The interest in the field of active flow control (AFC) is steadily increasing. In - cent years the number of conferences and special sessions devoted to AFC org- ized by various institutions around the world continuously rises. New advanced courses for AFC are offered by the American Institute of Aeronautics and Ast- nautics (AIAA), the European Research Community on Flow, Turbulence and Combustion (ERCOFTAC), the International Centre for Mechanical Sciences (CISM), the von Karman Institute for Fluid Dynamics (VKI), to name just a few. New books on AFC are published by prominent colleagues of our field and even a new periodical, the 'International Journal of Flow Control', appeared. Despite these many activities in AFC it was felt that a follow-up of the highly successful 'ACTIVE FLOW CONTROL' Conference held in Berlin in 2006 was appropriate. As in 2006, 'ACTIVE FLOW CONTROL II' consisted only of invited lectures. To sti- late multidisciplinary discussions between experimental, theoretical and numerical fluid dynamics, aerodynamics, turbomachinary, mathematics, control engineering, metrology and computer science parallel sessions were excluded. Unfortunately, not all of the presented papers made it into this volume. As the preparation and printing of a book takes time and as this volume should be available at the conf- ence, the Local Organizing Committee had to set up a very ambitious time sch- ule which could not be met by all contributors.

This book describes in a detailed fashion the application of hybrid intelligent systems using soft computing techniques for intelligent control and mobile robotics. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The prudent combination of SC techniques can produce powerful hybrid intelligent systems that are capable of solving real-world problems. This is illustrated in this book with a wide range of applications, with particular emphasis in intelligent control and mobile robotics. The book is organized in five main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theory and algorithms, which are basically papers that propose new models and concepts, which can be the basis for achieving intelligent control and mobile robotics. The second part contains papers with the main theme of intelligent control, which are basically papers using bio-inspired techniques, like evolutionary algorithms and neural networks, for achieving intelligent control of non-linear plants. The third part contains papers with the theme of optimization of fuzzy controllers, which basically consider the application of bio-inspired optimization methods to automate the de-sign process of optimal type-1 and type-2 fuzzy controllers. The fourth part contains papers that deal with the application of SC techniques in times series prediction and intelligent agents. The fifth part contains papers with the theme of computer vision and robotics, which are papers considering soft computing methods for applications related to vision and robotics.

Sliding-mode Control of PEM Fuel Cells demonstrates the application of higher-order sliding-mode control to PEMFC dynamics showing the advantages of sliding modes. The book introduces the theory of fuel cells and sliding-mode control. It contextualises PEMFCs both in terms of their development and within the hydrogen economy and today's energy production situation as a whole. It then discusses fuel-cell operation principles, the mathematical background of high-order sliding-mode control and to a feasibility study for the use of sliding modes in the control of an automotive fuel stack. Part II presents experimental results of sliding-mode-control application to laboratory fuel cells and deals with subsystem-based modelling, detailed design, and observability and controllability. Simulation results are contrasted with empirical data and performance, robustness and implementation issues are treated in depth. Possibilities for future research are also laid out.

This book contains an edited collection of eighteen contributions on soft and hard computing techniques and their applications to autonomous robotic systems. Each contribution has been exclusively written for this volume by a leading researcher. The volume demonstrates the various ways that the soft computing and hard computing techniques can be used in different integrated manners to better develop autonomous robotic systems that can perform various tasks of vision, perception, cognition, thinking, pattern recognition, decision-making, and reasoning and control, amongst others. Each chapter of the book is self-contained and points out the future direction of research.

"It is a must reading for students and researchers interested in exploring the potentials of the fascinating field that will form the basis for the design of the intelligent machines of the future"
(Madan M. Gupta)

Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics: Data science: as an alternative to time series, price movements in a market can be summarised as directional changes Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed Algorithmic trading: regime tracking information can help us to design trading algorithms It will be of great interest to researchers in computational finance, machine learning and data science. About the Authors Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019. Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.

Although the problem of nonlinear controller design is as old as that of linear controller design, the systematic design methods framed in response are more sparse. Given the range and complexity of nonlinear systems, effective new methods of control design are therefore of significant importance. Dynamic Surface Control of Uncertain Nonlinear Systems provides a theoretically rigorous and practical introduction to nonlinear control design. The convex optimization approach applied to good effect in linear systems is extended to the nonlinear case using the new dynamic surface control (DSC) algorithm developed by the authors. A variety of problems - DSC design, output feedback, input saturation and fault-tolerant control among them - are considered. The inclusion of applications material demonstrates the real significance of the DSC algorithm, which is robust and easy to use, for nonlinear systems with uncertainty in automotive and robotics. Written for the researcher and graduate student of nonlinear control theory, this book will provide the applied mathematician and engineer alike with a set of powerful tools for nonlinear control design. It will also be of interest to practitioners working with a mechatronic systems in aerospace, manufacturing and automotive and robotics, milieux.

