Control systems design methodologies have long suffered the traditional and myopic dichotomy between time and frequency domain approaches, each of them being specialized to cope with only scarcely overlapping performance requirements. This book is aimed at bridging the two approaches by presenting design methodologies based on the minimization of a norm (H2/H() of a suitable transfer function. A distinctive feature of these techniques is the fact that they do not create only one solution to the design problem, instead they provide a whole set of admissible solutions which satisfy a constraint on the maximum deterioration of the performance index.
A systematic book on this topic is long overdue. Self-contained and practical in its approach, Control Theory and Design enables the reader to use the relevant techniques in various real-life applications. The text covers the basic facts of robustcontrol and theory as well as more recent achievements, such as robust stability and robust performance in presence of parameter uncertainties. It features a new perspective on classical LQC results and further sections on robust synthesis, nonclassicaloptimization problems, and analysis and synthesis of uncertain systems.
Control Theory and Design is essential reading for graduates and those entering the research field. The required mathematical background is provided so that the book is also suitable for undergraduate students with some knowledge of basic systemsand control.
Key Features
* Provides a self-contained manual for learning control systems and design
* Contains a clear and concise presentation of the technical background needed
* Includes a new perspective of classical LQG results
* Contains updated results and novel contributions to nonstandard RH2/RH infinity symbol problems
* Covers all the theory from the basic to the more advanced issues

Introduction to Linear Control Systems is designed as a standard introduction to linear control systems for all those who one way or another deal with control systems. It can be used as a comprehensive up-to-date textbook for a one-semester 3-credit undergraduate course on linear control systems as the first course on this topic at university. This includes the faculties of electrical engineering, mechanical engineering, aerospace engineering, chemical and petroleum engineering, industrial engineering, civil engineering, bio-engineering, economics, mathematics, physics, management and social sciences, etc. The book covers foundations of linear control systems, their raison detre, different types, modelling, representations, computations, stability concepts, tools for time-domain and frequency-domain analysis and synthesis, and fundamental limitations, with an emphasis on frequency-domain methods. Every chapter includes a part on further readings where more advanced topics and pertinent references are introduced for further studies. The presentation is theoretically firm, contemporary, and self-contained. Appendices cover Laplace transform and differential equations, dynamics, MATLAB and SIMULINK, treatise on stability concepts and tools, treatise on Routh-Hurwitz method, random optimization techniques as well as convex and non-convex problems, and sample midterm and endterm exams. The book is divided to the sequel 3 parts plus appendices. PART I: In this part of the book, chapters 1-5, we present foundations of linear control systems. This includes: the introduction to control systems, their raison detre, their different types, modelling of control systems, different methods for their representation and fundamental computations, basic stability concepts and tools for both analysis and design, basic time domain analysis and design details, and the root locus as a stability analysis and synthesis tool. PART II: In this part of the book, Chapters 6-9, we present what is generally referred to as the frequency domain methods. This refers to the experiment of applying a sinusoidal input to the system and studying its output. There are basically three different methods for representation and studying of the data of the aforementioned frequency response experiment: these are the Nyquist plot, the Bode diagram, and the Krohn-Manger-Nichols chart. We study these methods in details. We learn that the output is also a sinusoid with the same frequency but generally with different phase and magnitude. By dividing the output by the input we obtain the so-called sinusoidal or frequency transfer function of the system which is the same as the transfer function when the Laplace variable s is substituted with . Finally we use the Bode diagram for the design process. PART III: In this part, Chapter 10, we introduce some miscellaneous advanced topics under the theme fundamental limitations which should be included in this undergraduate course at least in an introductory level. We make bridges between some seemingly disparate aspects of a control system and theoretically complement the previously studied subjects. Appendices: The book contains seven appendices. Appendix A is on the Laplace transform and differential equations. Appendix B is an introduction to dynamics. Appendix C is an introduction to MATLAB, including SIMULINK. Appendix D is a survey on stability concepts and tools. A glossary and road map of the available stability concepts and tests is provided which is missing even in the research literature. Appendix E is a survey on the Routh-Hurwitz method, also missing in the literature. Appendix F is an introduction to random optimization techniques and convex and non-convex problems. Finally, appendix G presents sample midterm and endterm exams, which are class-tested several times.

