Industrial Control Systems E-Book
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INDUSTRIAL CONTROL SYSTEMS This volume serves as a comprehensive guide in the journey of industrial control systems with a multidisciplinary approach to the key engineering problems in the 21st century. The journey of the control system may be viewed from the control of steam engines to spacecraft, aeroplane missile control systems to networked control systems and cybersecurity controls. In terms of industrial control and application, the journey starts from the design of P-I-D controllers to fuzzy controllers, neuro-fuzzy controllers, backstepping controllers, sliding mode controllers, and event-triggered controls for networked control systems. Recently, control theory has spread its golden feathers in different fields of engineering by use of the splendid tool of the control system. In this era, the boom of the Internet of Things is at its maximum pace. Different biomedical applications also come under this umbrella and provide the easiest way to continuous monitoring. One of the prominent research areas of green energy and sustainable development in which control plays a vital role is load frequency controllers, control of solar thermal plants, an event-driven building energy management system, speed-sensorless voltage and frequency control in autonomous DFIG-based wind energy, Hazardous Energy Control Programs, and many more. This exciting new volume: * Offers a complete journey through industrial control systems * Is written for multidisciplinary students and veteran engineers alike * Benefits researchers from diverse disciplines with real-world applications
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
List of Contributors
Preface
Part 1: ADVANCED CONTROL TECHNIQUES
1 Introduction: Industrial Control System
1.1 Types of Industry
1.2 Historical Perspective in Terms of Control
1.3 Future of Industry
References
2 Industrial Boiler Safety Monitoring System
2.1 Introduction
2.2 Boiler Definition
2.3 Classification of Boiler
2.4 Proposed System
2.5 Hardware Components
2.6 Conclusion and Future Scope
References
3 Robust Control of Industrial Rotary System
3.1 Introduction
3.2 Controller Design
3.3 Problem Formulation
3.4 LMI Formulation for Robust Stabilization Criteria
3.5 Plant Model
3.6 Simulation Study
3.7 Processor in Loop (PIL) Simulation
3.8 Conclusion
References
4 Proctored Secure Face Lock System
4.1 Introduction
4.2 Background
4.3 Proctored Secure Face Lock System
4.4 Implementation of Proctored Face Lock System Using Python
4.5 Analysis and Discussion
4.6 Conclusion and Future Work
References
5 Advanced Adaptive Control of Nonlinear Plants
5.1 Introduction
5.2 Model Reference Adaptive Control
5.3 Dynamic Inversion
5.4 U-Model
5.5 Single Inverted Pendulum
5.6 Performance Analysis
5.7 Conclusion
References
6 Design and Performance Analysis of Multiobjective Optimization Using PSO and SVM for PSS Tuning in SMIB System
6.1 Introduction
6.2 Small Signal Stability Analysis of SMIB System
6.3 Real Time Simulation of SMIB
6.4 Application of Optimization Techniques
6.5 Real-Time Simulation of Single Machine System Using PSO-PSS
6.6 Conclusion
References
7 Modelling and Control of PMSM Drives
7.1 Introduction
7.2 A Proposed Technique for Modelling and Control
7.3 Results and Discussions
7.4 Conclusions
References
8 VI System for Power Management of DC Microgrid
8.1 Introduction
8.2 Related Work
8.3 Proposed System
8.4 Microgrid Power Management and Metering Software
8.5 Experimental Work and Results
8.6 Conclusion
References
Part 2: CONTROL STRATEGIES FOR PRACTICAL SYSTEMS
9 Execution of a Portable Fuzzy Controller for Speed Regulator Brushless DC Motors
9.1 Introduction
9.2 Related Works
9.3 Materials and Methods
9.4 Result and Argument
9.5 Conclusions
References
10 Fuzzy Fractional Order PID Controller Design for Single Link Robotic Arm Manipulator
10.1 Introduction
10.2 Fuzzy Logic Control
10.3 Fractional Order Proportional Integral Derivative (FOPID) Controller
10.4 Modelling of Robotic Manipulator
10.5 Proposed Design of Fuzzy Fractional-Order PID Controller
10.6 Simulation Study of Proposed FFOPID Controller
10.7 Conclusion
References
11 Prototype Development of an Electromagnetic Levitation System for Maglev Vehicle
11.1 Introduction
11.2 System Modelling and Fabrication
11.3 Feedback Sensing, Experimental Results, and Discussions
11.4 Conclusions
References
12 Design of SSA Tuned Cascaded TI-TID Controller for Load Frequency Control of Multi-Source Power System with Electric Vehicle
12.1 Introduction
12.2 Modelling of Studied MSIPS
12.3 Modelling of EV
12.4 Adopted Control Approach
12.5 Description of SSA
12.6 Simulation Results and Analysis
12.7 Conclusion
References
Appendix
13 Cyber Security Control Systems for Operational Technology
13.1 Introduction
13.2 Operational Technology Security Risk
13.3 Taxonomy of Security Vulnerabilities
13.4 Methodology
13.5 Style of Cyber Security
13.6 Avoidance of Threads in Operational Technology
13.7 Conclusion
References
About the Editors
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 3
Table 3.1 DC servomotor parameters.
Table 3.2 Equivalent electrical model parameters of rotary peristaltic pump.