This edited monograph provides a comprehensive and in-depth analysis of sliding mode control, focusing on event-triggered implementation. The technique allows to prefix the steady-state bounds of the system, and this is independent of any boundary disturbances. The idea of event-triggered SMC is developed for both single input / single output and multi-input / multi-output linear systems. Moreover, the reader learns how to apply this method to nonlinear systems. The book primarily addresses research experts in the field of sliding mode control, but the book may also be beneficial for graduate students.

A manipulator, or 'robot', consists of a series of bodies (links) connected by joints to form a spatial mechanism. Usually the links are connected serially to form an open chain. The joints are either revolute (rotary) or prismatic (telescopic), various combinations of the two giving a wide va riety of possible configurations. Motive power is provided by pneumatic, hydraulic or electrical actuation of the joints. The robot arm is distinguished from other active spatial mechanisms by its reprogrammability. Therefore, the controller is integral to any de scription of the arm. In contrast with many other controlled processes (e. g. batch reactors), it is possible to model the dynamics of a ma nipulator very accurately. Unfortunately, for practical arm designs, the resulting models are complex and a considerable amount of research ef fort has gone into improving their numerical efficiency with a view to real time solution 32,41,51,61,77,87,91]. In recent years, improvements in electric motor technology coupled with new designs, such as direct-drive arms, have led to a rapid increase in the speed and load-carrying capabilities of manipulators. However, this has meant that the flexibility of the nominally rigid links has become increasingly significant. Present generation manipulators are limited to a load-carrying capacity of typically 5-10% of their own weight by the requirement of rigidity. For example, the Cincinatti-Milicron T3R3 robot weighs more than 1800 kg but has a maximum payload capacity of 23 kg."

Enhanced e-book includes videos Many books have been written on modelling, simulation and control of four-wheeled vehicles (cars, in particular). However, due to the very specific and different dynamics of two-wheeled vehicles, it is very difficult to reuse previous knowledge gained on cars for two-wheeled vehicles. Modelling, Simulation and Control of Two-Wheeled Vehicles presents all of the unique features of two-wheeled vehicles, comprehensively covering the main methods, tools and approaches to address the modelling, simulation and control design issues. With contributions from leading researchers, this book also offers a perspective on the future trends in the field, outlining the challenges and the industrial and academic development scenarios. Extensive reference to real-world problems and experimental tests is also included throughout. Key features: * The first book to cover all aspects of two-wheeled vehicle dynamics and control * Collates cutting-edge research from leading international researchers in the field * Covers motorcycle control a subject gaining more and more attention both from an academic and an industrial viewpoint * Covers modelling, simulation and control, areas that are integrated in two-wheeled vehicles, and therefore must be considered together in order to gain an insight into this very specific field of research * Presents analysis of experimental data and reports on the results obtained on instrumented vehicles. Modelling, Simulation and Control of Two-Wheeled Vehicles is a comprehensive reference for those in academia who are interested in the state of the art of two-wheeled vehicles, and is also a useful source of information for industrial practitioners.

Bionics evolved in the 1960s as a framework to pursue the development of artificial systems based on the study of biological systems. Numerous disciplines and technologies, including artificial intelligence and learningdevices, information processing, systems architecture and control, perception, sensory mechanisms, and bioenergetics, contributed to bionics research. This volume is based on a NATO Advanced Research Workshop within the Special Programme on Sensory Systems for Robotic Control, held in Il Ciocco, Italy, in June 1989. A consensus emerged at the workshop, and is reflected in the book, on the value of learning from nature in order to derive guidelines for the design of intelligent machines which operate in unstructured environments. The papers in the book are grouped into seven chapters: vision and dynamic systems, hands and tactile perception, locomotion, intelligent motor control, design technologies, interfacing robots to nervous systems, and robot societies and self-organization.

This book on autonomous road-following vehicles brings together twenty years of innovation in the field. The book uniquely details an approach to real-time machine vision for the understanding of dynamic scenes, viewed from a moving platform that begins with spatio-temporal representations of motion for hypothesized objects whose parameters are adjusted by well-known prediction error feedback and recursive estimation techniques.