This book deals with a combination of two main problems for the first time. They are saturation on control and on the rate (or increment) of the control, and the solution of unsymmetrical saturation on the control by LMIs. It treats linear systems in state space form, in both the continuous- and discrete-time domains. Necessary and sufficient conditions are derived for autonomous linear systems with constrained state increment or rate, such that the system evolves respecting incremental or rate constraints if any. A pole assignment technique is then used to solve the problem, giving stabilizing state feedback controllers that respect non-symmetrical constraints on control alone or on both control and its increment or rate. Illustrative examples show the application of these methods on academic examples or on such real plant models as the double integrator system. This problem is then extended to various others including: systems with constraints and perturbations; singular systems with constrained control; systems with unsymmetrical saturations; saturated systems with delay, and 2-D systems with saturations. The solutions obtained are of two types: necessary and sufficient conditions solved with linear programming techniques; and sufficient conditions under LMIs. A new approach extends existing techniques for dealing with symmetrical saturations to take direct account of unsymmetrical saturations into account with LMIs. This tool enables the authors to obtain new results on continuous- and discrete-time systems. The book uses illustrative examples and figures and provides many comparisons with existing results. Systems theoreticians interested in multidimensional systems and practitioners working with saturated and constrained controllers will find the research and background presented in Saturated Control of Linear Systems to be of considerable interest in helping them overcome problems with their plant and in stimulating further research.

Most physical systems possess parametric uncertainties or unmeasurable parameters and, since parametric uncertainty may degrade the performance of model predictive control (MPC), mechanisms to update the unknown or uncertain parameters are desirable in application. One possibility is to apply adaptive extensions of MPC in which parameter estimation and control are performed online. This book proposes such an approach, with a design methodology for adaptive robust nonlinear MPC (NMPC) systems in the presence of disturbances and parametric uncertainties. One of the key concepts pursued is the concept of set-based adaptive parameter estimation, which provides a mechanism to estimate the unknown parameters as well as an estimate of the parameter uncertainty set. The knowledge of non-conservative uncertain set estimates is exploited in the design of robust adaptive NMPC algorithms that guarantee robustness of the NMPC system to parameter uncertainty. Topics covered include: a review of nonlinear MPC; extensions for performance improvement; introduction to adaptive robust MPC; computational aspects of robust adaptive MPC; finite-time parameter estimation in adaptive control; performance improvement in adaptive control; adaptive MPC for constrained nonlinear systems; adaptive MPC with disturbance attenuation; robust adaptive economic MPC; setbased estimation in discrete-time systems; and robust adaptive MPC for discrete-time systems.

Praise for the Series:
"This book will be a useful reference to control engineers and researchers. The papers contained cover well the recent advances in the field of modern control theory."
--IEEE Group Correspondence
"This book will help all those researchers who valiantly try to keep abreast of what is new in the theory and practice of optimal control."
--Control

This book addresses the topic of fractional-order modeling of nuclear reactors. Approaching neutron transport in the reactor core as anomalous diffusion, specifically subdiffusion, it starts with the development of fractional-order neutron telegraph equations. Using a systematic approach, the book then examines the development and analysis of various fractional-order models representing nuclear reactor dynamics, ultimately leading to the fractional-order linear and nonlinear control-oriented models. The book utilizes the mathematical tool of fractional calculus, the calculus of derivatives and integrals with arbitrary non-integer orders (real or complex), which has recently been found to provide a more compact and realistic representation to the dynamics of diverse physical systems. Including extensive simulation results and discussing important issues related to the fractional-order modeling of nuclear reactors, the book offers a valuable resource for students and researchers working in the areas of fractional-order modeling and control and nuclear reactor modeling.

Covers advances in the field of computer techniques and algorithms in digital signal processing.

This book presents up-to-date research developments and novel methodologies on semi-Markovian jump systems (S-MJS). It presents solutions to a series of problems with new approaches for the control and filtering of S-MJS, including stability analysis, sliding mode control, dynamic output feedback control, robust filter design, and fault detection. A set of newly developed techniques such as piecewise analysis method, positively invariant set approach, event-triggered method, and cone complementary linearization approaches are presented. Control and Filtering for Semi-Markovian Jump Systems is a comprehensive reference for researcher and practitioners working in control engineering, system sciences and applied mathematics, and is also a useful source of information for senior undergraduates and graduates in these areas. The readers will benefit from some new concepts, new models and new methodologies with practical significance in control engineering and signal processing.

