analysis
6.5. Conclusions
References
Chapter 7: Modeling a modular omnidirectional AGV developmental platform with integrated suspension and power-plant
Abstract
7.1. Motivation for the use of omnidirectional AGVs
7.2. Design of the novel two-wheel swerve drive AGV
7.3. Kinematics
References
Chapter 8: Control system strategy of a modular omnidirectional AGV
Abstract
8.1. Introduction
8.2. Test methodology
8.3. Results
8.4. Combination test
8.5. Conclusions
References
Chapter 9: Mecanum wheel slip detection model implemented on velocity-controlled drives
Abstract
9.1. Introduction
9.2. Hardware considerations
9.3. Slip mitigation controller
9.4. Test methodology
9.5. Discussion
9.6. Conclusions
References
Chapter 10: Safety automotive sensors and actuators with end-to-end protection (E2E) in the context of AUTOSAR embedded applications
Abstract
10.1. Introduction
10.2. Architecture of a car
10.3. Communication stack in AUTOSAR
10.4. End-to-end protection (E2E) in AUTOSAR embedded applications
10.5. Conclusions
References
Part III: Motion and control of autonomous unmanned aerial systems as a challenge in Industry 4.0 process
Introduction
References
Chapter 11: Multibody simulations of distributed flight arrays for Industry 4.0 applications
Abstract
11.1. Introduction
11.2. Aims, objectives, and scopes
11.3. Background research
11.4. Methodology
11.5. Methods
11.6. Data analysis
11.7. Discussions, critical thinking, and reflections
11.8. Conclusions
11.9. Recommendations for future work
References
Chapter 12: Recent advancements in multi-objective pigeon inspired optimization (MPIO) for autonomous unmanned aerial systems
Abstract
12.1. Introduction
12.2. State of the art
12.3. Problem statement and solutions
12.4. Advancements of MPIO and its variants
12.5. Conclusions
References
Chapter 13: U-model-based dynamic inversion control for quadrotor UAV systems
Abstract
13.1. Introduction
13.2. Quadrotor dynamic model
13.3. U-model dynamic inversion-based control system design
13.4. Simulation study
13.5. Conclusions
References
Chapter 14: Nonlinear control allocation applied on a QTR: the influence of the frequency variation
Abstract
Acknowledgement
14.1. Introduction
14.2. Nonlinear aircraft modeling and control allocation
14.3. P-PID controllers
14.4. SITL scheme
14.5. Simulation results
14.6. Conclusions
References
Part IV: Theoretical and methodological advancements in disturbance rejection and robust control
Introduction
Introduction and issues of this part
Emergence of theoretical implications to use control loops
Conclusion
References
Chapter 15: Active disturbance rejection control of systems with large uncertainties
Abstract
15.1. Introduction
15.2. From PID to ADRC, MPC, and adaptive control
15.3. Multiple model ADRC for systems with large uncertainties
15.4. Simulation verification
15.5. Conclusions
References
Chapter 16: Gain scheduling design based on active disturbance rejection control for thermal power plant under full operating conditions
Abstract
16.1. Introduction
16.2. Problem formulation
16.3. Active disturbance rejection control
16.4. Gain scheduling design based on ADRC
16.5. Simulations validations
16.6. Conclusions
Appendix A.
Appendix B.
References
Chapter 17: Active disturbance rejection control of large-scale coal fired plant process for flexible operation
Abstract
17.1. Introduction
17.2. Problem formulation
17.3. Design procedure
17.4. Experiment verification
17.5. Field test
17.6. Summary
References
Chapter 18: Desired dynamic equational proportional-integral-derivative controller design based on probabilistic robustness
Abstract
18.1. Introduction
18.2. Problem formulation
18.3. DDE PID principles of DDE PID
18.4. DDE PID design based on PR
18.5. Simulation validation
18.6. Experimental verification
18.7. Conclusions
References
Index

