This edited volume contains sixteen research articles and presents recent and pressing issues in stochastic processes, control theory, differential games, optimization, and their applications in finance, manufacturing, queueing networks, and climate control. One of the salient features is that the book is highly multi-disciplinary. It assembles experts from the fields of operations research, control theory and optimization, stochastic analysis, and financial engineering to review and substantially update the recent progress in these fields. Another distinct characteristic of the book is that all papers are motivated by applications in which optimization, control, and stochastics are inseparable. The book will be a timely addition to the literature and will be of interest to people working in the aforementioned fields.

Most importantly, this volume is dedicated to Professor Suresh Sethi on the occasion of his 60th birthday. In view of his fundamental contributions, his distinguished career, his substantial achievements, his influence on the areas of control theory and applications, operations research, and management science, and his dedication to the scientific community, a number of leading experts in the fields of optimization, control, and operation management, have contributed to this volume in honor of him.

Increasing demands on the output performance, exhaust emissions, and fuel consumption necessitate the development of a new generation of automotive engine functionality. This monograph is written by a long year developmental automotive engineer and offers a wide coverage of automotive engine control and estimation problems and its solutions. It addresses idle speed control, cylinder flow estimation, engine torque and friction estimation, engine misfire and CAM profile switching diagnostics, as well as engine knock detection. The book provides a wide and well structured collection of tools and new techniques useful for automotive engine control and estimation problems such as input estimation, composite adaptation, threshold detection adaptation, real-time algorithms, as well as the very important statistical techniques. It demonstrates the statistical detection of engine problems such as misfire or knock events and how it can be used to build a new generation of robust engine functionality. This book will be useful for practising automotive engineers, black belts working in the automotive industry as well as for lecturers and students since it provides a wide coverage of engine control and estimation problems, detailed and well structured descriptions of useful techniques in automotive applications and future trends and challenges in engine functionality.

Microsystems are an important success factor in the automobile industry. In order to fulfil the customers' requests for safety convenience and vehicle economy, and to satisfy environmental requirements, microsystems are becoming indispensable. Thus a large number of microsystem applications came into the discussion. With the international conference AMAA 2000, VDI/VDE-IT provides a platform for the discussion of all MST relevant components for automotive applications. The conference proceedings gather the papers by authors from automobile suppliers and manufacturers.

In the mathematical treatment of many problems which arise in physics, economics, engineering, management, etc., the researcher frequently faces two major difficulties: infinite dimensionality and randomness of the evolution process. Infinite dimensionality occurs when the evolution in time of a process is accompanied by a space-like dependence; for example, spatial distribution of the temperature for a heat-conductor, spatial dependence of the time-varying displacement of a membrane subject to external forces, etc. Randomness is intrinsic to the mathematical formulation of many phenomena, such as fluctuation in the stock market, or noise in communication networks. Control theory of distributed parameter systems and stochastic systems focuses on physical phenomena which are governed by partial differential equations, delay-differential equations, integral differential equations, etc., and stochastic differential equations of various types. This has been a fertile field of research with over 40 years of history, which continues to be very active under the thrust of new emerging applications. Among the subjects covered are: Control of distributed parameter systems; Stochastic control; Applications in finance/insurance/manufacturing; Adapted control; Numerical approximation . It is essential reading for applied mathematicians, control theorists, economic/financial analysts and engineers.

The Engineering of Complex Real-Time Computer Control Systems brings together in one place important contributions and up-to-date research results in this important area. The Engineering of Complex Real-Time Computer Control Systems serves as an excellent reference, providing insight into some of the most important research issues in the field.

The book largely represents the extended version of select papers from the Inter- tional Conference on Intelligent Unmanned System ICIUS 2007 which was jointly organized by the Center for Unmanned System Studies at Institut Teknologi Bandung, Artificial Muscle Research Center at Konkuk University and Institute of Bio-inspired Structure and Surface Engineering, Nanjing University of Aeronautics and Astrona- ics. The joint-event was the 3rd conference extending from International Conference on Emerging System Technology (ICEST) in 2005 and International Conference on Technology Fusion (ICTF) in 2006 both conducted in Seoul. ICIUS 2007 was focused on both theory and application primarily covering the topics on robotics, autonomous vehicles and intelligent unmanned technologies. The conference was arranged into three parallel symposia with the following scope of topics: Unmanned Systems: Micro air vehicle, Underwater vehicle, Micro-satellite, - manned aerial vehicle, Multi-agent systems, Autonomous ground vehicle, Blimp, Swarm intelligence, learning and control Robotics and Biomimetics: Artificial muscle actuators, Smart sensors, Design and applications of MEMS/NEMS system, Intelligent robot system, Evolutionary al- rithm, Control of biological systems, AI and expert systems, Biological learning control systems, Neural networks, Genetic algorithm Control and Intelligent System: Distributed intelligence, Distributed/decentralized intelligent control, Distributed or decentralized control methods, Distributed and - bedded systems, Embedded intelligent control, Complex systems, Discrete event s- tems, Hybrid systems, Networked control systems, Delay systems, Fuzzy systems, Identification and estimation, Nonlinear systems, Precision motion control, Control applications, Control engineering education.