Chapter 5
Table 5.1 Value of concern performance indices utilizing various adaptive cont...
Table 5.2 Updated values of parameters of PID.
Chapter 6
Table 6.1 Test machine parameters and system data.
Table 6.2 Parameters of PSS - optimization algorithms.
Table 6.3 Parameters of PSS - optimization algorithms.
Table 6.4 Eigen values for test system.
Chapter 7
Table 7.1 Model reduction and minimum cost function of drive model in fused do...
Table 7.2 Time-domain specifications of lower-order electric drive model apply...
Table 7.3 Performance metrics of lower-order electric drive system in combined...
Table 7.4 Values of controller gains and cost function for reduced-order elect...
Chapter 9
Table 9.1 AFOPC-based gate pulse sequence.
Chapter 10
Table 10.1 Types of FOPID controllers and their structures.
Table 10.2 Effects of fractional-order PID actions.
Table 10.3 List of linguistic variables for designing FIS.
Table 10.4 Characteristics of PID gains.
Table 10.5 Rule-base for determining
K
p
.
Table 10.6 Rule-base for determining
K
i
.
Table 10.7 Rule-base for determining
K
d
.
Table 10.8 Specification of single-link manipulator system.
Table 10.9 Optimized values of FOPID model parameters.
Table 10.10 Performance comparison of proposed controller.
Chapter 11
Table 11.1 Parameters of magnetic coil.
Table 11.2 Design specifications.
Chapter 12
Table 12.1 Optimized gain values of adopted algorithm of Scenario 1.
Table 12.2 Transient response and performance indices for distinct algorithms ...
Table 12.3 Optimized gain values of adopted controller of Scenario 1.
Table 12.4 Optimized gain values of proposed CTI-TID controller with and witho...
Table 12.5 Transient response and performance indices related to two-area MSIP...
Table 12.6 Sensitivity analysis of concerned two-areas MSIPS with EVs of Scena...
Chapter 13
Table 13.1 Cyber attacks 2020.
List of Illustrations
Chapter 2
Figure 2.1 Factors affecting safe operations of boilers.
Figure 2.2 Outline of proposed framework.
Figure 2.3 Transmitter section.
Figure 2.4 Receiver section.
Figure 2.5 Fundamental thermocouple.
Chapter 3
Figure 3.1 Steinbuch’s RRC model [11].
Figure 3.2 Singh’s RRC model [20].
Figure 3.3 Proposed FDRRC model.
Figure 3.4 Closed loop system block diagram.
Figure 3.5 Block diagram of DC servo motor.
Figure 3.6 Equivalent electrical model of peristaltic pump of flow centric app...
Figure 3.7 Disturbance sinusoids.
Figure 3.8 Tracking error convergence with FDRRC and RC for peristaltic pump.
Figure 3.9 Tracking error convergence with FDRRC and RC for DC servo motor.
Figure 3.10 Block diagram of processor in loop simulation of proposed controll...
Figure 3.11 Real time set of processor in loop simulation of proposed controll...
Chapter 4
Figure 4.1 Block diagram of lock system.
Figure 4.2 Raspberry Pi 3B+.
Figure 4.3 PI camera.
Figure 4.4 PIR sensor.
Figure 4.5 Block diagram of power supply.
Figure 4.6 Flow chart proctored face lock system.
Figure 4.7 PIR sensor activates by detecting obstacles and in turn switches on...
Figure 4.8 Intruder image shared with owner’s mail.
Chapter 5
Figure 5.1 Scheme of MRAC.
Figure 5.2 Scheme of dynamic inversion.
Figure 5.3 Scheme of U-model based design.
Figure 5.4 Simple inverted pendulum.
Figure 5.5 Tracking characteristics when γ = 0.1.
Figure 5.6 Tracking characteristics when γ = 1.
Figure 5.7 Tracking characteristics when γ = 2.
Figure 5.8 Scheme of MRAC employed with Lyapunov method.
Figure 5.9 Tracking characteristics when γ = 0.1.
Figure 5.10 Tracking characteristics when γ = 1.
Figure 5.11 Tracking characteristics when γ = 2.
Figure 5.12 Scheme of modified PID control.
Figure 5.13 Scheme of MRAC with PID.
Figure 5.14 Tracking characteristics when γ =0.1.
Figure 5.15 Tracking characteristics when γ =1.
Figure 5.16 Tracking characteristics when γ =2.
Figure 5.17 Scheme of MRAC employing feedback linearization.
Figure 5.18 Response using dynamic inversion.
Figure 5.19 Pole placement based controller.
Figure 5.20 Response of system by classical pole placement method.
Figure 5.21 Response of controller o/p by classical pole placement method.
Figure 5.22 U-model based pole placement controller.
Figure 5.23 System response using U-model based pole placement method.
Figure 5.24 Output of controller using U-model based pole placement method.
Figure 5.25 U-model based MRAC scheme.
Figure 5.26 Response of system using U-model based MRAC technique with MIT rul...
Figure 5.27 Output of controller using U-model based MRAC technique with MIT r...
Chapter 6
Figure 6.1 Power system feedback control.
Figure 6.2 Single machine - infinite bus.
Figure 6.3 Response for changes in T
mech
. (a) real time variation of Tmech; (b...
