Controller Design for Industrial Applications
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.
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.
More details
Other editions
Additional editions
Persons
Arindam Mondal, PhD, is a co-private investigator for a Technology Development Program project under the Indian Department of Science and Technology. He has published 33 research papers in reputed international journals, conferences, and book chapters and has 12 patents published in his credit. His research interests include digital controller design, system identification, fractional order control and signal processing, Internet of Things, bioinformatics, load frequency control, and quantum computing.
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.
Content
Preface xv
1 Fuzzy Logic Control for Industrial Applications 1
Srabanti Maji and Souvik Ganguli
1.1 Introduction 2
1.2 The Evaluation of Fuzzy Logic Control: From Theory to Industrial Applications 4
1.3 Basics of Fuzzy Logic Control: A Comprehensive Overview 6
1.4 Merits of Fuzzy Logic Control 9
1.5 Industrial Applications of Fuzzy Logic Control 13
1.6 Discussions and Future Scope of Work 16
1.7 Conclusions 18
2 Artificial Neural Network for Industrial Applications 21
Biswajit Saha and Gour Sundar Mitra Thakur
2.1 Introduction 22
2.2 Neural Network Models for Industrial Applications 24
2.3 Challenges and Limitations 31
2.4 Industry Use Cases and a Sample Case Study 33
2.5 Future Directions and Emerging Trends 35
2.6 Conclusion 36
3 Artificial Neural Network-Based Sliding Mode Controller for a Class of Nonlinear System 41
Sheetla Prasad, Rammurti Meena and Vipin Chandra Pal
3.1 Introduction 42
3.2 Problem Formulation 44
3.3 Artificial Neural Network Structure 46
3.4 Neural Network Observer-Based SMC 47
3.5 Simulation and Demonstrations 49
3.6 Conclusion 52
4 Finite Control Set Model Predictive Control for Permanent Magnet Synchronous Motor Drives 55
Ravi Eswar Kodumur Meesala, Phani Teja Bankupalli and Chinta Praveen Kumar
4.1 Introduction 56
4.2 Mathematical Model of PMSM Drive 63
4.3 Optimal Control Concepts of Finite Control Set Model Predictive Control (FCSMPC) 69
4.4 Constraints in FCSMPC Operation 77
4.5 Real-Time Implementation of FCSMPC for PMSM Drive 78
5 Kinematic and Dynamic Modeling of Robots 89
Suman Lata Tripathi and Deepika Ghai
5.1 Introduction 90
5.2 Required Block for Robotic Model 91
5.3 Robotic Arm Model 91
5.4 Tool Description 93
5.5 Methodology/Design Steps 93
5.6 Result and Discussion 96
5.7 Applications and Future Scope 99
5.8 Conclusion 101
6 Design of FUZZY-(1+PD)-FOPID Controller for Hybrid Two-Area Power System 105
Susmit Chakraborty and Arindam Mondal
6.1 Introduction 106
6.2 Plant Model 107
6.3 Controller Design 107
6.4 Tree-Seed Algorithm 112
6.5 Result and Analysis 114
6.6 Conclusion 120
7 Design of MPC-TSA Controller for Hybrid Two-Area Power System 125
Susmit Chakraborty and Arindam Mondal
7.1 Introduction 126
7.2 Plant Model 127
7.3 Model Predictive Controller 128
7.4 Tree-Seed Algorithm 132
7.5 Result and Analysis 134
7.6 Conclusion 136
8 Wide-Area Monitoring, Protection, Automation and Control (WAMPAC) System 141
Sanchita Kumari and Amrita Sinha
8.1 Introduction 142
8.2 Blackouts 143
8.3 Supervisory Control and Data Acquisition System 145
8.4 Phasor Measurement Units (PMU) 149
8.5 Intelligent Electronic Devices (IEDs) 157
8.6 Communication Protocols 160
8.7 Conclusions 162
9 An Efficient Smart Prepaid Interface Design for Power Industries 167
Antara Kundu, Harsh Kumar Shaw and Maitrayee Chakrabarty