To describe the true behavior of most real-world systems with sufficient accuracy, engineers have to overcome difficulties arising from their lack of knowledge about certain parts of a process or from the impossibility of characterizing it with absolute certainty. Depending on the application at hand, uncertainties in modeling and measurements can be represented in different ways. For example, bounded uncertainties can be described by intervals, affine forms or general polynomial enclosures such as Taylor models, whereas stochastic uncertainties can be characterized in the form of a distribution described, for example, by the mean value, the standard deviation and higher-order moments.

The goal of this Special Volume on "Modeling, Design, and Simulation of Systems with Uncertainties" is to cover modern methods for dealing with the challenges presented by imprecise or unavailable information. All contributions tackle the topic from the point of view of control, state and parameter estimation, optimization and simulation.

Thematically, this volume can be divided into two parts. In the first we present works highlighting the theoretic background and current research on algorithmic approaches in the field of uncertainty handling, together with their reliable software implementation. The second part is concerned with real-life application scenarios from various areas including but not limited to mechatronics, robotics, and biomedical engineering.

This book is an up-to-date self-contained compendium of the research carried out by the authors on model-based diagnosis of a class of discrete-event systems called active systems. After defining the diagnosis problem, the book copes with a variety of reasoning mechanisms that generate the diagnosis, possibly within a monitoring setting. The book is structured into twelve chapters, each of which has its own introduction and concludes with bibliographic notes and itemized summaries. Concepts and techniques are presented with the help of numerous examples, figures, and tables, and when appropriate these concepts are formalized into propositions and theorems, while detailed algorithms are expressed in pseudocode. This work is primarily intended for researchers, professionals, and graduate students in the fields of artificial intelligence and control theory.

This book provides basic theories and implementations using SCILAB open-source software for digital images. The book simplifies image processing theories and well as implementation of image processing algorithms, making it accessible to those with basic knowledge of image processing. This book includes many SCILAB programs at the end of each theory, which help in understanding concepts. The book includes more than sixty SCILAB programs of the image processing theory. In the appendix, readers will find a deeper glimpse into the research areas in the image processing.

In the last ten years, a true explosion of investigations into fuzzy modeling and its applications in control, diagnostics, decision making, optimization, pattern recognition, robotics, etc. has been observed. The attraction of fuzzy modeling results from its intelligibility and the high effectiveness of the models obtained. Owing to this the modeling can be applied for the solution of problems which could not be solved till now with any known conventional methods. The book provides the reader with an advanced introduction to the problems of fuzzy modeling and to one of its most important applications: fuzzy control. It is based on the latest and most significant knowledge of the subject and can be used not only by control specialists but also by specialists working in any field requiring plant modeling, process modeling, and systems modeling, e.g. economics, business, medicine, agriculture,and meteorology.

It is a great pleasure for me to introduce this book which has the main ambition to make thermodynamics more directly accessible to engineers and physicists by stressing the analogies with the other physical domains; this science has discouraged more than a few students. The book comes from the meeting of two persons: 1. Jean Thoma, inventor of hydrostatic machines and transmissions, pro fessor at the University of Waterloo (Canada), expert in simulation and pilgrim for the promotion of bond graphs around the world. 2. Belkacem Ould Bouamama, associated professor at the University of Science and Technology in Lille, France, specialist in industrial control and seduced by the richness and structure of the bond graph method. Thermodynamics is a difficult subject; its concepts like entropy, enthalpy, etc. are not intuitive and often very abstract. For this reason, it is current practice to neglect the thermal aspects, although they are necessarily there in all physical phenomena, and to use isothermal models. This is equivalent to think that the system is immersed in an infinite temperature reservoir and maintains its temperature constant even if it receives or dissipates electric and other type of energy. For heat transfer and variable temperature, if it should be included, the classical approach is to study the changes between equilibrium states, and not the process itself, which is more a thermostatic than a thermodynamic approach. This is justified when only the constraints of equilibrium state must be satisfied."

