Modeling, identification, and control for cyberphysical systems towards Industry 4.0

This book focuses on the emerging methodologies and applications of modeling and control within the context of Industry 4.0. It explores key concepts such as the Internet of Things, smart manufacturing, and cyber-physical systems, emphasizing their roles in modern industrial processes. The book is e...

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Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Mercorelli, Paolo
Format:	eBook
Language:	English
Published:	London : Academic Press, 2024.
Subjects:	Cooperating objects (Computer systems) Industry 4.0. Objets coopérants (Systèmes informatiques) Industrie 4.0. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Front Cover
Modeling, Identification, and Control for Cyber-Physical Systems Towards Industry 4.0
Copyright
Dedication
Contents
Contributors
Biography
Paolo Mercorelli
Prof. Weicun Zhang
Hamidreza Nemati
YuMing Zhang
Preface
Objectives
1 Industry 4.0 more than a challenge in modeling, identification, and control for cyber-physical systems
1.1 Introduction
1.1.1 Background and challenging issues
1.1.2 Basic concepts of Industry 4.0
1.2 Theoretical background
1.2.1 Internet of Things and services
1.2.2 Smart manufacturing
1.2.3 Vertical integration and networked manufacturing system
1.2.4 Marginalization of the network center: towards horizontal integration
1.2.5 Cyber-physical system
1.2.6 Commercialization of CPS
1.2.7 Outcomes of application of CPS
1.3 Method and implementation
1.3.1 Research process
1.3.2 Findings and analysis
1.4 Conclusions
References
I Manufacturing as a challenge in Industry 4.0 process
2 Advanced ice-clamping control in the context of Industry 4.0
2.1 Introduction
2.2 Model
2.3 Advanced ice-camping structure
2.4 Measured results and performance evaluation
2.4.1 Performance comparison
2.5 Towards intelligent clamping
2.6 Towards Industry 4.0
2.7 Conclusions
Appendix
References
3 Temperature control in Peltier cells comparing sliding mode control and PID controllers
3.1 Introduction
3.1.1 Sliding mode controller
3.1.2 Multi-input multi-output control motivation
3.2 Sliding mode control law derivation
3.2.1 Simulation results of MIMO SM controller
3.3 Experimental validation
3.4 Controller extension
3.5 Controller comparison
3.5.1 PI controller and feedforward regulator
3.5.2 Conclusions and comparative measurements between PI and SM controllers
References.
