This book discusses the parametric modeling, performance evaluation, design optimization and comparative study of the high-speed, parallel pick-and-place robots. It collects the modeling methodology, evaluation criteria and design guidelines for parallel PnP robots to provide a systematic analysis method for robotic developers. Furthermore, it gathers the research results previously scattered in many prestigious international journals and conference proceedings and methodically edits them and presents them in a unified form. The book is of interest to researchers, R&D engineers and graduate students in industrial parallel robotics who wish to learn the core principles, methods, algorithms, and applications.

This book discusses systematic designs of stable adaptive fuzzy logic controllers employing hybridizations of Lyapunov strategy-based approaches/H theory-based approaches and contemporary stochastic optimization techniques. The text demonstrates how candidate stochastic optimization techniques like Particle swarm optimization (PSO), harmony search (HS) algorithms, covariance matrix adaptation (CMA) etc. can be utilized in conjunction with the Lyapunov theory/H theory to develop such hybrid control strategies. The goal of developing a series of such hybridization processes is to combine the strengths of both Lyapunov theory/H theory-based local search methods and stochastic optimization-based global search methods, so as to attain superior control algorithms that can simultaneously achieve desired asymptotic performance and provide improved transient responses. The book also demonstrates how these intelligent adaptive control algorithms can be effectively utilized in real-life applications such as in temperature control for air heater systems with transportation delay, vision-based navigation of mobile robots, intelligent control of robot manipulators etc.

This book presents high-quality contributions in the subject area of Aerospace System Science and Engineering, including topics such as: Trans-space vehicle systems design and integration, Air vehicle systems, Space vehicle systems, Near-space vehicle systems, Opto-electronic system, Aerospace robotics and unmanned system, Aerospace robotics and unmanned system, Communication, navigation, and surveillance, Dynamics and control, Intelligent sensing and information fusion, Aerodynamics and aircraft design, Aerospace propulsion, Avionics system, Air traffic management, Earth observation, Deep space exploration, and Bionic micro-aircraft/spacecraft. The book collects selected papers presented at the 4th International Conference on Aerospace System Science and Engineering (ICASSE 2020), organized by Shanghai Jiao Tong University, China, held on 14-16 July 2020 as virtual event due to COVID-19. It provides a forum for experts in aeronautics and astronautics to share new ideas and findings. ICASSE conferences have been organized annually since 2017 and hosted in Shanghai, Moscow, and Toronto in turn, where the three regional editors of the journal Aerospace Systems are located.

This textbook helps graduate level student to understand easily the linearization of nonlinear control system. Differential geometry is essential to understand the linearization problems of the control nonlinear systems. In this book, the basics of differential geometry needed in linearization are explained on the Euclidean space instead of the manifold for students who are not accustomed to differential geometry. Many Lie algebra formulas, used often in linearization, are also provided with proof. The conditions in the linearization problems are complicated to check because the Lie bracket calculation of vector fields by hand needs much concentration and time. This book provides MATLAB programs for most of the theorems. The book also includes end-of-chapter problems and other pedagogical aids to help understanding and self study.

Introduction to Network Simulator NS2 is a primer providing materials for NS2 beginners, whether students, professors, or researchers for understanding the architecture of Network Simulator 2 (NS2) and for incorporating simulation modules into NS2. The authors discuss the simulation architecture and the key components of NS2 including simulation-related objects, network objects, packet-related objects, and helper objects. The NS2 modules included within are nodes, links, SimpleLink objects, packets, agents, and applications. Further, the book covers three helper modules: timers, random number generators, and error models. Also included are chapters on summary of debugging, variable and packet tracing, result compilation, and examples for extending NS2. Two appendices provide the details of scripting language Tcl, OTcl and AWK, as well object oriented programming used extensively in NS2.

This book presents generalized Caputo fractional Ostrowski and Gruss-type inequalities involving several Banach algebra valued functions. Furthermore, the author gives generalized Canavati fractional Ostrowski, Opial, Gruss, and Hilbert-Pachpatte-type inequalities for multiple Banach algebra valued functions. By applying the p-Schatten norms over the von Neumann-Schatten classes, the author produces the analogous refined and interesting inequalities. The author provides many applications. This book's results are expected to find applications in many areas of pure and applied mathematics, especially in fractional inequalities and fractional differential equations. Other interesting applications are in applied sciences like geophysics, physics, chemistry, economics, and engineering. This book is appropriate for researchers, graduate students, practitioners, and seminars of the above disciplines, also to be in all science and engineering libraries.