This book incorporates recent advances in the design of feedback laws to the purpose of globally stabilizing nonlinear systems via state or output feedback. It is a continuation of the first volume by Alberto Isidori on Nonlinear Control Systems. Specifically this second volume will cover: *Stability analysis of interconnected nonlinear systems. The notion of Input-to-State stability and its role in analysing stability of cascade-connected or feedback-connected systems. The notion of dissipativity and its consequences (passivity and "gain"). *Robust stabilization in the case of parametric uncertainties. The case of state feedback: global or semi-global stabilization. The case of output feedback: semi-global stabilization. *Robust stabilization in the case of unstructured perturbations. Feedback design via the small-gain approach. Robust semi-global stabilization via output feedback. *Methods for asymptotic tracking, disturbance rejection and model following. Global and semi-global analysis. *Normal forms for multi-input multi-output nonlinear systems form a global point of view. Their role in feedback design.

Providing a wide variety of technologies for ensuring the safety and dependability of cyber-physical systems (CPS), this book offers a comprehensive introduction to the architecture-centric modeling, analysis, and verification of CPS. In particular, it focuses on model driven engineering methods including architecture description languages, virtual prototyping, and formal analysis methods. CPS are based on a new design paradigm intended to enable emerging software-intensive systems. Embedded computers and networks monitor and control the physical processes, usually with the help of feedback loops where physical processes affect computations and vice versa. The principal challenges in system design lie in this constant interaction of software, hardware and physics. Developing reliable CPS has become a critical issue for the industry and society, because many applications such as transportation, power distribution, medical equipment and tele-medicine are dependent on CPS. Safety and security requirements must be ensured by means of powerful validation tools. Satisfying such requirements, including quality of service, implies having formally proven the required properties of the system before it is deployed. The book is concerned with internationally standardized modeling languages such as AADL, SysML, and MARTE. As the effectiveness of the technologies is demonstrated with industrial sample cases from the automotive and aerospace sectors, links between the methods presented and industrial problems are clearly understandable. Each chapter is self-contained, addressing specific scientific or engineering problems, and identifying further issues. In closing, it includes perspectives on future directions in CPS design from an architecture analysis viewpoint.

Over the past decades, although stochastic system control has been studied intensively within the field of control engineering, all the modelling and control strategies developed so far have concentrated on the performance of one or two output properties of the system. such as minimum variance control and mean value control. The general assumption used in the formulation of modelling and control strategies is that the distribution of the random signals involved is Gaussian. In this book, a set of new approaches for the control of the output probability density function of stochastic dynamic systems (those subjected to any bounded random inputs), has been developed. In this context, the purpose of control system design becomes the selection of a control signal that makes the shape of the system outputs p.d.f. as close as possible to a given distribution. The book contains material on the subjects of: - Control of single-input single-output and multiple-input multiple-output stochastic systems; - Stable adaptive control of stochastic distributions; - Model reference adaptive control; - Control of nonlinear dynamic stochastic systems; - Condition monitoring of bounded stochastic distributions; - Control algorithm design; - Singular stochastic systems.
A new representation of dynamic stochastic systems is produced by using B-spline functions to descripe the output p.d.f. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Piecewise Linear (PL) approximation of non-linear behaviour is a well-known technique in synthesis and analysis of electrical networks. However, the PL description should be efficient in data storage and the description should allow simple retrieval of the stored information. Furthermore, it would be useful if the model description could handle a large class of piecewise linear mappings. Piecewise Linear Modeling and Analysis explains in detail all possible model descriptions for efficiently storing piecewise linear functions, starting with the Chua descriptions. Detailed explanation on how the model parameter can be obtained for a given mapping is provided and demonstrated by examples. The models are ranked to compare them and to show which model can handle the largest class of PL mappings. All model descriptions are implicitly related to the Linear Complementarity Problem and most solution techniques for this problem, like Katzenelson and Lemke, are discussed according to examples that are explained in detail. To analyse PL electrical networks a simulator is mandatory. Piecewise Linear Modeling and Analysis provides a detailed outline of a possible PL simulator, including pseudo-programming code. Several simulation domains like transient, AC and distortion are discussed. The book explains the attractive features of PL simulators with respect to mixed-level and mixed-signal simulation while paying due regard also to hierarchical simulation. Piecewise Linear Modeling and Analysis shows in detail how many existing components in electrical networks can be modeled. These range from digital logic and analog basic elements such as transistors to complex systems like Phase-Locked Loops and detection systems. Simulation results are also provided. The book concludes with a discussion on how to find multiple solutions for PL functions or networks. Again, the most common techniques are outlined using clear examples. Piecewise Linear Modeling and Analysis is an indispensable guide for researchers and designers interested in network theory, network synthesis and network analysis.

