Jitendra R. Raol – författare

Flight Mechanics Modeling and Analysis comprehensively covers flight mechanics and flight dynamics using a systems approach.

This book focuses on applied mathematics and control theory in its discussion of flight mechanics to build a strong foundation for solving design and control problems in the areas of flight simulation and flight data analysis. The second edition has been expanded to include two new chapters and coverage of aeroservoelastic topics and engineering mechanics, presenting more concepts of flight control and aircraft parameter estimation.

This book is intended for senior undergraduate aerospace students taking Aircraft Mechanics, Flight Dynamics & Controls, and Flight Mechanics courses. It will also be of interest to research students and R&D project-scientists of the same disciplines.

Including end-of-chapter exercises and illustrative examples with a MATLAB®-based approach, this book also includes a Solutions Manual and Figure Slides for adopting instructors.

Features:

Flight Mechanics Modeling and Analysis comprehensively covers flight mechanics and flight dynamics using a systems approach.

This book focuses on applied mathematics and control theory in its discussion of flight mechanics to build a strong foundation for solving design and control problems in the areas of flight simulation and flight data analysis. The second edition has been expanded to include two new chapters and coverage of aeroservoelastic topics and engineering mechanics, presenting more concepts of flight control and aircraft parameter estimation.

This book is intended for senior undergraduate aerospace students taking Aircraft Mechanics, Flight Dynamics & Controls, and Flight Mechanics courses. It will also be of interest to research students and R&D project-scientists of the same disciplines.

Including end-of-chapter exercises and illustrative examples with a MATLAB®-based approach, this book also includes a Solutions Manual and Figure Slides for adopting instructors.

Features:

Situation Assessment in Aviation focuses on new aspects of soft computing technologies for the evaluation and assessment of situations in aviation scenarios. It considers technologies emerging from multisensory data fusion (MSDF), Bayesian networks (BN), and fuzzy logic (FL) to assist pilots in their decision-making.

Studying MSDF, BN, and FL from the perspective of their applications to the problem of situation assessment, the book discusses the development of certain soft technologies that can be further used for devising more sophisticated technologies for a pilot''s decision-making when performing certain tasks: airplane monitoring, pair formation, attack, and threat. It explains the concepts of situation awareness, data fusion, decision fusion, Bayesian networks, fuzzy logic type 1, and interval type 2 fuzzy logic. The book also presents a hybrid technique by using BN and FL and a unique approach to the problem of situation assessment, beyond visual range and air-to-air combat, by utilizing building blocks of artificial intelligence (AI) for the future development of more advanced automated systems, especially using commercial software.

The book is intended for aerospace R&D engineers, systems engineers, aeronautical engineers, and aviation training professionals. It will also be useful for aerospace and electrical engineering students taking courses in Air Traffic Management, Aviation Management, Aviation Operations, and Aviation Safety Systems.

Situation Assessment in Aviation focuses on new aspects of soft computing technologies for the evaluation and assessment of situations in aviation scenarios. It considers technologies emerging from multisensory data fusion (MSDF), Bayesian networks (BN), and fuzzy logic (FL) to assist pilots in their decision-making.

Studying MSDF, BN, and FL from the perspective of their applications to the problem of situation assessment, the book discusses the development of certain soft technologies that can be further used for devising more sophisticated technologies for a pilot''s decision-making when performing certain tasks: airplane monitoring, pair formation, attack, and threat. It explains the concepts of situation awareness, data fusion, decision fusion, Bayesian networks, fuzzy logic type 1, and interval type 2 fuzzy logic. The book also presents a hybrid technique by using BN and FL and a unique approach to the problem of situation assessment, beyond visual range and air-to-air combat, by utilizing building blocks of artificial intelligence (AI) for the future development of more advanced automated systems, especially using commercial software.

The book is intended for aerospace R&D engineers, systems engineers, aeronautical engineers, and aviation training professionals. It will also be useful for aerospace and electrical engineering students taking courses in Air Traffic Management, Aviation Management, Aviation Operations, and Aviation Safety Systems.

Mathematical Modelling of Aerospace Dynamic Systems with Practical Applications provides mathematical models for several aerospace dynamic systems: aircraft, rotorcraft, missiles, unmanned aerial vehicles (UAVs), mini air vehicles (MAVs), autonomous underwater vehicles (AUWVs), and satellite-coordinate systems.

Presenting the use of mathematical models for analysis, prediction, and control of these systems, this book discusses numerous applications in aircraft/helicopter parameter estimation, guidance and navigation of these vehicles, underwater object search, aerial terrain mapping, and satellite orbit determination. It explains path planning with obstacle avoidance, object occlusion detection and tracking, and multisensory target tracking and sensor data fusion.

This book is intended for senior undergraduate mechanical and aerospace engineering students taking courses in aerospace systems and dynamics, flight dynamics and control, and dynamical systems and estimation.

Instructors will be able to utilize a Solutions Manual and Figure Slides for their course.

Mathematical Modelling of Aerospace Dynamic Systems with Practical Applications provides mathematical models for several aerospace dynamic systems: aircraft, rotorcraft, missiles, unmanned aerial vehicles (UAVs), mini air vehicles (MAVs), autonomous underwater vehicles (AUWVs), and satellite-coordinate systems.

Presenting the use of mathematical models for analysis, prediction, and control of these systems, this book discusses numerous applications in aircraft/helicopter parameter estimation, guidance and navigation of these vehicles, underwater object search, aerial terrain mapping, and satellite orbit determination. It explains path planning with obstacle avoidance, object occlusion detection and tracking, and multisensory target tracking and sensor data fusion.