Figure 6.4 Machine response: excitation voltage raised by 10%: (a) 10% increas...
Figure 6.5 Three-phase short circuit for response of synchronous generator: (a...
Figure 6.6 Synchronous generator model’s simulation diagram.
Figure 6.7 (a) Real time variations of electrical torque and mechanical torque...
Figure 6.8 (a) V
ref
and V
t
variations. (b) Field voltage, rotor speed, and ang...
Figure 6.9 Convergence – SMIB system.
Figure 6.10 (a) Responses of machine variables - change in V
ref
for system wit...
Chapter 8
Figure 8.1 Conceptual model of smart microgrid.
Figure 8.2 Typical architecture of proposed virtual instrumentation metering i...
Figure 8.3 Circuit diagram of automatic battery charge controller.
Figure 8.4 Circuit diagram of 15V regulated DC supply voltage grid supply.
Figure 8.5 Interfacing of NI-MyRIO to microgrid hardware components.
Figure 8.6 Functional design model of microgrid power management and metering ...
Figure 8.7 Flowchart of microgrid multi-power controller for efficient power m...
Figure 8.8 Characteristics of 20W solar panel under no load for variations in ...
Figure 8.9 Microgrid control panel indicating vital parameters and operational...
Figure 8.10 Microgrid control panel indicating vital parameters and operationa...
Chapter 9
Figure 9.1 Block diagram for ANOC pulse width modulation based inverter.
Figure 9.2 Proposed block diagram for BLDC motor.
Figure 9.3 Inverter circuit diagram for BLDC Motor.
Figure 9.4 Adaptive fuzzy optimal power control (AFOPC) block.
Figure 9.5 Input
e
ω
membership functions of BLDC motor.
Figure 9.6 Output
I
*
membership function for BLDCM.
Figure 9.7 (a) No load; (b) full load.
Figure 9.8 Flow chart for proposed AFOPFC technique.
Figure 9.9 Simulink diagram for proposed system.
Figure 9.10 Output current of projected system.
Chapter 10
Figure 10.1 Servo motor armature circuit.
Figure 10.2 Single-link manipulator mechanism.
Figure 10.3 Structure of FIS and FOPID controller.
Figure 10.4 Membership functions plot for inputs of designed FIS.
Figure 10.5 Membership functions plot for outputs of designed FIS.
Figure 10.6 (a) Fuzzy IO surface for
K
p
, (b) Fuzzy IO surface for
K
i
, (c) Fuzz...
Figure 10.7 Fuzzy rule-viewer example.
Figure 10.8 Overall Fuzzy FOPID controller-in-loop structure.
Figure 10.9 Simulink block diagram for comparative performance evaluation stud...
Figure 10.10 Step response of different FFOPID controllers.
Figure 10.11 Evolution of error with time for different FFOPID controllers.
Figure 10.12 Comparative plot of control action.
Figure 10.13 PID gain variation plot for different FFOPID controllers.
Chapter 11
Figure 11.1 Mechanical dimensions of levitation system.
Figure 11.2 3D view of model.
Figure 11.3 AutoCAD drawing of fabricated aluminum made mechanical structure.
Figure 11.4 Photograph of proposed levitation system.
Figure 11.5 Full prototype of levitation system.
Figure 11.6 Applications of levitation.
Figure 11.7 Global scenario of maglev transport.
Figure 11.8 Speed of world’s fastest maglev vehicle.
Figure 11.9 Classifications of levitation.
Figure 11.10 Advantages and disadvantages of levitation.
Figure 11.11 High speed train.
Figure 11.12 Model of high speed train (a) details of carriage and rail, (b) m...
Figure 11.13 Different control strategies in levitation.
Figure 11.14 Force
air – gap
profile in FEM analysis.
Figure 11.15 Different approach in modeling of levitation.
Figure 11.16 Modeling in
x
and
z
directions.
Figure 11.17 Different parameters vs.
x
-direction at
z
= 2
mm.
Figure 11.18 Velocity and acceleration plots at
z
= 2
mm.
Figure 11.19 Different parameters vs.
x
-direction at
z
= 7
mm.
Figure 11.20 Velocity and acceleration plots at
z
= 7
mm.
Figure 11.21 Velocity and acceleration plots at
z
= 8
mm.
Figure 11.22 Different parameters vs.
x
-direction at
z
= 8
mm.
Figure 11.23 Different parameters vs.
z
-direction of flat plate levitation sys...
Figure 11.24 Circuit diagram of outer loop of controller.
Figure 11.25 Circuit diagram of inner loop of controller.
Figure 11.26 Driver circuit.
Figure 11.27 Comparative study of parameters at different operating air- gap.
Figure 11.28 Operating current at stable levitated position.
Figure 11.29 Photograph of practical set up.
Chapter 12
Figure 12.1 Configuration of studied two-area MSIPS.
Figure 12.2 Configuration of lumped EVs model.
Figure 12.3 State and transitions of EVs.
Figure 12.4 Configuration of control system of EVs.
Figure 12.5 Stored energy model of one LC center.
Figure 12.6 Configuration of PID controller.
Figure 12.7 Configuration of cascade controller.
Figure 12.8 Configuration of CPI-TD controller.
Figure 12.9 Configuration of CTI-TID controller.