This book presents state-of-the-art research advances in the field of biologically inspired cooperative control theories and their applications. It describes various biologically inspired cooperative control and optimization approaches and highlights real-world examples in complex industrial processes. Multidisciplinary in nature and closely integrating theory and practice, the book will be of interest to all university researchers, control engineers and graduate students in intelligent systems and control who wish to learn the core principles, methods, algorithms, and applications.

This book describes load modeling approaches for complex work pieces and batch forgings, and demonstrates analytical modeling and data-driven modeling approaches for known and unknown complex forging processes. It overcomes the current shortcomings of modeling, analysis and control approaches, presenting contributions in three major areas: In the first, several novel modeling approaches are proposed: a process/shape-decomposition modeling method to help estimate the deformation force; an online probabilistic learning machine for the modeling of batch forging processes; and several data-driven identification and modeling approaches for unknown forging processes under different work conditions. The second area develops model-based dynamic analysis methods to derive the conditions of stability and creep. Lastly, several novel intelligent control methods are proposed for complex forging processes. One of the most serious problems in forging forming involves the inaccurate forging conditions, velocity and position offered by the hydraulic actuator due to the complexity of both the deformation process of the metal work piece and the motion process of the hydraulic actuator. The book summarizes the current weaknesses of modeling, analysis and control approaches. are summarized as follows: a) With the current modeling approaches it is difficult to model complex forging processes with unknown parameters, as they only model the dynamics in local working areas but do not effectively model unknown nonlinear systems across multiple working areas; further, they do not take the batch forging process into account, let alone its distribution modeling. b) All previous dynamic analysis studies simplify the forging system to having a single-frequency pressure fluctuation and neglect the influences of non-linear load force. Further, they fail to take the flow equation in both valves and cylinders into account. c) Conventional control approaches only consider the linear deformation force and pay no attention to sudden changes and the motion synchronization for the multi-cylinder system, making them less effective for complex, nonlinear time-varying forging processes subject to sudden changes.

Active Disturbance Rejection Control of Dynamic Systems: A Flatness Based Approach describes the linear control of uncertain nonlinear systems. The net result is a practical controller design that is simple and surprisingly robust, one that also guarantees convergence to small neighborhoods of desired equilibria or tracking errors that are as close to zero as desired. This methodology differs from current robust feedback controllers characterized by either complex matrix manipulations, complex parameter adaptation schemes and, in other cases, induced high frequency noises through the classical chattering phenomenon. The approach contains many of the cornerstones, or philosophical features, of Model Free Control and ADRC, while exploiting flatness and GPI control in an efficient manner for linear, nonlinear, mono-variable and multivariable systems, including those exhibiting inputs delays. The book contains successful experimental laboratory case studies of diverse engineering problems, especially those relating to mechanical, electro-mechanical, robotics, mobile robotics and power electronics systems.

This book introduces researchers and advanced students with a basic control systems background to an array of control techniques which they can easily implement and use to meet the required performance specifications for their mechatronic applications. It is the result of close to two decades of work of the authors on modeling, simulating and controlling different mechatronic systems from the motion control, automotive control and micro and nano-mechanical systems control areas. The methods presented in the book have all been tested by the authors and a very large group of researchers, who have produced practically implementable controllers with highly successful results. The approach that is recommended in this book is to first start with a conventional control method which may then be cascaded with a feedforward controller if the input is known or can be measured with a preview; to add a disturbance observer if unknown disturbances are to be rejected and if regulation of the uncertain plant about a nominal model is desired; and to add a repetitive controller to take care of any periodic inputs of fixed and known period. Case studies ranging from road vehicle yaw stability control and automated path following, to decoupling control of piezotube actuators in an atomic force microscope are presented. Parameter space based methods are used in the book for achieving robust controllers. Control of Mechatronic Systems is essential reading for researchers and advanced students who want to be exposed to control methods that have been field tested in a wide variety of mechatronic applications, and for practicing engineers who design and implement feedback control systems.