Paolo Mercorelli

Paolo Mercorelli received his MS degree (Laurea) in Electronic Engineering from the University of Florence, Italy, in 1992, and Ph.D. degree in Systems Engineering from Alma Mater Studiorum University of Bologna, Italy, in 1998. He was awarded the Marie Sklodowska-Curie Actions Research Fellowship sponsored by the European Commission in 1998, and spent time in the ABB (Asea Brown Boveri) Corporate Research Heidelberg until 2001. From 2002 to 2005, he was a Senior Researcher with the Institute of Automation and Informatics, Wernigerode, Germany. From 2005 to 2011, he became Associate Professor of Process Informatics at Ostfalia University of Applied Sciences, Wolfsburg, Germany. There, he was involved in various projects with the Volkswagen AG Research Center. Since 2012, he has been a distinguished Full Professor (Chair) of Control and Drive Systems at the Institute of Product and Process Innovation, Leuphana University of Lueneburg, Lueneburg, in Germany, and has recently obtained an International Distinguished Visiting Professor Fellowship at the Institute of Automatic Control of Lodz University of Technology, in Poland.

Affiliations and expertise

Professor (Chair), Control and Drive Systems, Institute of Product and Process Innovation, Leuphana University of Lueneburg, Lueneburg, Germany

Weicun Zhang

Weicun Zhang is an associate professor at the School of Automation and Electrical Engineering, University of Science and Technology, in Beijing. He obtained his MSc degree in Automatic Control (1989) from the Beijing Institute of Technology and the PhD in Control Theory and Applications (1993) from Tsinghua University, P. R. China. From 1997 to 1998, he was a visiting research fellow in the Industrial and Operations Engineering Department, University of Michigan at Ann Arbor, and from 2006 to 2007 was a visiting professor in the Department of Electrical and Computer Engineering, Seoul National University, South Korea. His research interests include self-tuning adaptive control, multiple model adaptive control, and estimation for both linear and nonlinear dynamic systems. As representative research work, he established the Virtual Equivalent System (VES) theory for unified analysis of self-tuning control systems and other model based complex control systems. Prof. Zhang is the Editor-In-Chief for International Journal of Cybernetics and Cyber-Physical Systems.

Affiliations and expertise

Associate Professor, School of Automation and Electrical Engineering, University of Science and Technology, Beijing, China

Hamidreza Nemati

Hamidreza Nemati is a lecturer in avionics and control at the Engineering Design and Mathematics Department of the University of the West of England (UWE), Bristol, in the UK. His work focuses on autonomous navigation and intelligent control of cyber-physical systems in harsh environments. He previously served as Research Associate at Lancaster University, Lancaster, UK, working on robotic mobility and navigation for decommissioning and demolishing processes in nuclear facilities funded by the Engineering and Physical Sciences Research Council (EPSRC) via National Centre for Nuclear Robotics (NCNR). Hamidreza is a founder of ‘Vickers Robotics & AI’ and is trying to display great curiosity and attempts to fit his diverse experience into a clear understanding of the world.

Affiliations and expertise

Lecturer, Avionics and Control, Engineering Design and Mathematics Department, University of the West of England (UWE), Bristol, UK

YuMing Zhang

YuMing Zhang is a Full Professor at the University of Kentucky, Lexington, Kentucky, in the USA. He received his BS and MS degrees in control major from Harbin Institute of Technology (HIT), China where he finished his PhD degree in welding major in 1990. He has over 200 Journal Papers and 12 US Patents, with his expertise including advanced sensing, control, learning/modelling, and intelligent robotic systems with application to real-time monitoring and control of innovative welding processes, human-robot collaborative systems, and intelligent welding robots. He is currently an Editor for the Journal of Manufacturing Processes. He is a Fellow of the American Welding Society (AWS), American Society of Mechanical Engineers (ASME), and Society of Manufacturing (SME).

Affiliations and expertise

Professor, University of Kentucky, Lexington, Kentucky, USA