Figure 12.10 Comparative transient response profiles of two-area MSIPS obtaine...
Figure 12.11 Comparative transient response profiles of two-area MSIPS obtaine...
Figure 12.12 Comparative transient response profiles using proposed controller...
Figure 12.13 Transient response profiles to the ±50% variation in nominal load...
Figure 12.14 Transient response profiles to the ±50% variation in
T
12
of Scena...
Figure 12.15 Transient response profiles to the ±50% variation in
T
W
of Scenar...
Figure 12.16 Transient response profiles to the ±50% variation in
T
GT
of Scena...
Chapter 13
Figure 13.1 Prevention is not enough based on cyber security.
Figure 13.2 Breach detection.
Figure 13.3 Distributed denial of services [DDoS] attacks. DDoS attack aims to...
Figure 13.4 Advanced malware protection [AMP] threat grid.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
List of Contributors
Preface
Begin Reading
About the Editors
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Industrial Control Systems
Edited by
Vipin Chandra Pal
Suman Lata Tripathi
and
Souvik Ganguli
This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 9781119829256
Front cover images supplied by Pixabay.comCover design by Russell Richardson
List of Contributors
Nitendra TiwariDepartment of Electronics & Instrumentation Engineering,National Institute of Technology Silchar,Assam, India
Subhajit BhattacharyaDepartment of Electronics & Instrumentation Engineering,National Institute of Technology Silchar,Assam, India
Vipin Chandra PalDepartment of Electronics & Instrumentation Engineering,National Institute of Technology Silchar,Assam, India
Sudipta ChakrabortyDepartment of Electronics & Instrumentation Engineering,National Institute of Technology Silchar,Assam, India
Sheetla PrasadDepartment of Electrical, Electronics & Communication Engineering,Galgotia University, Greater Noida - Uttar Pradesh, India
G. Boopathi RajaDepartment of ECE,Velalar College of Engineering and Technology, Erode, Tamil Nadu, India
Naiwrita DeyDepartment of ECE,RCC Institute of Information Technology, Kolkata, West Bengal Kolkata,India
Ujjwal MondalDepartment of Applied Physics,Calcutta University, Kolkata, India
Anindita SenguptaDepartment of EE,IIEST, Shibpur, India
M. KalpanaDepartment of ECE,Kalasalingam Academy of Research and Education, Krishnankovil,Virudhunagar (Dt), Tamil Nadu, India
Krishnapriya J.Department of ECE,Kalasalingam Academy of Research and Education, Krishnankovil,Virudhunagar (Dt), Tamil Nadu, India
Sathya Pradeep C.Department of ECE,Kalasalingam Academy of Research and Education, Krishnankovil,Virudhunagar (Dt), Tamil Nadu, India
Gayatri P.Department of ECE,Kalasalingam Academy of Research and Education, Krishnankovil,Virudhunagar (Dt), Tamil Nadu, India
Santanu MallickDepartment of Applied Electronics & Instrumentation Engineering,Bankura Unnayani Institute of Engineering, Bankura, India
Ujjwal MondalInstrumentation Engineering, Department of Applied Physics,University of Calcutta, Kolkata, India
R. RamyaSRM Institute of Science and Technology, Kattankulathur, Chennai, India
M.V. SuganyadeviSaranathan College of Engineering, Trichy, India
S. UshaSRM Institute of Science and Technology, Kattankulathur, Chennai, India
Souvik GanguliDepartment of Electrical and Instrumentation Engineering,Thapar Institute of Engineering and Technology, Patiala, Punjab, India
Yashinidhi SrivastvaDepartment of Electrical and Instrumentation Engineering,Thapar Institute of Engineering and Technology, Patiala, Punjab, India
Gagandeep KaurDepartment of Electrical and Instrumentation Engineering,Thapar Institute of Engineering and Technology, Patiala, Punjab, India
Prasanta SarkarElectrical Engineering Department,National Institute of Technical Teachers’ Training & Research, Kolkata,West Bengal, India
Roop PahujaDept. of Instrumentation & Control Engineering,Dr. B R Ambedkar National Institute of Technology, Jalandhar (Punjab)India
Ranveer SinghDept. of Instrumentation & Control Engineering,Dr. B R Ambedkar National Institute of Technology, Jalandhar (Punjab)India
D. Stalin DavidDepartment of CSE,IFET College of Engineering, Villupuram, Tamil Nadu, India
Sayan DasDepartment of AEIE,RCC Institute of Information Technology, Kolkata, West Bengal, India
Naiwrita DeyDepartment of ECE,RCC Institute of Information Technology, Kolkata, West Bengal, India
J. KunduDepartment of Electrical Engineering, UCET, Bikaner, Rajasthan, India
A. ChoumalDepartment of Electrical Engineering, UCET, Bikaner, Rajasthan, India
Sandhya KumariDepartment of Electrical Engineering, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India
Gauri ShankarDepartment of Electrical Engineering, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India
S. SriramDept. of Computer Engineering,Sri Ramakrishna Polytechnic. College, Coimbatore, Tamil Nadu, India
Preface
Human civilization started by controlling natural parameters, like fire for making food, water for irrigation to produce grains, creating housing and other products out of wood, etc. Products have turned into a daily need for the people. Controlling input parameters leads to the advancement of manufacturing and other industries. Therefore, advancement of control theory is always a primary focus of research in the development of industry. In view of modern industries, the journey of industrial control systems may be viewed from the control of steam engine to spacecraft, airplane missile control systems to networked control systems, and cybersecurity controls in the new emerging field of robotics. More specifically, in terms of controllers, the journey of the industrial revolution started from the design of P-I-D controllers, fuzzy controllers, neuro-fuzzy controllers, back stepping controllers, sliding mode controllers, and event-triggered controls for networked control systems. Recently, control theory has spread its golden wings in different fields of engineering by use of splendid tools of the control system. In this era, the boom of Internet of Things is exploding at a maximum pace. Different biomedical applications also come under this umbrella and provide the easiest way for continuous monitoring. Some of the prominent research areas in green energy and sustainable development in which control is playing a vital role is as a load frequency controller to control solar thermo plants, event-driven building energy management systems, speed-sensorless voltage and frequency control in autonomous DFIG-based wind energy, hazardous energy control programs, and many more. This book aims to cover all of these subjects and more, in our push to further the field of industrial control systems. We hope this is useful for engineers, scientists, industry professionals, students, and faculty at all levels.