This book surveys the well-known results and also presents a series of original results on the mathematical modeling of social networks, focusing on models of informational influence, control and confrontation. Online social networks are intended for communication, opinion exchange and information acquisition for their members, but recently, online social networks have been intensively used as the objects and means of informational control and an arena of informational confrontation. They have become a powerful informational influence tool, particularly for the manipulation of individuals, social groups and society as a whole, as well as a battlefield of information warfare (cyberwars). This book aimed at under- and postgraduate university students as well as experts in information technology and modeling of social systems and processes.

This book presents an authoritative collection of contributions by researchers from 16 different countries (Austria, Chile, Georgia, Germany, Mexico, Norway, P.R. of China, Poland, North Macedonia, Romania, Russia, Spain, Turkey, Ukraine, the United Kingdom and United States) that report on recent developments and new directions in advanced control systems, together with new theoretical findings, industrial applications and case studies on complex engineering systems. This book is dedicated to Professor Vsevolod Mykhailovych Kuntsevich, an Academician of the National Academy of Sciences of Ukraine, and President of the National Committee of the Ukrainian Association on Automatic Control, in recognition of his pioneering works, his great scientific and scholarly achievements, and his years of service to many scientific and professional communities, notably those involved in automation, cybernetics, control, management and, more specifically, the fundamentals and applications of tools and techniques for dealing with uncertain information, robustness, non-linearity, extremal systems, discrete control systems, adaptive control systems and others. Covering essential theories, methods and new challenges in control systems design, the book is not only a timely reference guide but also a source of new ideas and inspirations for graduate students and researchers alike. Its 15 chapters are grouped into four sections: (a) fundamental theoretical issues in complex engineering systems, (b) artificial intelligence and soft computing for control and decision-making systems, (c) advanced control techniques for industrial and collaborative automation, and (d) modern applications for management and information processing in complex systems. All chapters are intended to provide an easy-to-follow introduction to the topics addressed, including the most relevant references. At the same time, they reflect various aspects of the latest research work being conducted around the world and, therefore, provide information on the state of the art.

This book presents an operator theoretic approach to robust control analysis for linear time-varying systems. It emphasizes the conceptual similarity with the H control theory for time-invariant systems and at the same time clarifies the major difficulties confronted in the time varying case. The necessary operator theory is developed from first principles and the book is as self-contained as possible. After presenting the necessary results from the theories of Toeplitz operators and nest algebras, linear systems are defined as input- output operators and the relationship between stabilization and the existance of co-prime factorizations is described. Uniform optimal control problems are formulated as model-matching problems and are reduced to four block problems. Robustness is considered both from the point of view of fractional representations and the "time varying gap" metric, and the relationship between these types of uncertainties is clarified. The book closes with the solution of the orthogonal embedding problem for time varying contractive systems. This book will be useful to both mathematicians interested in the potential applications of operator theory in control and control engineers who wish to deal with some of the more mathematically sophisticated extension of their work.

This monograph presents a new analytical approach to the design of proportional-integral-derivative (PID) controllers for linear time-invariant plants. The authors develop a computer-aided procedure, to synthesize PID controllers that satisfy multiple design specifications. A geometric approach, which can be used to determine such designs methodically using 2- and 3-D computer graphics is the result. The text expands on the computation of the complete stabilizing set previously developed by the authors and presented here. This set is then systematically exploited to achieve multiple design specifications simultaneously. These specifications include classical gain and phase margins, time-delay tolerance, settling time and H-infinity norm bounds. The results are developed for continuous- and discrete-time systems. An extension to multivariable systems is also included. Analytical Design of PID Controllers provides a novel method of designing PID controllers, which makes it ideal for both researchers and professionals working in traditional industries as well as those connected with unmanned aerial vehicles, driverless cars and autonomous robots.

Complexity and dynamic order of controlled engineering systems is constantly increasing. Complex large scale systems (where "large" reflects the system's order and not necessarily its physical size) appear in many engineering fields, such as micro-electromechanics, manufacturing, aerospace, civil engineering and power engineering. Modeling of these systems often result in very high-order models imposing great challenges to the analysis, design and control problems. "Efficient Modeling and Control of Large-Scale Systems" compiles state-of-the-art contributions on recent analytical and computational methods for addressing model reduction, performance analysis and feedback control design for such systems. Also addressed at length are new theoretical developments, novel computational approaches and illustrative applications to various fields, along with: - An interdisciplinary focus emphasizing methods and approaches that can be commonly applied in various engineering fields -Examinations of applications in various fields including micro-electromechanical systems (MEMS), manufacturing processes, power networks, traffic control "Efficient Modeling and Control of Large-Scale Systems" is an ideal volume for engineers and researchers working in the fields of control and dynamic systems.

