INDUSTRIAL CONTROL SYSTEMS Mathematical and Statistical Models and Techniques 1st ediiton by Adedeji Badiru, Oye Ibidapo Obe, Babatunde Ayeni ISBN 1420075586 ‎ 978-1420075588
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Authors:Adedeji B. Badiru , Series:Mechanical engineering [99] , Tags:Computers; Security; Network Security; Technology & Engineering; Manufacturing , Author sort:Badiru, Adedeji B. , Languages:Languages:eng , Published:Published:Dec 2012 , Publisher:Momentum Press , Comments:Comments:This book is meant to help both the novice and expert in Information Technology (IT) security and industrial control systems (ICS) gain a better understanding of protecting ICSs from electronic threats. The term ICS was chosen as ICSs include Supervisory Control and Data Acquisition (SCADA), Distributed Control Systems (DCS), Programmable Logic Controllers (PLCs), Remote Terminal Units (RTUs), Intelligent Electronic Devices (IEDs), field controllers, sensors, and drives, emission controls, building controls including fire suppression, thermostats, and elevator controls, and meters including business and residential automated metering. For the purpose of this book, ICSs also include safety systems. The term electronic threats was chosen rather than cyber security because there are electronic threats to ICSs beyond traditional cyber threats. Additionally, the book is about protecting the mission of the ICS – a compromise of a computer that isn’t critical to the mission of the control system may be a cyber security event, but it is not of importance. The term protecting was chosen as this not a book on how to attack control systems. From a cyber perspective, they are very brittle and attacking them is not rocket science. On the other hand, protecting them while at the same time maintaining their mission can be rocket science. The term it takes a village can be applied to securing ICSs as Operations alone cannot do this. It takes a team of ICS expertise, IT security expertise, telecom knowledge, networking, ICS and IT vendor support, and most of all senior management support to make this work. I hope you find the book of interest. Respectfully, Joe
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ISBN 10: 1420075586
ISBN 13: 978-1420075588
Author: Adedeji Badiru, Oye Ibidapo Obe, Babatunde Ayeni
Issues such as logistics, the coordination of different teams, and automatic control of machinery become more difficult when dealing with large, complex projects. Yet all these activities have common elements and can be represented by mathematics. Linking theory to practice, Industrial Control Systems: Mathematical and Statistical Models and Techniques presents the mathematical foundation for building and implementing industrial control systems. The book contains mathematically rigorous models and techniques generally applicable to control systems with specific orientation toward industrial systems.
INDUSTRIAL CONTROL SYSTEMS Mathematical and Statistical Models and Techniques 1st Table of contents:
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Introduction to Industrial Control Systems
1.1. Overview of Industrial Control Systems
1.2. Applications in Manufacturing, Process Control, and Automation
1.3. Components of Control Systems: Sensors, Actuators, and Controllers
1.4. Mathematical Foundations of Control Theory
1.5. Statistical Techniques in Control Systems -
Chapter 1: Fundamentals of Control Systems
2.1. Basic Control System Concepts
2.2. Feedback and Open-Loop vs. Closed-Loop Systems
2.3. System Stability and Control
2.4. Control System Modeling
2.5. Laplace Transforms and Transfer Functions -
Chapter 2: Mathematical Models in Industrial Control
3.1. Differential Equations in Control Systems
3.2. Linear vs. Nonlinear Systems
3.3. State-Space Models
3.4. System Dynamics and Modeling Techniques
3.5. Control Design Using Mathematical Models -
Chapter 3: Statistical Methods in Control Systems
4.1. Introduction to Statistical Process Control (SPC)
4.2. Probability and Statistical Inference in Control Systems
4.3. Regression Analysis for System Identification
4.4. Time Series Analysis and Forecasting in Control
4.5. Statistical Process Control Charts -
Chapter 4: System Identification Techniques
5.1. Introduction to System Identification
5.2. Data Collection and Preprocessing for System Identification
5.3. Parametric vs. Non-Parametric Identification
5.4. Methods of Identifying Linear Systems
5.5. Use of Statistical Methods in Model Estimation -
Chapter 5: Stability Analysis in Control Systems
6.1. Stability Criteria in Control Theory
6.2. Routh-Hurwitz Criterion
6.3. Nyquist and Bode Plots
6.4. Lyapunov Stability Method
6.5. Application of Statistical Tools to Stability Analysis -
Chapter 6: Optimization Techniques in Industrial Control
7.1. Optimization Theory and Control Systems
7.2. Linear and Nonlinear Optimization Methods
7.3. Gradient Descent and Optimal Control
7.4. Optimization Algorithms in Industrial Processes
7.5. Statistical Methods for System Optimization -
Chapter 7: Advanced Control Techniques
8.1. Model Predictive Control (MPC)
8.2. Adaptive and Robust Control
8.3. Fuzzy Logic and Neural Networks in Control
8.4. Control of Multivariable Systems
8.5. Real-Time Control and Automation -
Chapter 8: Fault Detection and Diagnosis in Control Systems
9.1. Importance of Fault Detection
9.2. Methods of Fault Diagnosis
9.3. Statistical Process Control for Fault Detection
9.4. Reliability and Maintenance in Control Systems
9.5. Case Studies in Fault Diagnosis -
Chapter 9: Data-Driven and Machine Learning Techniques in Control Systems
10.1. Data-Driven Control Systems
10.2. Machine Learning Models for System Control
10.3. Neural Networks for Control System Design
10.4. Reinforcement Learning in Industrial Control
10.5. Integration of AI in Industrial Automation -
Chapter 10: Case Studies in Industrial Control Systems
11.1. Manufacturing and Production Line Control
11.2. Process Control in Chemical Plants
11.3. Robotics and Automated Systems
11.4. Smart Grid and Energy Control Systems
11.5. Real-World Applications of Control Systems -
Chapter 11: Future Trends in Industrial Control Systems
12.1. IoT and Industry 4.0 Integration in Control Systems
12.2. The Role of Big Data and Analytics
12.3. Cybersecurity in Control Systems
12.4. Sustainable Control and Energy-Efficient Systems
12.5. Challenges and Emerging Technologies -
Conclusion: The Evolving Landscape of Industrial Control Systems
13.1. Advances in Mathematical and Statistical Models
13.2. The Convergence of Control, Data Science, and Engineering
13.3. The Role of Control Systems in the Future of Automation
13.4. Closing Thoughts and Future Research Directions
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