This book presents the singular configurations associated with a robot mechanism, together with robust methods for their computation, interpretation, and avoidance path planning. Having such methods is essential as singularities generally pose problems to the normal operation of a robot, but also determine the workspaces and motion impediments of its underlying mechanical structure. A distinctive feature of this volume is that the methods are applicable to nonredundant mechanisms of general architecture, defined by planar or spatial kinematic chains interconnected in an arbitrary way. Moreover, singularities are interpreted as silhouettes of the configuration space when seen from the input or output spaces. This leads to a powerful image that explains the consequences of traversing singular configurations, and all the rich information that can be extracted from them. The problems are solved by means of effective branch-and-prune and numerical continuation methods that are of independent interest in themselves. The theory can be put into practice as well: a companion web page gives open access to implementations of the algorithms and the corresponding input files. Using them, the reader can gain hands-on experience on the topic, or analyse new mechanisms beyond those examined in the text. Overall, the book contributes new tools for robot design, and constitutes a single reference source of knowledge that is otherwise dispersed in the literature.

Most practical processes such as chemical reactor, industrial furnace, heat exchanger, etc., are nonlinear stochastic systems, which makes their con trol in general a hard problem. Currently, there is no successful design method for this class of systems in the literature. One common alterna tive consists of linearizing the nonlinear dynamical stochastic system in the neighborhood of an operating point and then using the techniques for linear systems to design the controller. The resulting model is in general an approximation of the real behavior of a dynamical system. The inclusion of the uncertainties in the model is therefore necessary and will certainly improve the performance of the dynamical system we want to control. The control of uncertain systems has attracted a lot of researchers from the control community. This topic has in fact dominated the research effort of the control community during the last two decades, and many contributions have been reported in the literature. Some practical dynamical systems have time delay in their dynamics, which makes their control a complicated task even in the deterministic case. Recently, the class ofuncertain dynamical deterministic systems with time delay has attracted some researchers, and some interesting results have been reported in both deterministic and stochastic cases. But wecan't claim that the control problem ofthis class ofsystems is completely solved; more work must be done for this class of systems."

Underwater Robots reports on the latest progress in underwater robotics. In spite of its importance, the ocean is generally overlooked, since we focus more of our attention on land and atmospheric issues. We have not yet been able to explore the full depths of the ocean and its resources. The deep oceans range between 19000 to 36000 feet. At a mere 33-foot depth, the pressure is twice the normal atmospheric pressure of 29.4 psi. This obstacle, compounded with other complex issues due to the unstructured and hazardous environment, makes it difficult to travel in the ocean even though today's technologies allow humans to land on the moon. Only recently, we discovered by using manned submersibles that a large amount of carbon dioxide comes from the sea-floor and that extraordinary groups of organisms live in hydrothermal vent areas. On March 24, 1995 Kaiko (a remotely operated vehicle) navigated the deepest region of the ocean, the Mariana Trough. This vehicle successfully dived to a depth of 33000 feet and instantly showed scenes from the trench through a video camera. New tools like this enable us to gain knowledge of mysterious places. However, extensive use of manned submersibles and remotely operated vehicles is limited to a few applications because of very high operational costs, operator fatigue and safety issues. In spite of these hindrances, the demand for advanced underwater robot technologies is growing and will eventually arrive at fully autonomous, specialized, reliable underwater robotic vehicles. Underwater Robots is an edited volume of peer-reviewed original research comprising thirteen invited contributions by leading researchers. This research work has also been published as a special issue of Autonomous Robots (Volume 3, Numbers 2 and 3).

Harold Lewis applied a cross-disciplinary approach in his highly accessible discussion of fuzzy control concepts. With the aid of fifty-seven illustrations, he thoroughly presents a unique mathematical formalism to explain the workings of the fuzzy inference engine and a novel test plant used in the research. Additionally, the text posits a new viewpoint on why fuzzy control is more popular in some countries than in others. A direct and original view of Japanese thinking on fuzzy control methods, based on the author's personal knowledge of - and association with - Japanese fuzzy research, is also included.

This book focuses on the design of a multi-criteria automated vehicle longitudinal control system as an enhancement of the adaptive cruise control system. It analyses the effects of various parameters on the average traffic speed and the traction force of the vehicles in mixed traffic from a macroscopic point of view, and also demonstrates why research and development in speed control and predictive cruise control is important. The book also summarises the main steps of the system's robust control design, from the modelling to its synthesis, and discusses both the theoretical background and the practical computation method of the control invariant sets. The book presents the analysis and verification of the system both in a simulation environment and under real-world conditions. By including the systematic design of the predictive cruise control using road and traffic information, it shows how optimization criteria can lead to multiobjective solutions, and the advanced optimization and control design methods required. The book focuses on a particular method by which the unfavourable effect of the traffic flow consideration can be reduced. It also includes simulation examples in which the speed design is performed, while the analysis is carried out in simulation and visualization environments. This book is a valuable reference for researchers and control engineers working on traffic control, vehicle control and control theory. It is also of interest to students and academics as it provides an overview of the strong interaction between the traffic flow and an individual vehicle cruising from both a microscopic and a macroscopic point of view.