9.1 Introduction 168
9.2 Literature Survey 170
9.3 Proposed System 178
9.4 Industrial Designing Application for Power Systems Control 185
9.5 Conclusion 185
10 PV System Maximum Power Point Tracking Under Partial Shadowing Using Gray Wolf Optimization Algorithm 189
Snehashis Ghoshal and Arindam Mondal
10.1 Introduction 189
10.2 Analysis and Modeling of Solar PV Systems 190
10.3 Impact of Partial Shading in a PV System 193
10.4 Performance Optimization of Solar Panel During Partial Shading Condition 194
10.5 Significance of MPPT in Partial Shading Conditions 196
10.6 MPPT Techniques for Partially Shaded Scenario 197
10.7 Simulation Result and Analysis 199
10.8 Conclusion 207
11 An Efficient Optimization Approach for Solving the Relay Coordination Problem 211
Maitrayee Chakrabarty, Sudipta Chakraborty, Suparna Pal and Raju Basak
11.1 Introduction 212
11.2 Literature Survey 213
11.3 Overview of the System 215
11.4 Problem Formulation and Constraint Criterion 216
11.5 Proposed PSO Algorithm 219
11.6 Simulation Outcomes and Discussions 220
11.7 Industrial Application Design Consideration 227
11.8 Conclusion 228
12 Intelligent Control for Energy-Efficient HVAC System Modeling and Control 233
R. Sanjeevi, J. Anuradha, Sandeep Tripathi and Prashantkumar B. Sathvara
12.1 Introduction 234
12.2 Challenge in HVAC System 236
12.3 HVAC Control Applications 237
12.4 Advanced Control Strategies Mentioned for HVAC Systems 238
12.5 Energy Efficiency and Sustainability in HVAC System 241
12.6 Human-Centric HVAC Control 246
12.7 Indoor Environmental Quality 248
12.8 Case Studies 249
12.9 Conclusion 251
13 Enhancing UAV Navigation in Partially Observable 2D Environments: An Optimized Obstacle Avoidance Approach 257
Jun Jet Tai and Swee King Phang
13.1 Introduction 258
13.2 Background 259
13.3 Overview of the Proposed Method 262
13.4 Implementation and Deployment 270
13.5 Results 270
13.6 Conclusion 282
14 Fast Inner and Outer Dynamics Control of Multi-Rotor UAVs with Novel SIPIC and RPT Controllers Design 287
Swee King Phang and Jun Jet Tai
14.1 Introduction 288
14.2 Dynamic Model of Quadrotor UAV 289
14.3 Control Strategy for High-Speed Maneuver 294
14.4 Simulation and Flight Results 302
14.5 Conclusions 304
15 Type 1 Cascaded Fuzzy Logic-Based Autonomous Vehicles Control Applications 309
Eshan Samanta, Sagarika Pal and Anupam De
15.1 Introduction 310
15.2 Mathematical Kinematic Model 311
15.3 Control Architectures for Autonomous Vehicles 314
15.4 Perception and Sensor Fusion for Autonomous Navigation 315
15.5 Intelligent Control for Path Planning and Collision Avoidance 316
15.6 Case Studies of Intelligent Control in Autonomous Vehicles 324
15.7 Conclusion 326
16 AI-Driven Electric Vehicle Integration for Sustainable Transportation 331
Loveneet Mishra, Usha Chauhan and Manasi Pattnaik
16.1 Introduction 332
16.2 Overview of Electric Vehicle Charging 336
16.3 Rate Control Oriented at the Power Grid 337
16.4 Model of the System 339
16.5 Future of AI-Enabled EV Charging 341
16.6 Conclusion 346
17 Wireless EV Charging System Design 351
Koushik Majumder, Maitrayee Chakrabarty, Rakesh Das and Raju Basak
17.1 Introduction 352
17.2 Literature Survey 353
17.3 Problem Statement 356
17.4 Methodology 357
17.5 Principle of WPT 359
17.6 Advantages 359
17.7 WPT Method 360
17.8 Performance Analysis for Three Case Studies 370
17.9 Proposed Method for Power Source by Renewable Energy Source 371
17.10 Future Scope for Contactless Power Transfer Method 372
17.11 Conclusion 372
References 373
Index 375