This edited book aims at presenting current research activities in the field of robust variable-structure systems. The scope equally comprises highlighting novel methodological aspects as well as presenting the use of variable-structure techniques in industrial applications including their efficient implementation on hardware for real-time control. The target audience primarily comprises research experts in the field of control theory and nonlinear dynamics but the book may also be beneficial for graduate students.

The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems.

A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered.

All the concepts described in this book are illustrated by both simple academic illustrative examples and practical applications.

"

"Hybrid Predictive Control for Dynamic Transport Problems" develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems. Operational and service policies are considered, as well as cost reduction. The control structure is based on a sound definition of the key variables and their evolution. A flexible objective function able to capture the predictive behaviour of the system variables is described. Coupled with efficient algorithms, mainly drawn from area of computational intelligence, this is shown to optimize performance indices for real-time applications. The framework of the proposed predictive control methodology is generic and, being able to solve nonlinear mixed integer optimization problems dynamically, is readily extendable to other industrial processes.

The main topics of this book are:

. hybrid predictive control (HPC) design based on evolutionary multiobjective optimization (EMO);

. HPC based on EMO for dial-a-ride systems; and

. HPC based on EMO for operational decisions in public transport systems.

"Hybrid Predictive Control for Dynamic Transport Problems" is a comprehensive analysis of HPC and its application to dynamic transport systems. Introductory material on evolutionary algorithms is presented in summary in an appendix. The text will be of interest to control and transport engineers working on the operational optimization of transport systems and to academic researchers working with hybrid systems. The potential applications of the generic methods presented here to other process fields will make the book of interest to a wider group of researchers, scientists and graduate students working in other control-related disciplines."

The ability of fuzzy systems to provide shades of gray between "on or off" and "yes or no" is ideally suited to many of todaya (TM)s complex industrial control systems. The static fuzzy systems usually discussed in this context fail to take account of inputs outside a pre-set range and their off-line nature makes tuning complicated.

Advanced Fuzzy Logic Technologies in Industrial Applications addresses the problem by introducing a dynamic, on-line fuzzy inference system. In this system membership functions and control rules are not determined until the system is applied and each output of its lookup table is calculated based on current inputs.

The tuning process is a major focus in this volume because it is the most difficult stage in fuzzy control application. Using new methods such as A-law technique, histogram equalization and the Bezier-based method, all detailed here, the tuning process can be significantly simplified and control performance improved.

The other great strength of this book lies in the range and contemporaneity of its applications and examples which include: laser tracking and control; robot calibration; image processing and pattern recognition; medical engineering; audio systems; autonomous underwater vehicles and data mining.

Advanced Fuzzy Logic Technologies in Industrial Applications is written to be easily understood by readers not having specialized knowledge of fuzzy logic and intelligent control. Design and application engineers and project managers working in control, as well as researchers and graduate students in the discipline will find much to interest them in this work.

This book aims at addressing the challenges of contemporary manufacturing in Industry 4.0 environment and future manufacturing (aka Industry 5.0), by implementing soft computing as one of the major sub-fields of artificial intelligence. It contributes to development and application of the soft computing systems, including links to hardware, software and enterprise systems, in resolving modern manufacturing issues in complex, highly dynamic and globalized industrial circumstances. It embraces heterogeneous complementary aspects, such as control, monitoring and modeling of different manufacturing tasks, including intelligent robotic systems and processes, addressed by various machine learning and fuzzy techniques; modeling and parametric optimization of advanced conventional and non-conventional, eco-friendly manufacturing processes by using machine learning and evolutionary computing techniques; cybersecurity framework for Internet of Things-based systems addressing trustworthiness and resilience in machine-to-machine and human-machine collaboration; static and dynamic digital twins integration and synchronization in a smart factory environment; STEP-NC technology for a smart machine vision system, and integration of Open CNC with Service-Oriented Architecture for STEP-NC monitoring system in a smart manufacturing. Areas of interest include but are not limited to applications of soft computing to address the following: dynamic process/system modeling and simulation, dynamic process/system parametric optimization, dynamic planning and scheduling, smart, predictive maintenance, intelligent and autonomous systems, improved machine cognition, effective digital twins integration, human-machine collaboration, robots, and cobots.