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Controller Design for Industrial Applications

Arindam Mondal & Souvik Ganguli
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394287079  |  Hardcover  |  
400 pages
Price: $225 USD
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One Line Description
Controller Design for Industrial Applications is essential for anyone looking to master the advanced techniques of intelligent controller design, enabling you to effectively tackle the complexities of modern industrial processes and optimize performance in an ever-evolving landscape.

Audience
Undergraduate, postgraduate, and research students and faculty, researchers, policymakers, and industry professionals working with controllers in the industrial field

Description
Industrial processes are often complex and dynamic, making it challenging to design controllers that can maintain stable and optimal operation. Traditional controllers, such as PID controllers, have been widely used in industrial applications but have limitations in handling non-linear and uncertain systems. Intelligent controllers offer an alternative solution that can adapt to changing system dynamics and disturbances. The use of intelligent controllers in industrial applications has gained increasing attention in recent years, with numerous successful implementations in various fields, such as process control, robotics control, HVAC control, power systems control, and autonomous vehicle control. However, the design and implementation of intelligent controllers require careful consideration of hardware and software requirements, as well as simulation and testing procedures to ensure reliable and safe operation.
In the rapidly evolving industrial landscape, it is essential to develop advanced control techniques to enhance productivity, minimize costs, and ensure safety. Traditional control methods often struggle to handle complex systems and unpredictable environments. However, with the emergence of intelligent control techniques, there is a great opportunity to improve industrial automation and control systems. Controller Design for Industrial Applications aims to provide a comprehensive understanding of intelligent controller design for industrial applications, from theoretical concepts to practical implementation. It will cover the fundamental concepts of intelligent control theory and techniques, their application in various industrial fields, and practical implementation and design considerations.

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Author / Editor Details
Arindam Mondal, PhD is a professor of Electrical Engineering at Dr. B C Roy Engineering College, Durgapur. He is a co-principal investigator for a Technology Development Program project under the Indian Department of Science and Technology. He has published 48 research papers in reputed international journals, conferences, and book chapters and has 35 patents published in his credit. His research interests include digital controller design, system identification, fractional order control and signal processing, Internet of Things load frequency control, and machine learning.

Souvik Ganguli, PhD is an assistant professor at the Thapar Institute of Engineering and Technology, Patiala. He has published 17 papers in international journals, 36 SCOPUS-indexed papers, book chapters, and conference papers, and has been granted nine Indian patents, four German patents, and two South African patents. His research interests include model order reduction, identification and control, nature-inspired metaheuristic algorithms, electronic devices, and renewable energy applications.