This book proposes some novel approaches for finding unmanned aerial vehicle trajectories to reach targets with unknown location in minimum time. At first, it reviews probabilistic search algorithms that have been used for dealing with the minimum time search (MTS) problem, and discusses how metaheuristics, and in particular the ant colony optimization algorithm (ACO), can help to find high-quality solutions with low computational time. Then, it describes two ACO-based approaches to solve the discrete MTS problem and the continuous MTS problem, respectively. In turn, it reports on the evaluation of the ACO-based discrete and continuous approaches to the MTS problem in different simulated scenarios, showing that the methods outperform in most all the cases over other state-of-the-art approaches. In the last part of the thesis, the work of integration of the proposed techniques in the ground control station developed by Airbus to control ATLANTE UAV is reported in detail, providing practical insights into the implementation of these methods for real UAVs.

This multi-volume handbook is the most up-to-date and comprehensive reference work in the field of fractional calculus and its numerous applications. This sixth volume collects authoritative chapters covering several applications of fractional calculus in control theory, including fractional controllers, design methods and toolboxes, and a large number of engineering applications of control.

Changing world market conditions have forced manufacturers to apply new architectures and technologies for the design and control of manufacturing systems. Distributed Manufacturing: Paradigm, Concepts, Solutions and Examples outlines the current requirements of manufacturing systems and addresses the architectures, methodologies, and technologies developed within European research activities in response to these requirements.

Distributed Manufacturing: Paradigm, Concepts, Solutions and Examples will be of interest to researchers and developers in all fields involving industrial control systems, as well as to decision-makers within industry and government organizations. The reader will gain a detailed knowledge of the current research directions in industrial control, reaching a comprehensive understanding of current advances, their expected benefits and limitations, and the possible consequences for industrial businesses.

This book contains new and useful materials concerning fuzzy fractional differential and integral operators and their relationship. As the title of the book suggests, the fuzzy subject matter is one of the most important tools discussed. Therefore, it begins by providing a brief but important and new description of fuzzy sets and the computational calculus they require. Fuzzy fractals and fractional operators have a broad range of applications in the engineering, medical and economic sciences. Although these operators have been addressed briefly in previous papers, this book represents the first comprehensive collection of all relevant explanations. Most of the real problems in the biological and engineering sciences involve dynamic models, which are defined by fuzzy fractional operators in the form of fuzzy fractional initial value problems. Another important goal of this book is to solve these systems and analyze their solutions both theoretically and numerically. Given the content covered, the book will benefit all researchers and students in the mathematical and computer sciences, but also the engineering sciences.

Hybrid System Identification helps readers to build mathematical models of dynamical systems switching between different operating modes, from their experimental observations. It provides an overview of the interaction between system identification, machine learning and pattern recognition fields in explaining and analysing hybrid system identification. It emphasises the optimization and computational complexity issues that lie at the core of the problems considered and sets them aside from standard system identification problems. The book presents practical methods that leverage this complexity, as well as a broad view of state-of-the-art machine learning methods. The authors illustrate the key technical points using examples and figures to help the reader understand the material. The book includes an in-depth discussion and computational analysis of hybrid system identification problems, moving from the basic questions of the definition of hybrid systems and system identification to methods of hybrid system identification and the estimation of switched linear/affine and piecewise affine models. The authors also give an overview of the various applications of hybrid systems, discuss the connections to other fields, and describe more advanced material on recursive, state-space and nonlinear hybrid system identification. Hybrid System Identification includes a detailed exposition of major methods, which allows researchers and practitioners to acquaint themselves rapidly with state-of-the-art tools. The book is also a sound basis for graduate and undergraduate students studying this area of control, as the presentation and form of the book provides the background and coverage necessary for a full understanding of hybrid system identification, whether the reader is initially familiar with system identification related to hybrid systems or not.

This book consolidates decades of knowledge on space flight navigation theory, which has thus far been spread across various research articles. By gathering this research into a single text, it will be more accessible to students curious about the study of space flight navigation. Books on optimal control theory and orbital mechanics have not adequately explored the field of space flight navigation theory until this point. The opening chapters introduce essential concepts within optimal control theory, such as the optimization of static systems, special boundary conditions, and dynamic equality constraints. An analytical approach is focused on throughout, as opposed to computational. The result is a book that emphasizes simplicity and practicability, which makes it accessible and engaging. This holds true in later chapters that involve orbital mechanics, two-body maneuvers, bounded inputs, and flight in non-spherical gravity fields. The intended audience is primarily upper-undergraduate students, graduate students, and researchers of aerospace, mechanical, and/or electrical engineering. It will be especially valuable to those with interests in spacecraft dynamics and control. Readers should be familiar with basic dynamics and modern control theory. Additionally, a knowledge of linear algebra, variational methods, and ordinary differential equations is recommended.

