Modeling, Identification, and Control for Cyber- Physical Systems Towards Industry 4.0
- 1st Edition - January 9, 2024
- Imprint: Academic Press
- 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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Request a sales quoteModeling, 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.
- Provides the new and cutting-edge research and development and a series of guidance procedures for potential applications from academic research to industrial R&D
- Focuses on the conception and implementation of intelligent algorithms
- Covers a wide spectrum of topics, including 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
Graduate students, researchers, engineers, and academics in fields related to automation, industrial standards, mechanical, electrical, or aero engineering, robotics, and mechatronics, Undergraduate students in engineering subjects
- 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
- Edition: 1
- Published: January 9, 2024
- Imprint: Academic Press
- No. of pages: 484
- Language: English
- Paperback ISBN: 9780323952071
- eBook ISBN: 9780323952088
PM
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, GermanyWZ
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, ChinaHN
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, UKYZ
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, USARead Modeling, Identification, and Control for Cyber- Physical Systems Towards Industry 4.0 on ScienceDirect