Organization of Book
This book is composed of two parts which contain 13 chapters. Part 1 begins with an introduction to different control techniques which are used in the industrial revolution for making modernization in industry. Chapter 1 describes the history of industry and also classifies the different categories of industries. The primary concerns of working in industry are safety and precautions. The safety monitoring and control of an industrial boiler is explained in Chapter 2 for preventing major accidents in the plant. Chapter 3 reviews the existing literature in controller design for robust control of rotary machineries involved in many industrial applications which are often subjected to position dependent disturbances. A modified robust approach of finite dimensional repetitive controller design and its synthesis is proposed here. After safety, security is a major concern of many precious machinery and other equipment which are used in real time application in Chapter 4. Chapter 5 proposes about advanced adaptive control applied to nonlinear plants. In Chapter 6, the discussion of optimization algorithm PSO and SVM is included and it is analyzed that they are responsible for identifying the appropriate controls for various operating conditions. Two popular algorithms, viz. the firefly technique abbreviated as FA and the pattern search (PS) technique, are combined to develop a new topology termed the hybrid firefly algorithm, designated in Chapter 7 as the FAPS method. Motivated to re-design some of the essential subsystems of a DC micro-grid for efficient power management in a more informative and user-friendly manner, Chapter 8 represents a virtual instrumentation metering infrastructure and a modern ICS (Industrial Control System) for the same.
Part 2 starts with the real time implementation of different control strategies to practical systems.
Chapter 9 proposes an Adaptive Fuzzy Optimal Power Controller (AFOPC) for speed regulation of Brushless Direct Current Motors (BLDCM) and by using this technique, it is feasible to achieve the lowest possible losses while maintaining the highest possible motor speed. New industrial development is going into new dimensions by including robots for different manufacturing industries where humans are not able to work. In Chapter 10, a Fuzzy logic based partially-optimized FOPID controller is developed mainly focusing on controlling robot manipulators. Chapter 11 represents a brief study, analysis, validation, and control of an electromagnetic attraction type levitation set-up where a flat plate has been successfully levitated at a desired operating point. The final product of any industry is used by consumers worldwide and petrol/diesel based transport is a common facility for receiving items by customers. To protect the environment from pollution, the automobile industry is shifting towards the electric vehicle. In Chapter 12, two-area MSIPS with nonlinearities is attempted to examine the effectiveness of a proposed SSA tuned CTI-TID controller on electric vehicles. The modern era is the transfer of information through a single click via different networks. Therefore, security and control are very important and from here the new emerging field of Cyber Security Control Systems is gaining prime importance. In Chapter 13, cyber security control systems for operational technology have been discussed.
Part 1ADVANCED CONTROL TECHNIQUES
1Introduction: Industrial Control System
Nitendra Tiwari1*, Subhajit Bhattacharya1, Vipin Chandra Pal1, Sudipta Chakraborty1 and Sheetla Prasad2
1Department of Electronics and Instrumentation Engineering, National Institute of Technology Silchar, Assam, India
2Department of Electrical Engineering, Galgotias University, Noida, Uttar Pradesh Punjab, India
Abstract
In this chapter, the meaning of industry and the types have been discussed. The history of industry and their way of working are described to focus on the beginning of industry. Finally, the future of industries has been discussed with consideration to the new recent technology in different fields like communication, networking, etc.
Keywords: Industry, revolution of industry, classification of industries
An industry is a collection of firms that have common basic business operations. In today’s economy, there are hundreds of industry categories. Typically, industrial groupings are grouped together into larger groups called sectors.
Individual firms are classified into industries based on their principal sources of revenue. Despite the fact that a vehicle manufacturer’s finance sector may account for 10% of total sales, most categorization methodologies identify the firm as part of the automobile industry.
1.1 Types of Industry
On the basis of raw materials and their processing, industries can be divided as follows.
1.1.1 The Primary Sector
Forestry, agriculture, fishing, quarrying, mining, and mineral exploitation are all part of a country’s economy. It can be divided into two categories:
Genetic Industry
- This category comprises any raw material manufacturing that can benefit from human involvement in the development process.