Deep learning is an artificially intelligent entity that teaches itself and can be utilized to make predictions. Deep learning mimics the human brain and provides learned solutions addressing many challenging problems in the area of visual computing. From object recognition to image classification for diagnostics, deep learning has shown the power of artificial deep neural networks in solving real world visual computing problems with super-human accuracy. The introduction of deep learning into the field of visual computing has meant to be the death of most of the traditional image processing and computer vision techniques. Today, deep learning is considered to be the most powerful, accurate, efficient and effective method with the potential to solve many of the most challenging problems in visual computing. This book provides an insight into deep machine learning and the challenges in visual computing to tackle the novel method of machine learning. It introduces readers to the world of deep neural network architectures with easy-to-understand explanations. From face recognition to image classification for diagnosis of cancer, the book provides unique examples of solved problems in applied visual computing using deep learning. Interested and enthusiastic readers of modern machine learning methods will find this book easy to follow. They will find it a handy guide for designing and implementing their own projects in the field of visual computing.

This book discusses the realization and control problems of finite-dimensional dynamical systems which contain linear and nonlinear systems. The author focuses on algebraic methods for the discussion of control problems of linear and non-linear dynamical systems. The book contains detailed examples to showcase the effectiveness of the presented method. The target audience comprises primarily research experts in the field of control theory, but the book may also be beneficial for graduate students alike.

Consensus Tracking of Multi-agent Systems with Switching Topologies takes an advanced look at the development of multi-agent systems with continuously switching topologies and relay tracking systems with switching of agents. Research problems addressed are well defined and numerical examples and simulation results are given to demonstrate the engineering potential. The book is aimed at advanced graduate students in control engineering, signal processing, nonlinear systems, switched systems and applied mathematics. It will also be a core reference for control engineers working on nonlinear control and switched control, as well as mathematicians and biomedical engineering researchers working on complex systems.

This book focuses on most recent theoretical findings on control issues for active suspension systems. The authors first introduce the theoretical background of active suspension control, then present constrained H control approaches of active suspension systems in the entire frequency domain, focusing on the state feedback and dynamic output feedback controller in the finite frequency domain which people are most sensitive to. The book also contains nonlinear constrained tracking control via terminal sliding-mode control and adaptive robust theory, presenting controller design of active suspensions as well as the reliability control of active suspension systems. The target audience primarily comprises research experts in control theory, but the book may also be beneficial for graduate students alike.

The book provides knowledge in the Building Information Model (BIM)-enabled cognitive computing methods for smart built environment involving cognitive network capabilities for smart buildings, integrating Augmented Reality/Mixed Reality in cognitive building concepts, cognitive Internet of Things (CIoT) for smart cities, Artificial Intelligence applications for cognitive cities, and cognitive smart cities using big data and machine learning. It focuses on the potential, requirements and implementation of CIoT paradigm to buildings, Artificial Intelligence techniques, reasoning, and Augmented Reality/Mixed Reality in cognitive building concepts, the concept of cognitive smart cities in its complexity, heterogeneity, and scope, and the challenge of utilizing the big data generated by smart cities from a machine learning perspective. The book comprises BIM-based and data-analytic research on cognitive IoT for smart buildings and cognitive cities using big data and machine learning as complex and dynamic systems. It presents applied theoretical contributions fostering a better understanding of such systems and the synergistic relationships between the motivating physical and informational settings. It reviews ongoing development of BIM-based and data science technologies for the processing, analysis, management, modeling, and simulation of big and context data and the associated applicability to cognitive systems that will advance different aspects of future cognitive cities. The book also analyses the required material to inform pertinent research communities of the state-of-the-art research and the latest development in the area of cognitive smart cities development, as well as a valuable reference for planners, designers, strategists, and ICT experts who are working towards the development and implementation of CIoT based on big data analytics and context-aware computing.

There is an increasing demand for dynamic systems to become more safe and reliable. This requirement extends beyond the normally accepted safety-critical systems of nuclear reactors and aircraft where safety is paramount important, to systems such as autonomous vehicles and fast railways where the system availability is vital. It is clear that fault diagnosis (including fault detection and isolation, FDI) has been becoming an important subject in modern control theory and practice. For example, the number of papers on FDI presented in many control-related conferences has been increasing steadily. The subject of fault detection and isolation continues to mature to an established field of research in control engineering. A large amount of knowledge on model-based fault diagnosis has been ac cumulated through the literature since the beginning of the 1970s. However, publications are scattered over many papers and a few edited books. Up to the end of 1997, there is no any book which presents the subject in an unified framework. The consequence of this is the lack of "common language," dif ferent researchers use different terminology. This problem has obstructed the progress of model-based FDI techniques and has been causing great concern in research community. Many survey papers have been published to tackle this problem. However, a book which presents the materials in a unified format and provides a comprehensive foundation of model-based FDI is urgently needed."