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Table of Contents
Preface
1. Fuzzy Logic Control for Industrial Applications
Srabanti Maji and Souvik Ganguli
1.1 Introduction
1.2 The Evaluation of Fuzzy Logic Control: From Theory to Industrial Applications
1.2.1 The Origin of Fuzzy Logic: A Paradigm Shift in Control Theory
1.2.2 Mamdani’s Fuzzy Logic Control: The First Practical Implementation
1.2.3 Takagi-Sugeno-Kang (TSK) Fuzzy Models: Advancing the Practicality of FLC
1.2.4 Hybrid and Adaptive Fuzzy Logic Control: Addressing Complexity and Uncertainty
1.2.4.1 Hybrid Fuzzy Logic Control
1.2.4.2 Adaptive Fuzzy Logic Control
1.3 Basics of Fuzzy Logic Control: A Comprehensive Overview
1.3.1 Fuzzy Sets and Linguistic Variables
1.3.2 Membership Functions
1.3.3 Fuzzy Rule Base
1.3.4 Fuzzification
1.3.5 Rule Evaluation
1.3.6 Rule Aggregation
1.3.7 Defuzzification
1.3.8 Feedback and Iteration
1.3.9 Architecture of Fuzzy Logic Controller
1.4 Merits of Fuzzy Logic Control
1.4.1 Ability to Handle Imprecise and Uncertain Information
1.4.2 Linguistic Representation and Human-Like Decision-Making
1.4.3 Adaptive and Self-Learning Capabilities
1.4.4 Nonlinear and Complex System Control
1.4.5 Robustness and Fault Tolerance
1.4.6 Intuitive Rule–Based Design
1.4.7 Reduced Development and Implementation Time
1.4.8 Integration with Conventional Control Methods
1.5 Industrial Applications of Fuzzy Logic Control
1.5.1 Automotive Industry
1.5.2 Consumer Electronics
1.5.3 Robotics
1.5.4 Industrial Process Control
1.5.5 Power Systems
1.5.6 Traffic Control Systems
1.5.7 Medical Systems
1.5.8 Agriculture
1.5.9 Aerospace and Defense
1.5.10 Financial Systems
1.6 Discussions and Future Scope of Work
1.7 Conclusions
References
2. Artificial Neural Network for Industrial Applications
Biswajit Saha and Gour Sundar Mitra Thakur
2.1 Introduction
2.2 Neural Network Models for Industrial Applications
2.3 Challenges and Limitations
2.4 Industry Use Cases and a Sample Case Study
2.5 Future Directions and Emerging Trends
2.6 Conclusion
References
3. Artificial Neural Network–Based Sliding Mode Controller for a Class of Nonlinear System
Sheetla Prasad, Rammurti Meena and Vipin Chandra Pal
3.1 Introduction
3.2 Problem Formulation
3.3 Artificial Neural Network Structure
3.4 Neural Network Observer–Based SMC
3.5 Simulation and Demonstrations
3.6 Conclusion
References
4. Finite Control Set Model Predictive Control for Permanent Magnet Synchronous Motor Drives
Ravi Eswar Kodumur Meesala, Phani Teja Bankupalli and Chinta Praveen Kumar
4.1 Introduction
4.1.1 Literature Survey on EV Motors
4.1.2 Literature Survey on Motor Control
4.1.2.1 FOC Operation
4.1.2.2 DTC Operation
4.1.2.3 MPC Operation
4.2 Mathematical Model of PMSM Drive
4.2.1 Dynamic Mathematical Model of PMSM
4.2.2 Mathematical Model of Two-Level VSI
4.3 Optimal Control Concepts of Finite Control Set Model Predictive Control (FCSMPC)
4.3.1 PTC Operating Principle
4.3.1.1 Measurements and Estimation
4.3.1.2 Stator Current Measurement
4.3.1.3 Stator Flux (Ψs) Estimation
4.3.1.4 Predictions
4.3.1.5 Cost Function Assessment
4.3.2 PCC Operating Principle
4.3.3 Simulation Analysis
4.4 Constraints in FCSMPC Operation
4.4.1 Weighting Factors Calculation
4.4.2 Steady-State Error Issues
4.4.3 Long Prediction Horizon
4.5 Real-Time Implementation of FCSMPC for PMSM Drive
Acknowledgements
References
5. Kinematic and Dynamic Modeling of Robots
Suman Lata Tripathi and Deepika Ghai
5.1 Introduction
5.2 Required Block for Robotic Model