Extractive Industries
- This category involves the development of finite raw materials that cannot be replenished by cultivation.
The primary industry continues to dominate the economies of underdeveloped and growing countries, but as the secondary and tertiary industries increase, its share of total economic output decreases.
1.1.2 The Secondary Sector
The manufacturing industry (secondary industry) processes the raw materials given by primary industries and converts them into consumer goods. Products that have been included into product lines by specialised secondary industries are subjected to additional procedures. These industries develop capital equipment for the production of both consumer and non-consumer goods. The industry is further subdivided into the following categories:
Large Scale Industry
– It necessitates considerable capital investment in plants and equipment, provides a varied variety of industries, such as other industrial sectors, has a complex industrial organisation that typically employs qualified experts, and generates a large volume of output. Examples include steel and iron production, petroleum refining, and other sectors.
Small Industry
– It is characterised by the non-durability of industrial goods and cheap cost of capital in plants and machinery, which may include nonstandard things like customised or design work [
1
]. Manufacturing of plastics, textiles, food processing, and other sectors are examples.
1.1.3 The Tertiary Sector
The tertiary industrial sector, sometimes known as the service industry, combines industries that provide services or generate revenue without producing tangible items. This sector is often a mix of government and private firms in free market and mixed economies.
Real estate services, banking and finance services, communication services, and so on are examples. An old, well-known anecdote on industrial control.
Throughout history, individuals have gathered to discuss and learn about strategies to exercise organisational control to claim moral authority and political power. Norms concerning effective conduct patterns have arisen through such exchanges and some of these norms have been codified in codes, principles, laws, adages, edicts, and maxims, or the discursive artefacts that people employ to exert political power and claim moral authority.
People have created stories tying situational facts to behavioural norms and institutional settings in order to explain how an organisation exercised control at a specific time and location. People’s assumptions about organisational control have evolved throughout time and these assumptions have shaped the stories they have told. This examines how organisational control has been viewed throughout history.
We will start with one of the few times that contemporary organisational scholars have directly addressed the content of this vast human legacy. Rindova and Starbuck (1997) go into detail about how the ancient Chinese viewed organisations and how they employed agency relationship conceptualizations to build different strategies to wield organisational control. Then, we will look at how the exercise of organisational control evolved over time in eighteenth-century Europe and then America [2]. Specifically, we show how industrial bureaucracies arose and how people often resisted the organisational limitations that came with them. We look at how this resistance sparked efforts to make organisational governance more democratic and sensitive to human needs.
1.2 Historical Perspective in Terms of Control
The factory was the first expression of the capitalist firm, which was created by capitalism. Historical events have indicated that important alterations in business organisation have coincides with industrial revolutions since their inception. The British Industrial Revolution (BIR) gave birth to the factory and the Second Industrial Revolution (SIR) gave birth to the massive contemporary business firm by the 1920s.
A multidivisional kind of business organisation is the name given to this type of firm structure (M-form). Another key transformation in business organisation is occurring as a result of today’s ICT revolution: “huge vertically integrated” firms are becoming flatter, decentralised, and organised in “semi-autonomous project-based teams”. This revolutionary business organisation is commonly referred to as a project-based firm revolution in the literature [3–7].
What is driving these huge organisational changes in businesses? Do these changes put the firm’s core values in jeopardy? These are clearly questions about the evolution of a firm and they are questions that are not addressed in post-coasean conceptions of the firm (e.g., Williamson, 1985 [13]). Nonetheless, there is little doubt that studying a firm’s growth can aid in gaining a deeper knowledge of it.
In the context of the co-evolution of social and physical technologies, a historical study of the firm’s evolution will be developed [14, 15].
The argument will be that the history of the company must be regarded as part of this co-evolutionary process, which has been mostly driven by big changes in physical technology, or macro-inventions in Mokyr (1990) [16] terms, as historical events reveal. The above three firm organisations must be regarded as mutants according to the theory of the firm and the fundamental difference between them is connected to the shift in the mix of low and high-powered incentives (Williamson, 1985) applied within a given mutant-firm.
Human creation procedure also converts as an output of scientific enhancement. The industrial insurrection refers to a shift in production technology which is vastly unlike its old cohort. People’s works circumstances and lives were dramatically altered by new industrial technologies. What was the industrial insurrection like and where do we stand? “From the First Industrial Revolution to Industry 4.0” is the title of a new book.
1.2.1 First Industrial Revolution
The utilisation in the eighteenth century of mechanisation and steam power in production ushered in the 1st Industrial Revolution [3]. The automated version, which previously employed simple revolving wheels to generate threads, now produces 8 times the total in the same amount of time. Steam’s power was also well acknowledged. The harnessing of human output for industrial goals was the most significant step forward in increasing human productivity. The steam engine could be utilised to power weaving looms instead of muscle power. Next, an enormous swap occurred as an output of enhancement such as the steamship and the steam-powered locomotive that allows passengers and things to move a great displacement in minimum time.
1.2.2 Second Industrial Revolution
The search for electricity and congregation line production announced the 2nd Industrial insurrection in the 19th century. Henry Ford took these concepts and applied them to the production of automobiles, remarkably changing the factories. Initially, a complete automobile was developing at only a station, but now automobiles were produced in parts on a conveyor belt, which is rapid and more economic.