4 A Digital Twin for part quality prediction and control in plastic injection molding
Funding
4.1 Introduction
4.1.1 Digital Twin
4.1.2 Challenges
4.1.3 Solution approach
4.2 Plastic injection molding
4.2.1 Process cycle and machine components
4.2.2 Machine setpoints and measured process variables
4.2.3 State of the art: process control in injection molding
4.3 Data acquisition and management
4.3.1 Machine and process values acquisition via OPC UA
4.3.2 In-line part quality data acquisition
4.4 Control-oriented modeling of final part quality
4.4.1 Final part quality prediction
4.4.2 Preliminaries
4.4.3 Internal dynamics quality model
4.4.4 External dynamics quality model
4.4.5 Static quality model
4.5 Case study: tamper-evident closure quality prediction
4.6 Conclusions and outlook
4.6.1 Conclusions
4.6.2 Outlook
References
II Motion control and autonomous robots as a challenge in Industry 4.0 process
5 SLAM algorithms for autonomous mobile robots
5.1 Introduction
5.2 SLAM classification
5.3 liDAR-SLAM
5.3.1 Kalman Filter
5.3.1.1 Weighting algorithm
5.3.2 Particle filter
5.3.3 Graph-based optimization
5.4 Visual SLAM algorithm
5.4.1 Feature-based method
5.4.2 Direct method
5.4.3 Scheme comparison
5.5 Conclusions
Appendix
References
6 Optimization of motion control smoothness based on Eband algorithm
6.1 Introduction
6.2 Eband algorithm implementation principle
6.3 Improved Eband algorithm
6.3.1 Curvature algorithm optimization
6.3.2 Velocity product factor denoising
6.3.3 Velocity interpolation
6.4 Experiment analysis
6.4.1 Experimental data set
6.4.2 Experimental software and hardware configuration
6.4.3 Experimental data comparison
6.4.3.1 Smooth curvature algorithm optimization.
6.4.3.2 Velocity product factor denoising
6.4.3.3 Local target point interpolation
6.5 Conclusions
References
7 Modeling a modular omnidirectional AGV developmental platform with integrated suspension and power-plant
7.1 Motivation for the use of omnidirectional AGVs
7.1.1 Justifying research: two wheel swerve drive system
7.1.2 Justifying research: conforming to SIL standards
7.1.3 Justifying research: intergeneration of a suspension system in swerve drive
7.2 Design of the novel two-wheel swerve drive AGV
7.3 Kinematics
7.3.1 Introduction to the drive philosophy
7.3.2 Forward kinematics &amp
drive unit considerations
7.3.3 Inverse kinematics of the drive units
7.3.4 Kinematics of the swerve drive AGV
References
8 Control system strategy of a modular omnidirectional AGV
8.1 Introduction
8.2 Test methodology
8.3 Results
8.3.1 Straight-line test
8.3.2 Strafe tests
8.3.3 Ackerman steering test
8.4 Combination test
8.5 Conclusions
8.5.1 Research conclusion
References
9 Mecanum wheel slip detection model implemented on velocity-controlled drives
9.1 Introduction
9.2 Hardware considerations
9.3 Slip mitigation controller
9.3.1 Slip detection
9.3.2 Slip mitigator
9.4 Test methodology
9.5 Discussion
9.6 Conclusions
References
10 Safety automotive sensors and actuators with end-to-end protection (E2E) in the context of AUTOSAR embedded applications
10.1 Introduction
10.2 Architecture of a car
10.2.1 Hardware overview
10.2.2 Safety overview
10.2.3 Software overview
10.3 Communication stack in AUTOSAR
10.4 End-to-end protection (E2E) in AUTOSAR embedded applications
10.4.1 End-to-end (E2E) communication protection in AUTOSAR
10.4.2 Migrating E2E communication protection library in hardware.
10.4.2.1 Hardware E2E module for basic sensors
10.4.2.2 Detailed design of hardware E2E module
10.4.3 Experimental results and discussions
10.5 Conclusions
References
III Motion and control of autonomous unmanned aerial systems as a challenge in Industry 4.0 process
11 Multibody simulations of distributed flight arrays for Industry 4.0 applications
11.1 Introduction
11.2 Aims, objectives, and scopes
11.3 Background research
11.3.1 Chassis
11.3.2 Thrust mechanism
11.3.3 Driving mechanism
11.3.4 Docking mechanism
11.3.5 Sensors
11.3.6 Previous usages of DFAs
11.3.7 Warehouse applications and material handling
11.3.8 Payload systems
11.3.9 Navigation
11.3.10 Perching
11.3.11 Conclusions of the literature review
11.4 Methodology
11.5 Methods
11.5.1 Payload simulation
11.5.2 Single module flight simulation
11.6 Data analysis
11.6.1 Single tether simulations
11.6.2 N-tether simulations
11.6.3 Payload configuration simulations
11.6.4 Single module flight simulation
11.7 Discussions, critical thinking, and reflections
11.8 Conclusions
11.9 Recommendations for future work
References
12 Recent advancements in multi-objective pigeon inspired optimization (MPIO) for autonomous unmanned aerial systems
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
13 U-model-based dynamic inversion control for quadrotor UAV systems
13.1 Introduction
13.2 Quadrotor dynamic model
13.3 U-model dynamic inversion-based control system design
13.3.1 Design of the universal controller Gcl
13.3.2 U-model state space realization
13.4 Simulation study
13.5 Conclusions
References.
14 Nonlinear control allocation applied on a QTR: the influence of the frequency variation
14.1 Introduction
14.2 Nonlinear aircraft modeling and control allocation
14.3 P-PID controllers
14.3.1 The fast control allocation technique
14.4 SITL scheme
14.5 Simulation results
14.5.1 Remarks about the control technique
14.5.2 Kinematics and dynamics control results
14.5.3 Three-dimensional results
14.6 Conclusions
Acknowledgment
References
IV Theoretical and methodological advancements in disturbance rejection and robust control
15 Active disturbance rejection control of systems with large uncertainties
15.1 Introduction
15.2 From PID to ADRC, MPC, and adaptive control
15.2.1 PID as model reference adaptive control
15.2.2 Brief introduction of ADRC
15.2.3 ADRC as reinforced PID control and adaptive control
15.3 Multiple model ADRC for systems with large uncertainties
15.3.1 Model set
15.3.2 Controller set
15.3.2.1 Tracking Differentiator (TD)
15.3.2.2 Extended State Observer (ESO)
15.3.2.3 State error feedback
15.3.3 Weighted control strategy
15.4 Simulation verification
15.5 Conclusions
References
16 Gain scheduling design based on active disturbance rejection control for thermal power plant under full operating conditions
16.1 Introduction
16.2 Problem formulation
16.2.1 Dynamic model of the coordinated control system
16.2.2 Control difficulties
16.3 Active disturbance rejection control
16.3.1 Brief principle of ADRC
16.3.2 Tuning method and stability region of ADRC
16.4 Gain scheduling design based on ADRC
16.4.1 Brief principle of DEB
16.4.2 Division of operating conditions and selection of scheduling parameter
16.4.3 Gain scheduling design based on ADRC under full operating conditions.