This book gathers contributions from a multidisciplinary research team comprised of control engineering and economics researchers and formed to address a central interdisciplinary social issue, namely economically enabled energy management. The book's primary focus is on achieving optimal energy management that is viable from both an engineering and economic standpoint. In addition to the theoretical results and techniques presented, several chapters highlight experimental case studies, which will benefit academic researchers and practitioners alike. The first three chapters present comprehensive overviews of respective social contexts, underscore the pressing need for economically efficient energy management systems and academic work on this emerging research topic, and identify fundamental differences between approaches in control engineering and economics. In turn, the next three chapters (Chapters 4-6) provide economics-oriented approaches to the subject. The following five chapters (Chapters 7-11) address optimal energy market design, integrating both physical and economic models. The book's last three chapters (Chapters 12-14) mainly focus on the engineering aspects of next-generation energy management, though economic factors are also shown to play important roles.

As featured on CNN, Forbes and Inc - BookAuthority identifies and rates the best books in the world, based on recommendations by the world's most successful business leaders and experts. Winning the spot of #19 out of 26 on the 2020 Bookauthority Best New Industrial Management Books of All Time. Winning the spot of #3 out of 8 on the 2021 Bookauthority Best New Industrial Management Books to Read in 2021. Winning the spot of #5 out of 11 on the 2021 Bookauthority Best New Product Design Books to Read in 2021. 2020 Taylor & Francis Award Winner for Outstanding Professional Book! Product Lifecycle Management (PLM): A Digital Journey Using Industrial Internet of Things (IIot) provides a summary of the essential topics of Product Lifecycle Management (PLM) and the Industrial Internet of Things (IIoT) in the era of Industry 4.0. The book discusses emerging technologies, their contribution towards enhancing product design, development, and manufacturing. It also presents the integration of PLM, Enterprise Resource Planning (ERP), and Manufacturing Execution System (MES) along with IIoT as well the integration of mechanical, electronic components, embedded systems, firmware and software focusing on smart design, development, and manufacturing in the digital transformation journey. The book provides a high-level overview of how the smart product development through smart manufacturing materializes within the smart ecosystem. Manufacturing professionals, designers, mechanical, electrical, electronics, instrumentation and industrial engineers, information and communication technology consultants and those working in production planning, process control, and operations will find this book invaluable.

This book is dedicated to the analysis and modelling of fractional behaviours that mainly result from physical stochastic phenomena (diffusion, adsorption or aggregation, etc.) of a population (ions, molecules, people, etc.) in a constrained environment and that can be found in numerous areas. It breaks with the usual approaches based on fractional models since it proposes to use unusual models which have the advantage of overcoming some of the limitations of fractional models. This book is dedicated to postgraduated students and to researchers in the field or those who wish to learn with a fresh perspective. After a review of fractional models and their limitations, it proposes and demonstrates the interest of four other modelling tools to capture fractional behaviours: new kernels in integral operators, Volterra equations, nonlinear models and partial differential equations with spatially variable coefficients. Several applications on real data and devices illustrate their efficiency.

Supervisory Control Theory (SCT) provides a tool to model and control human-engineered complex systems, such as computer networks, World Wide Web, identification and spread of malicious executables, and command, control, communication, and information systems. Although there are some excellent monographs and books on SCT to control and diagnose discrete-event systems, there is a need for a research monograph that provides a coherent quantitative treatment of SCT theory for decision and control of complex systems. This new monograph will assimilate many new concepts that have been recently reported or are in the process of being reported in open literature. The major objectives here are to present a) a quantitative approach, supported by a formal theory, for discrete-event decision and control of human-engineered complex systems; and b) a set of applications to emerging technological areas such as control of software systems, malicious executables, and complex engineering systems. The monograph will provide the necessary background materials in automata theory and languages for supervisory control. It will introduce a new paradigm of language measure to quantitatively compare the performance of different automata models of a physical system. A novel feature of this approach is to generate discrete-event robust optimal decision and control algorithms for both military and commercial systems.

This book contains a collection of 13 carefully selected papers contributed by researches in technical and partial medical diagnostics as well as fault-tolerant control and constitutes a comprehensive study of the field. Nowadays technical diagnostics and fault-tolerant control are a field of intensive scientific research that covers well-established topics along with emerging developments in control engineering, artificial intelligence, applied mathematics and statistics. At the same time, a growing number of applications of different fault diagnosis methods, especially in the electrical, mechanical, chemical and medical areas, are being observed. The aim of the book is to show the bridge between technical and medical diagnosis based on analytical and artificial intelligence methods and techniques. The book is divided into three parts: I. Fault-Tolerant Control and Reconfiguration, II. Fault Diagnosis of Processes and Systems, III. Medical Applications. The book is of interest to scientists, engineers and academics dealing with the problems of designing technical diagnosis and fault-tolerant control systems. Its target readers are also junior researchers and students of control, artificial intelligence and computer engineering.