The Second Industrial Revolution began in the nineteenth century with the creation of electricity and the introduction of construction lines.
At a butchery in Chicago where pigs were stretched on conveyor belts and every butcher only accomplished half of the task, Henry Ford (1863-1947) developed the concept for mass production. Henry Ford took these ideas and applied them to the automobile industry, drastically altering it in the process. Previously, a whole vehicle was built at a single station, but now, cars are built in stages on a conveyor belt, which is significantly accelerated and less expensive.
1.2.3 Third Industrial Revolution
The foundation of “memory-programmable controllers and computers to partially robotize activity kicked off the Third Industrial Revolution in the 1970s of the twentieth century”. We may now robotize a whole industrial activity without the requirement for personal involvement in regards to the adaptation of these modifications. This is exemplified by robots that follow pre-defined commands without requiring personal intervention. In the 1970s, the use of memory-programmable controllers and computers ushered in the Third Industrial Revolution. In regards to technological advancements, we can now automate an entire production process without the requirement for human participation.
1.2.4 Fourth Industrial Revolution
During the 1st Industrial Revolt, water and steam were used to mechanise industries and thee Secondary used electric power to accomplish mass production. The Third used electronics and computer technology to automate production. A Fourth Industrial Revolution is presently being developed upon the Third Industrial Revolution, the digital revolution. It is explained by a merging of technology that makes the boundaries between the biological, digital, and physical realms.
Currently, the 4th Industrial Revolution is underway. The application of data and communication technologies in the manufacturing business is referred to as “Business 4.0.” It is built on the accomplishments of the 3rd Industrial Revolt. A network link powered computer-based manufacturing system, which has a digital counterpart on the Internet in certain aspects can communicate with another system and get data about each other as a result of this. This is a step forward in the process of manufacturing robotization.
All systems are linked, resulting in “cyber-physical production systems” and sophisticated businesses including creation systems, goods, and people. All systems are linked and the outcomes in “cyber-physical production systems” and smart industries, in which manufacturing equipment, and people interact in a network and production is almost independent.
When these authorizations are combined, “Industry 4.0” has the ability to significantly enhance manufacturing settings. Self-organizing logistics include machines that can discover issues and inform maintenance operations on their own or self-organizing management that adapts to unplanned moves in production.
It also has the capability to influence people’s work routine. People may be drawn to smarter networks as an outcome of Industry 4.0 that might lead to more efficient work.
The 4th Industrial Revolt, like its predecessors, has the ability to raise world income levels and modify people’s lives all throughout the globe. Consumers who can bear and get permission to the digital world have reaped the biggest benefits thus far, as technology has allowed new items and services that increase the enjoyment and efficiency of our lives. Any of these operations, such as hailing a cab, booking a ticket, buying a product, watching a movie, or playing a game, may now be completed remotely. Technological developments will provide a supply-side miracle in the future, resulting in long-term increases in efficiency and production. Communication and transportation and costs will fall, while arrangement and world supply chains will enhance and trade costs will fall, opening up new markets and propelling economic development.
1.3 Future of Industry
1.3.1 Edge Computing
The usage and collection of data distinguishes the third and fourth industrial revolutions. We need technology that allows us to use production information more quickly at any time, regardless of location, in an increasingly competitive and international environment.
Edge computing facilitates smooth management of massive amounts of data by allowing information to be processed at the network’s edge, near to the machine that generates the data, lowering processing latency, for example in [6, 7].
Furthermore, because this data has already been processed, the data centre receives lesser volumes, resulting in bandwidth savings.
1.3.2 Additive Manufacturing
3D printers that use new materials will be able to create the unthinkable and will transform industrial processes and systems. This will result in significant cost savings when creating prototypes or small batches [9].
It is now possible to print any element in a variety of shapes and sizes in a completely personalised manner using a variety of materials, as well as to make models and functional parts in small series at extremely low rates, thanks to 3D printing.
1.3.3 5G
5G can improve automation and information transfer speed in the manufacturing process. This will improve the performance of autonomous machines by utilising 5G’s “Network Slicing” capability, which allows the network to be divided into subnets.
Network Slicing will tailor connectivity to individual requirements and circumstances. More network resources will be allocated to the machines thanks to the combination of 5G and industrial automation. The full potential of 5G will then be available to the industry’s automated sector [11, 12].
Wireless communication speeds are 10 times quicker than earlier creations, rivalling fiber-optic cable speeds, and 5G is the basic mobile technology able to provide broadband wireless technology to attached devices. By 2025, it is predicted that there will be 25 billion linked Internet of Things (IOT) devices. The competition to make 5G commercially available in every country is heating up. When it occurs, the way we communicate— and live—will shift dramatically.
IOT, driverless vehicles, perverted reality, smart towns, mission-complicated manufacturing, 3D videos, remote healthcare, and regenerative medicine are all expected to benefit from 5G [11]. These and other 5G-enabled applications have the potential to revolutionise the legal, risk, and regulatory landscapes, ushering in the Fourth Industrial Revolution as a whole. The Fourth Industrial Revolution will combine sophisticated technologies with high-speed wireless communication to blur the barriers between the biological, digital, and physical, domains. Previous industrial revolutions harnessed the power of steam, electric, and information technology. The ramifications for our systems will change how we create, manage, and govern our planet and ourselves.