5.3 Robotic Arm Model
5.4 Tool Description
5.5 Methodology/Design Steps
5.6 Result and Discussion
5.7 Applications and Future Scope
5.8 Conclusion
References
6. Design of FUZZY-(1+PD)‑FOPID Controller for Hybrid Two‑Area Power System
Susmit Chakraborty and Arindam Mondal
6.1 Introduction
6.2 Plant Model
6.3 Controller Design
6.3.1 Fuzzy Logic Controller (FLC)
6.3.1.1 Fuzzy Sets and Fuzzy Logic
6.3.2 (1+PD) Controller Unit
6.3.3 Fractional-Order PID (FOPID) Controller
6.4 Tree-Seed Algorithm
6.5 Result and Analysis
6.6 Conclusion
Appendix
References
7. Design of MPC-TSA Controller for Hybrid Two-Area Power System
Susmit Chakraborty and Arindam Mondal
7.1 Introduction
7.2 Plant Model
7.3 Model Predictive Controller
7.3.1 Model System
7.3.2 Disorder Model
7.3.3 Predicting Model
7.3.4 MPC on Load-Frequency-Control (MP-LFC)
7.4 Tree-Seed Algorithm
7.5 Result and Analysis
7.6 Conclusion
Appendix
References
8. Wide-Area Monitoring, Protection, Automation and Control (WAMPAC) System
Sanchita Kumari and Amrita Sinha
8.1 Introduction
8.2 Blackouts
8.3 Supervisory Control and Data Acquisition System
8.3.1 Components of SCADA in Power System
8.4 Phasor Measurement Units (PMU)
8.4.1 Block Diagram of PMU
8.4.2 Synchrophasor Technology
8.4.3 Phasor Estimation Techniques
8.4.3.1 Discrete Fourier Transform (DFT)
8.4.3.2 Taylor Weighted Least Square Technique
8.4.3.3 Phase-Locked Loop (PLL) Technique
8.4.3.4 Kalman Filter (KM)–Based Technique
8.5 Intelligent Electronic Devices (IEDs)
8.6 Communication Protocols
8.6.1 IEC 61850
8.6.2 Distributed Network Protocol 3 (DNP3)
8.6.3 MODBUS
8.6.4 Inter-Control Center Communications Protocol (ICCP)
8.6.5 C37.118
8.6.6 IEC 60870-5-101 and IEC 60870-5-104
8.6.7 IEC 61400-25
8.7 Conclusions
References
9. An Efficient Smart Prepaid Interface Design for Power Industries
Antara Kundu, Harsh Kumar Shaw and Maitrayee Chakrabarty
9.1 Introduction
9.2 Literature Survey
9.3 Proposed System
9.3.1 Methodology
9.3.2 Implementation
9.4 Industrial Designing Application for Power Systems Control
9.5 Conclusion
References
10. PV System Maximum Power Point Tracking Under Partial Shadowing Using Gray Wolf Optimization Algorithm
Snehashis Ghoshal and Arindam Mondal
10.1 Introduction
10.2 Analysis and Modeling of Solar PV Systems
10.3 Impact of Partial Shading in a PV System
10.4 Performance Optimization of Solar Panel During Partial Shading Condition
10.5 Significance of MPPT in Partial Shading Conditions
10.6 MPPT Techniques for Partially Shaded Scenario
10.6.1 Perturb and Observe (P&O) Method
10.6.2 Gray Wolf Optimization (GWO) Method
10.7 Simulation Result and Analysis
10.8 Conclusion
References
11. An Efficient Optimization Approach for Solving the Relay Coordination Problem
Maitrayee Chakrabarty, Sudipta Chakraborty, Suparna Pal and Raju Basak
11.1 Introduction
11.2 Literature Survey
11.3 Overview of the System
11.4 Problem Formulation and Constraint Criterion
11.4.1 Limitations on Operating Time
11.4.2 TDS Constraints
11.4.3 Coordination Limitation
11.4.4 Relay Characteristics
11.5 Proposed PSO Algorithm
11.5.1 What Makes the Particle Swarm Optimization (PSO) Algorithm So Helpful?
11.6 Simulation Outcomes and Discussions
11.6.1 Case I
11.6.2 Case II
11.7 Industrial Application Design Consideration
11.8 Conclusion
References
12. Intelligent Control for Energy-Efficient HVAC System Modeling and Control
R. Sanjeevi, J. Anuradha, Sandeep Tripathi and Prashantkumar B. Sathvara
12.1 Introduction
12.2 Challenge in HVAC System
12.3 HVAC Control Applications