Interest in the area of control of systems defined by partial differential Equations has increased strongly in recent years. A major reason has been the requirement of these systems for sensible continuum mechanical modelling and optimization or control techniques which account for typical physical phenomena. Particular examples of problems on which substantial progress has been made are the control and stabilization of mechatronic structures, the control of growth of thin films and crystals, the control of Laser and semi-conductor devices, and shape optimization problems for turbomachine blades, shells, smart materials and microdiffractive optics. This volume contains original articles by world reknowned experts in the fields of optimal control of partial differential equations, shape optimization, numerical methods for partial differential equations and fluid dynamics, all of whom have contributed to the analysis and solution of many of the problems discussed. The collection provides a state-of-the-art overview of the most challenging and exciting recent developments in the field. It is geared towards postgraduate students and researchers dealing with the theoretical and practical aspects of a wide variety of high technology problems in applied mathematics, fluid control, optimal design, and computer modelling.

This book reports on the latest advances in the study of motion control in biomimetic swimming robots with high speed and high manoeuvrability. It presents state-of-the-art studies on various swimming robots including robotic fish, dolphins and jellyfish in a unified framework, and discusses the potential benefits of applying biomimetic underwater propulsion to autonomous underwater vehicle design, such as: speed, energy economy, enhanced manoeuvrability, and reduced detectability. Given its scope, the book will be of interest to researchers, engineers and graduate students in robotics and ocean engineering who wish to learn about the core principles, methods, algorithms, and applications of biomimetic underwater robots.

Control of Discrete-Time Descriptor Systems takes an anisotropy-based approach to the explanation of random input disturbance with an information-theoretic representation. It describes the random input signal more precisely, and the anisotropic norm minimization included in the book enables readers to tune their controllers better through the mathematical methods provided. The book contains numerous examples of practical applications of descriptor systems in various fields, from robotics to economics, and presents an information-theoretic approach to the mathematical description of coloured noise. Anisotropy-based analysis and design for descriptor systems is supplied along with proofs of basic statements, which help readers to understand the algorithms proposed, and to undertake their own numerical simulations. This book serves as a source of ideas for academic researchers and postgraduate students working in the control of discrete-time systems. The control design procedures outlined are numerically effective and easily implementable in MATLAB (R)

Robust and Fault-Tolerant Control proposes novel automatic control strategies for nonlinear systems developed by means of artificial neural networks and pays special attention to robust and fault-tolerant approaches. The book discusses robustness and fault tolerance in the context of model predictive control, fault accommodation and reconfiguration, and iterative learning control strategies. Expanding on its theoretical deliberations the monograph includes many case studies demonstrating how the proposed approaches work in practice. The most important features of the book include: a comprehensive review of neural network architectures with possible applications in system modelling and control; a concise introduction to robust and fault-tolerant control; step-by-step presentation of the control approaches proposed; an abundance of case studies illustrating the important steps in designing robust and fault-tolerant control; and a large number of figures and tables facilitating the performance analysis of the control approaches described. The material presented in this book will be useful for researchers and engineers who wish to avoid spending excessive time in searching neural-network-based control solutions. It is written for electrical, computer science and automatic control engineers interested in control theory and their applications. This monograph will also interest postgraduate students engaged in self-study of nonlinear robust and fault-tolerant control.

This book reports on the formulation of a multi-stage optimization framework for the Danish power system, taking into account the real operational cost, the voltage constraints and the uncertainty associated to the forecasting errors of the wind power. It describes in detail the implementation of this framework into a simulation platform and its validation in real-world applications. The book especially focuses on automatic voltage control systems and on methods to handle uncertainty in them. All in all, it provides readers with a comprehensive overview of power system optimization and future trends in power system operation.

This monograph combines the commutant lifting theorem for operator theory and the state space method from system theory to provide a unified approach for solving both stationary and nonstationary interpolation problems with norm constraints. Included are the operator-valued versions of the tangential Nevanlinna-Pick problem, the Hermite-FejA(c)r problem, the Nehari problem, the Sarason problem, and the two-sided Nudelman problem, and their nonstationary analogues. The main results concern the existence of solutions, the explicit construction of the central solutions in state space form, the maximum entropy property of the central solutions, and state space parametrizations of all solutions. Direct connections between the various interpolation problems are displayed. Applications to H infinity] control problems are presented. This monograph should appeal to a wide group of mathematicians and engineers. The material is self-contained and may be used for advanced graduate courses and seminars.