1.3.4 Artificial Intelligence
The framework of Industry 4.0 cannot be restricted solely to the above-mentioned new technologies.
Artificial intelligence (AI) is at the heart of the factory’s internal transformation. AI enables machines and robots to interact with one another and, more importantly, to learn from the numerous situations that arise during the manufacturing processes, resulting in higher productivity and lower costs for enterprises.
However, in the automotive business, artificial intelligence will result in the exponential expansion of driverless systems, which would have a detrimental impact on the industry [10].
As the hype surrounding AI has risen, dealers have been attempting to demonstrate how AI is applied in their goods and services. For example, machine learning is usually referred to as AI. AI demands the use of specialised software and hardware for building and training machine learning algorithms. Although there is no one programming language that is equivalent, along with AI, Python and Java are two common alternatives [7].
Generally, AI operates by absorbing massive groups of labelled training data, analysing the data for relations and designs, and then using these designs to anticipate further states. A chatbot given text chat samples may learn to have lifelike conversations with persons by evaluating millions of cases, while an image recognition computer may learn to identify and describe things in images by examining millions of instances [9].
Self-correction, reasoning, and learning, are the 3 cognitive activities that AI programming targets.
Many academics began to worry that the symbolic method would be able to replicate all of human cognition’s operations, including vision, robotics, learning, and pattern recognition. Several academics began investigating “sub-symbolic” methods to certain AI challenges [3]. Rodney Brooks, a robotics researcher, eschewed symbolic AI in favour of targeting on the basic technical issues that allow robots to survive and learn about their atmosphere. In the mid-1980s, Geoffrey Hinton, David Rumelhart, and others resurrected interest in neural networks and “connectionism.” Fuzzy systems, Grey system, Neural networks, theory, revolt computation, and many more methods derived from mathematical or statistics optimization were created in the 1980s.
In the late 1990s and early 2000s, AI progressively regained its reputation by identifying and solving particular challenges. Researchers were able to create reliable results, use more mathematical approaches, and work with people from different professions because of the focused emphasis [4]. AI researchers’ solutions were extensively employed by 2000, despite the fact that they were rarely referred to as “artificial intelligence” in the 1990s.
Advances in machine learning and perception were aided by algorithmic, faster computer improvements, and availability to vast quantities of data. Around 2012, data-hungry extreme learning algorithms began to influence accuracy standards. According to Bloomberg’s Jack Clark, 2015 marked a watershed move for artificial intelligence, with the number of software projects using AI at Google increasing from “sporadic usage” in 2012 to over 2,700. This, he says, is attributable to a growth in inexpensive neural networks, as well as a rise in research tools and datasets, as well as a rise in cloud computing infrastructure. According to a 2017 poll, one out of every five businesses have “integrated AI in certain services or operations.” Between 2015 and 2019, the volume of AI research was enhanced by 50%.
Many academics began to worry that AI was no longer following its original objective of producing adaptable, fully sentient computers. Statistical AI, which is overwhelmingly used to tackle specific issues and includes extremely successful approaches like deep learning, is at the centre of much contemporary research. This worry gave rise to the artificial general intelligence (or “AGI”) discipline, which by 2010 included numerous well-funded organisations.
The processes of learning element of AI programming is connected with collecting data and creating rules for moving it to benefited knowledge. Algorithms are methods that provide computer equipment with one-by-one instructions for finalization of a certain work.
1.3.5 Cybersecurity
Let’s not forget that cybersecurity entails safeguarding data and systems against serious cyber threats including cyber terrorism and cyber espionage [8].
Maintaining cyber security strategy and operations in the face of ever-evolving cyber threats is a huge issue for private enterprises.
As more business activities are automated and a growing number of computers are utilised to store sensitive information, the need for secure computer systems becomes increasingly apparent. This requirement becomes more apparent when systems and applications become widespread and accessible via an unsecured network, such as the Internet. The Internet is used by governments, corporations, financial institutions, and millions of everyday customers. Computer networks support a vast range of functions that, if disrupted, can be disastrous and could productively destroy these businesses. As a outcome, cybersecurity challenges have evolved into national security issues. Keeping the Internet safe is a difficult task. Cybersecurity cannot be obtained via random seat-of-the-pants tactics; it requires deliberate development [8]. Using software engineering ability to tackle the issue is a practical start. Software developers, designers, developers, and administrators, on the other hand, must be aware of the risks and security problems that come with creating, developing, and implementing network-based software.
References
1. Vaidya, S., Ambad,P. Bhosle, S.,Industry 4.0 – A Glimpse, Procedia Manufacturing, 20, 233–238, 2018.
2. Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries, Boston Consulting Group, 1-14, 2015.
3. Field C., Internet-Future Communication within the Industry, IEEE Transactions on Industry Applications, 33(2), 397-401, 1997.
4. Mehta, B. S., Awasthi, I C, Industry 4.0 and Future of Work in India, FIIB Business Review, 8(1) 9–16, 2019.
5. Peneder M., Industry Classification: Aim, Scope and Techniques, Journal of Industry, Competition and Trade, 3:1/2, 109-129, 2003.