Reviews different machine learning and deep learning techniques with a biomedical perspective Provides the relevant case studies that demonstrate applicability of different AI techniques Explain different kinds of inputs like various image modalities, biomedical signals types, etc. Covers the latest trends of AI-based biomedical domains including IoT, drug discovery, biomechanics, robotics, electronic health records, etc. Discusses the research challenges and opportunities in AI and biomedical domain

This book presents the proceedings of the Third International Conference on Electrical Engineering and Control (ICEECA2017). It covers new control system models and troubleshooting tips, and also addresses complex system requirements, such as increased speed, precision and remote capabilities, bridging the gap between the complex, math-heavy controls theory taught in formal courses, and the efficient implementation required in real-world industry settings. Further, it considers both the engineering aspects of signal processing and the practical issues in the broad field of information transmission and novel technologies for communication networks and modern antenna design. This book is intended for researchers, engineers, and advanced postgraduate students in control and electrical engineering, computer science, signal processing, as well as mechanical and chemical engineering.

In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.

System-Level Synthesis deals with the concurrent design of electronic applications, including both hardware and software. The issue has become the bottleneck in the design of electronic systems, including both hardware and software, in several major industrial fields, including telecommunications, automotive and aerospace engineering. The major difficulty with the subject is that it demands contributions from several research fields, including system specification, system architecture, hardware design, and software design. Most existing book cover well only a few aspects of system-level synthesis. The present volume presents a comprehensive discussion of all the aspects of system-level synthesis. Each topic is covered by a contribution written by an international authority on the subject.

Fuzzy Control of Industrial Systems: Theory and Applications presents the basic theoretical framework of crisp and fuzzy set theory, relating these concepts to control engineering based on the analogy between the Laplace transfer function of linear systems and the fuzzy relation of a nonlinear fuzzy system. Included are generic aspects of fuzzy systems with an emphasis on the many degrees of freedom and its practical design implications, modeling and systems identification techniques based on fuzzy rules, parametrized rules and relational equations, and the principles of adaptive fuzzy and neurofuzzy systems. Practical design aspects of fuzzy controllers are covered by the detailed treatment of fuzzy and neurofuzzy software design tools with an emphasis on iterative fuzzy tuning, while novel stability limit testing methods and the definition and practical examples of the new concept of collaborative control systems are also given. In addition, case studies of successful applications in industrial automation, process control, electric power technology, electric traction, traffic engineering, wastewater treatment, manufacturing, mineral processing and automotive engineering are also presented, in order to assist industrial control systems engineers in recognizing situations when fuzzy and neurofuzzy would offer certain advantages over traditional methods, particularly in controlling highly nonlinear and time-variant plants and processes.

This is a book about how management and control decisions are made by persons who collaborate and possibly use the support of an information system. The decision is the result of human conscious activities aiming at choosing a course of action for attaining a certain objective (or a set of objectives). The act of collaboration implies that several entities who work together and share responsibilities to jointly plan, implement and evaluate a program of activities to achieve the common goals. The book is intended to present a balanced view of the domain to include both well-established concepts and a selection of new results in the domains of methods and key technologies. It is meant to answer several questions, such as: a) "How are evolving the business models towards the ever more collaborative schemes?"; b) "What is the role of the decision-maker in the new context?" c) "What are the basic attributes and trends in the domain of decision-supporting information systems?"; d) "Which are the basic methods to aggregate the individual preferences?" e)"What is the impact of modern information and communication technologies on the design and usage of decision support systems for groups of people?".

"In other words, the invention of a mechanism will be to the scientific kinematist a synthetic problem, - which he can solve by the use of systematic, if also difficult, methods." Reuleaux, F., Theoretische Kinematik, Braunschweig: Vieweg, 1875 Reuleaux, F., The Kinematics of Machinery, London: Macmillan, 1876 and New York: Dover, 1963 (translated by A.B.W. Kennedy) This book represents the third part of a larger work dedicated to the structural synthesis of parallel robots. Part 1 (Gogu 2008a) presented the methodology of structural synthesis and the systematisation of structural solutions of simple and complex limbs with two to six degrees of connectivity systematically generated by the structural synthesis approach. Part 2 (Gogu 2009a) presented structural solutions of translational parallel robotic manipulators with two and three degrees of mobility. This book focuses on various topologies of parallel robotic manipulators with planar motion of the moving platform systematically generated by using the structural synthesis approach proposed in Part 1. The originality of this work resides in the fact that it combines the new formulae for mobility connectivity, redundancy and overconstraints, and the evolutionary morphology in a unified approach of structural synthesis giving interesting innovative solutions for parallel mechanisms.