12.4 Advanced Control Strategies Mentioned for HVAC Systems
12.5 Energy Efficiency and Sustainability in HVAC System
12.6 Human-Centric HVAC Control
12.7 Indoor Environmental Quality
12.8 Case Studies
12.9 Conclusion
References
13. Enhancing UAV Navigation in Partially Observable 2D Environments: An Optimized Obstacle Avoidance Approach
Jun Jet Tai and Swee King Phang
13.1 Introduction
13.2 Background
13.2.1 Preliminaries
13.2.2 Pathfinding Algorithms
13.2.3 Obstacle Avoidance Algorithms
13.2.4 Learned Obstacle Avoidance and Navigation
13.3 Overview of the Proposed Method
13.3.1 Notation
13.3.2 Obstacle Avoidance and Target Heading
13.3.3 A* Search Integration
13.4 Implementation and Deployment
13.4.1 UAV Platform
13.5 Results
13.5.1 Simulation Performance vs. Real-World Performance
13.5.2 Sensitivity Analysis of Varying Hyperparameters
13.5.3 Repeated Performance Example
13.6 Conclusion
References
14. Fast Inner and Outer Dynamics Control of Multi-Rotor UAVs with Novel SIPIC and RPT Controllers Design
Swee King Phang and Jun Jet Tai
14.1 Introduction
14.2 Dynamic Model of Quadrotor UAV
14.2.1 Kinematic and Rigid-Body Dynamics
14.2.2 Forces and Moments Generation
14.2.3 Motor Dynamics
14.3 Control Strategy for High-Speed Maneuver
14.3.1 SIPIC Orientation Controller Design
14.3.2 Inner-Loop Stability Analysis
14.3.3 RPT Position Controller Design
14.4 Simulation and Flight Results
14.5 Conclusions
References
15. Type 1 Cascaded Fuzzy Logic–Based Autonomous Vehicles Control Applications
Eshan Samanta, Sagarika Pal and Anupam De
15.1 Introduction
15.2 Mathematical Kinematic Model
15.2.1 Car Kinematics
15.2.2 Differential Kinematics
15.3 Control Architectures for Autonomous Vehicles
15.4 Perception and Sensor Fusion for Autonomous Navigation
15.5 Intelligent Control for Path Planning and Collision Avoidance
15.5.1 Fuzzy System Rules
15.5.2 Membership Function
15.5.3 Results and Discussions
15.6 Case Studies of Intelligent Control in Autonomous Vehicles
15.6.1 Google’s Driverless Car Technology - First Case Study
15.6.2 AI-Based Robotaxis - Second Case Study
15.7 Conclusion
References
16. AI-Driven Electric Vehicle Integration for Sustainable Transportation
Loveneet Mishra, Usha Chauhan and Manasi Pattnaik
16.1 Introduction
16.2 Overview of Electric Vehicle Charging
16.3 Rate Control Oriented at the Power Grid
16.4 Model of the System
16.5 Future of AI-Enabled EV Charging
16.5.1 Edge Computing and IoT Integration
16.5.2 Vehicle-to-Grid (V2G) Integration
16.5.3 Collaborative Charging Networks
16.5.4 Autonomous and Connected Vehicles
16.6 Conclusion
References
17. Wireless EV Charging System Design
Koushik Majumder, Maitrayee Chakrabarty, Rakesh Das and Raju Basak
17.1 Introduction
17.2 Literature Survey
17.3 Problem Statement
17.4 Methodology
17.5 Principle of WPT
17.6 Advantages
17.7 WPT Method
17.7.1 Case Study 1 (IWPT Method)
17.7.1.1 Result Analysis for Case Study 1
17.7.1.2 Graphical Nature For IWPT
17.7.2 Case Study 2 (CWPT)
17.7.2.1 Result Analysis for Case Study 2
17.7.2.2 Graphical Nature for CWPT Method
17.7.3 Case Study 3 (Resonance Inductive Power Transfer)
17.7.3.1 Result Analysis for Case Study 3
17.7.3.2 Graphical Nature for RIWPT Method
17.8 Performance Analysis for Three Case Studies
17.9 Proposed Method for Power Source by Renewable Energy Source
17.9.1 MATLAB Model for Proposed System
17.10 Future Scope for Contactless Power Transfer Method
17.11 Conclusion
References
Index

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