This book focuses on developing a comprehensive methodology for designing interval type-3 fuzzy systems. It includes the robustness analysis of general type-2 fuzzy systems and presents a systematic approach to designing an interval type-2 Takagi-Sugeno (T-S) fuzzy model-based (FMB) flight control system, specifically tailored for Generic Aircraft and UAVs. A novel concept is introduced, where flight envelope parameters are mapped to serve as premise variables in the IT2 T-S fuzzy system. Fuzzy system theory is widely regarded for its effectiveness in system modeling and control design. Fuzzy controllers offer robust performance, nonlinear modeling capabilities, and other advanced features that exceed the capabilities of traditional control methods. They are particularly suited for managing complex dynamical systems, which are often subject to noise, dynamic changes, and imprecise models. This book focuses upon the application of fuzzy systems in aircraft and UAVs.
Table of Contents:
Chapter 1 Introduction.- Chapter 2 Advancements in Fuzzy Logic.- Chapter 3 Type 1 Fuzzy Model Based Control System.
About the Author :
Dr. Dhan Jeet Singh received his Ph.D. degree in Electrical Engineering from the Indian Institute of Technology Kanpur, India. Dr. Singh is currently working with Aircraft Research and Design Centre, Hindustan Aeronautics Limited (HAL), Bangalore, India. Dr. Singh's research interests are Artificial Intelligence, Machine Learning, Deep Learning, and Higher-Order Fuzzy Systems and Control. Dr. Singh is member of Aeronautical Society of India (AeSI), Institute of Engineers (IEI) India and Institute of Electrical and Electronics Engineers (IEEE). Dr. Singh has been serving as active reviewer of various reputed journals and conferences which include IEEE Transactions on Artificial Intelligence, IEEE Transactions on Neural Networks and Learning Systems, and many more.
Dr. Teena Sharma is an Assistant Professor at the Mehta Family School of Data Science and Artificial Intelligence (MFSDS&AI), Indian Institute of Technology Guwahati, India. Dr. Sharma worked as a Postdoctoral Scholar at the University of Tennessee, Memphis, Tennessee, USA. Dr. Sharma received her Ph.D. degree in Electrical Engineering from the Indian Institute of Technology Kanpur, India. Dr. Sharma's research interests are Artificial Intelligence, Machine Learning, and Deep Learning Algorithms and their applications to Computer Vision: Object detection, Classification, Identification, Recognition, Image enhancement, Image matching; Equitable Precision Medicine: Transfer learning, Meta-learning, Few-shot learning; and Condition-based Monitoring: Fault diagnosis and remaining useful life prediction. Dr. Sharma is also serving as an Associate Editor for the IEEE Transactions on Artificial Intelligence.
Dr. Nishchal K. Verma is a Professor of Electrical Engineering at the Indian Institute of Technology Kanpur, India, and a leading researcher in artificial intelligence and computational intelligence. His research focuses on AI theory and its real‑world deployment across interdisciplinary domains, including machine learning, deep and hybrid models, computer vision, prognostics and health management (PHM), cyber‑physical systems, bioinformatics, and complex nonlinear system modeling. Dr. Verma has published 275+ peer‑reviewed papers and four books, and has led 23 funded research projects supported by premier agencies and industries such as The Boeing Company (USA), DST, DRDO, SERB, CSIR, MHRD, and IIT Kanpur. His work has contributed to both fundamental advances and large‑scale industrial applications of AI. He serves on the editorial boards of leading journals.