This book is intended for senior undergraduate mechanical and aerospace engineering students taking courses in aerospace systems and dynamics, flight dynamics and control, and dynamical systems and estimation.

Instructors will be able to utilize a Solutions Manual and Figure Slides for their course.

This book deals with the basics of parameter estimation and state estimation as the fundamental building blocks of mathematical modelling activity in the broader field of control theory. All the methods are validated using MATLAB®-based implementations with realistically simulated data for general dynamic systems, as well as for aircraft parameter estimation. This book includes several illustrative examples and chapter-end exercises.

Features:

This book is aimed at researchers and graduate students in systems and control, signal processing, estimation theory, engineering mathematics, and aerospace engineering.

This book deals with the basics of parameter estimation and state estimation as the fundamental building blocks of mathematical modelling activity in the broader field of control theory. All the methods are validated using MATLAB®-based implementations with realistically simulated data for general dynamic systems, as well as for aircraft parameter estimation. This book includes several illustrative examples and chapter-end exercises.

Features:

This book is aimed at researchers and graduate students in systems and control, signal processing, estimation theory, engineering mathematics, and aerospace engineering.

Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion (DF) and provides a proper and adequate understanding of the basic mathematics directly related to DF.

This new edition offers updated chapters alongside four new chapters that are based on recent research carried out by the authors, including topics on machine learning techniques, target localization using a network of 2D ground radar, thermal imaging sensors for multi‑target angle‑only tracking, and multi‑sensor data fusion for a single platform and team platforms. This book also covers major mathematical expressions, formulae and equations, and, where feasible, their derivations. It discusses signed distance function concepts, DF models and architectures, aspects and methods of types 1 and 2 fuzzy logics, and related practical applications. In addition, the authors cover soft computing paradigms that are finding increasing applications in multi-sensory DF approaches and applications.

This text is geared toward researchers, scientists, teachers, and practicing engineers interested in and working in the multi‑sensor data fusion area.

Data Fusion Mathematics: Theory and Practice offers a comprehensive overview of data fusion (DF) and provides a proper and adequate understanding of the basic mathematics directly related to DF.

This new edition offers updated chapters alongside four new chapters that are based on recent research carried out by the authors, including topics on machine learning techniques, target localization using a network of 2D ground radar, thermal imaging sensors for multi‑target angle‑only tracking, and multi‑sensor data fusion for a single platform and team platforms. This book also covers major mathematical expressions, formulae and equations, and, where feasible, their derivations. It discusses signed distance function concepts, DF models and architectures, aspects and methods of types 1 and 2 fuzzy logics, and related practical applications. In addition, the authors cover soft computing paradigms that are finding increasing applications in multi-sensory DF approaches and applications.

This text is geared toward researchers, scientists, teachers, and practicing engineers interested in and working in the multi‑sensor data fusion area.

Control Systems: Classical, Modern, and AI-Based Approaches provides a broad and comprehensive study of the principles, mathematics, and applications for those studying basic control in mechanical, electrical, aerospace, and other engineering disciplines. The text builds a strong mathematical foundation of control theory of linear, nonlinear, optimal, model predictive, robust, digital, and adaptive control systems, and it addresses applications in several emerging areas, such as aircraft, electro-mechanical, and some nonengineering systems: DC motor control, steel beam thickness control, drum boiler, motional control system, chemical reactor, head-disk assembly, pitch control of an aircraft, yaw-damper control, helicopter control, and tidal power control. Decentralized control, game-theoretic control, and control of hybrid systems are discussed. Also, control systems based on artificial neural networks, fuzzy logic, and genetic algorithms, termed as AI-based systems are studied and analyzed with applications such as auto-landing aircraft, industrial process control, active suspension system, fuzzy gain scheduling, PID control, and adaptive neuro control. Numerical coverage with MATLAB® is integrated, and numerous examples and exercises are included for each chapter. Associated MATLAB® code will be made available.

Control Systems: Classical, Modern, and AI-Based Approaches provides a broad and comprehensive study of the principles, mathematics, and applications for those studying basic control in mechanical, electrical, aerospace, and other engineering disciplines. The text builds a strong mathematical foundation of control theory of linear, nonlinear, optimal, model predictive, robust, digital, and adaptive control systems, and it addresses applications in several emerging areas, such as aircraft, electro-mechanical, and some nonengineering systems: DC motor control, steel beam thickness control, drum boiler, motional control system, chemical reactor, head-disk assembly, pitch control of an aircraft, yaw-damper control, helicopter control, and tidal power control. Decentralized control, game-theoretic control, and control of hybrid systems are discussed. Also, control systems based on artificial neural networks, fuzzy logic, and genetic algorithms, termed as AI-based systems are studied and analyzed with applications such as auto-landing aircraft, industrial process control, active suspension system, fuzzy gain scheduling, PID control, and adaptive neuro control. Numerical coverage with MATLAB® is integrated, and numerous examples and exercises are included for each chapter. Associated MATLAB® code will be made available.

Nonlinear Filtering covers linear and nonlinear filtering in a comprehensive manner, with appropriate theoretic and practical development. Aspects of modeling, estimation, recursive filtering, linear filtering, and nonlinear filtering are presented with appropriate and sufficient mathematics. A modeling-control-system approach is used when applicable, and detailed practical applications are presented to elucidate the analysis and filtering concepts. MATLAB routines are included, and examples from a wide range of engineering applications - including aerospace, automated manufacturing, robotics, and advanced control systems - are referenced throughout the text.