This book focuses on the latest applications of nonlinear approaches in engineering and addresses a range of scientific problems. Examples focus on issues in automotive technology, including automotive dynamics, control for electric and hybrid vehicles, and autodriver algorithm for autonomous vehicles. Also included are discussions on renewable energy plants, data modeling, driver-aid methods, and low-frequency vibration. Chapters are based on invited contributions from world-class experts who advance the future of engineering by discussing the development of more optimal, accurate, efficient, cost, and energy effective systems. This book is appropriate for researchers, students, and practising engineers who are interested in the applications of nonlinear approaches to solving engineering and science problems. Presents a broad range of practical topics and approaches; Explains approaches to better, safer, and cheaper systems; Emphasises automotive applications, physical meaning, and methodologies.

As the world has entered the era of big data, there is a need to give a semantic perspective to the data to find unseen patterns, derive meaningful information, and make intelligent decisions. This 2-volume handbook set is a unique, comprehensive, and complete presentation of the current progress and future potential explorations in the field of data science and related topics. Handbook of Data Science with Semantic Technologies provides a roadmap for a new trend and future development of data science with semantic technologies. The first volume serves as an important guide towards applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for both academic researchers and industry professionals. The second volume provides a roadmap for the deployment of semantic technologies in the field of data science that enables users to create intelligence through these technologies by exploring the opportunities while eradicating the current and future challenges. The set explores the optimal use of these technologies to provide the maximum benefit to the user under one comprehensive source. This set consisting of two separate volumes can be utilized independently or together as an invaluable resource for students, scholars, researchers, professionals, and practitioners in the field.

This book investigates the performance limitation issues in networked feedback systems. The fact that networked feedback systems consist of control and communication devices and systems calls for the integration of control theory and information theory. The primary contributions of this book lie in two aspects: the newly-proposed information-theoretic measures and the newly-discovered control performance limitations. We first propose a number of information notions to facilitate the analysis. Using those notions, classes of performance limitations of networked feedback systems, as well as state estimation systems, are then investigated. In general, the book presents a unique, cohesive treatment of performance limitation issues of networked feedback systems via an information-theoretic approach. This book is believed to be the first to treat the aforementioned subjects systematically and in a unified manner, offering a unique perspective differing from existing books.

This thesis develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle. Using said framework as a point of departure, it then proposes novel strategies for dealing with control monitoring and quality prediction problems in industrial production contexts. The thesis reveals the slowly varying nature of industrial production processes under feedback control, and integrates it with process data analytics to offer powerful prior knowledge that gives rise to statistical methods tailored to industrial data. It addresses several issues of immediate interest in industrial practice, including process monitoring, control performance assessment and diagnosis, monitoring system design, and product quality prediction. In particular, it proposes a holistic and pragmatic design framework for industrial monitoring systems, which delivers effective elimination of false alarms, as well as intelligent self-running by fully utilizing the information underlying the data. One of the strengths of this thesis is its integration of insights from statistics, machine learning, control theory and engineering to provide a new scheme for industrial process modeling in the era of big data.

This book provides a new approach to the control of food transformation processes, emphasizing the advantage of considering the system as a multivariable one, and taking a holistic approach to the decision-making process in the plant, considering not only the technical but also the economic implications of these decisions. In addition, it presents a hierarchical structure for the global control of the plant, and includes appropriate techniques for each of the control layers. The book addresses the challenges of modeling food transformation processes, using both traditional system-identification techniques and, where these prove impractical, models based on expert knowledge and using fuzzy systems. The construction of optimal controllers for each of these types of models is also discussed, as a means to close a feedback loop on the higher-level outputs of the process. Finally, the problem of production planning is covered from two standpoints: the traditional batch-sizing problem, and the planning of production throughout the season. Systematic season-wide production planning is built upon the models constructed for the control of the plant, and incorporates market- and business-specific information. Examples based on the processing of various foodstuffs help to illustrate the text throughout, while the book's closing chapter presents a case study on advances in the processing of olive oil. Given its scope, the book will primarily be of interest to two groups of readers: food engineering practitioners and students, who are familiar with the characteristics of food processes but have little or no background in control engineering; and control engineering researchers, students and practitioners, whose situation is just the opposite, and who wish to learn more about food engineering and its specific challenges for control. Advances in Industrial Control reports and encourages 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.

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real-time control theory, data assimilation, real-time visualization, high-dimensional state spaces and interaction of different reduction techniques.