Communications and Control Engineering – serie

Suitable either as a reference for practicing engineers or as a text for a graduate course in adaptive control systems, this book is a self -contained compendium of readily implementable adaptive control algorithms that have been developed and applied by the authors for over fifteen years. These algorithms, which do not require the user to identify the process parameters explicitly, have been successfully applied to a wide variety of engineering problems including flexible structure control, blood pressure control, and robotics; they are suitable for a wide variety of multiple input-output control systems with uncertainity and external disturbances. The text is intended to enable anyone with knowledge of basic linear multivariable systems to adapt the algorithms to problems in a wide variety of disciplines. Thus, in addition to developing the theoretical details of the algorithms presented, the text gives considerable emphasis to design of algorithms and to representative applications in flight control, flexible structure control, robotics, and drug-infusion control. Engineers can thus use and test these algorithms in practical problems. This second edition has been corrected and updated throughout. It makes use of MATLAB programs for some of the illustrative examples; these programs are described in the text and can be obtained from the MathWorks file server.

Stabilization of Navier–Stokes Flows presents recent notable progress in the mathematical theory of stabilization of Newtonian fluid flows.

Analysis and Control of Boolean Networks presents a systematic new approach to the investigation of Boolean control networks. The fundamental tool in this approach is a novel matrix product called the semi-tensor product (STP). Using the STP, a logical function can be expressed as a conventional discrete-time linear system. In the light of this linear expression, certain major issues concerning Boolean network topology – fixed points, cycles, transient times and basins of attractors – can be easily revealed by a set of formulae. This framework renders the state-space approach to dynamic control systems applicable to Boolean control networks. The bilinear-systemic representation of a Boolean control network makes it possible to investigate basic control problems including controllability, observability, stabilization, disturbance decoupling etc.

Distributed Coordination of Multi-agent Networks introduces problems, models, and issues such as collective periodic motion coordination, collective tracking with a dynamic leader, and containment control with multiple leaders, and explores ideas for their solution. Solving these problems extends the existing application domains of multi-agent networks; for example, collective periodic motion coordination is appropriate for applications involving repetitive movements, collective tracking guarantees tracking of a dynamic leader by multiple followers in the presence of reduced interaction and partial measurements, and containment control enables maneuvering of multiple followers by multiple leaders.

There are plenty of challenging and interesting problems open for investigation in the field of switched systems. Stability issues help to generate many complex nonlinear dynamic behaviors within switched systems. The authors present a thorough investigation of stability effects on three broad classes of switching mechanism: arbitrary switching where stability represents robustness to unpredictable and undesirable perturbation, constrained switching, including random (within a known stochastic distribution), dwell-time (with a known minimum duration for each subsystem) and autonomously-generated (with a pre-assigned mechanism) switching; and designed switching in which a measurable and freely-assigned switching mechanism contributes to stability by acting as a control input.For each of these classes this book propounds: detailed stability analysis and/or design, related robustness and performance issues, connections to other control problems and many motivating and illustrative examples.

Nonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. NMPC schemes with and without stabilizing terminal constraints are detailed and intuitive examples illustrate the performance of different NMPC variants. An introduction to nonlinear optimal control algorithms gives insight into how the nonlinear optimisation routine – the core of any NMPC controller – works. An appendix covering NMPC software and accompanying software in MATLAB® and C++(downloadable from www.springer.com/ISBN) enables readers to perform computer experiments exploring the possibilities and limitations of NMPC.

Recently, the subject of nonlinear control systems analysis has grown rapidly and this book provides a simple and self-contained presentation of their stability and feedback stabilization which enables the reader to learn and understand major techniques used in mathematical control theory. In particular: the important techniques of proving global stability properties are presented closely linked with corresponding methods of nonlinear feedback stabilization; a general framework of methods for proving stability is given, thus allowing the study of a wide class of nonlinear systems, including finite-dimensional systems described by ordinary differential equations, discrete-time systems, systems with delays and sampled-data systems; approaches to the proof of classical global stability properties are extended to non-classical global stability properties such as non-uniform-in-time stability and input-to-output stability; and new tools for stability analysis and control design of a wide class of nonlinear systems are introduced. The presentational emphasis of Stability and Stabilization of Nonlinear Systems is theoretical but the theory’s importance for concrete control problems is highlighted with a chapter specifically dedicated to applications and with numerous illustrative examples. Researchers working on nonlinear control theory will find this monograph of interest while graduate students of systems and control can also gain much insight and assistance from the methods and proofs detailed in this book.

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.

Thoroughly revised and updated, this second edition of Adaptive Control covers new developments in the field, including multi-model adaptive control with switching, direct and indirect adaptive regulation, and adaptive feedforward disturbance compensation.

"Control of Complex Systems: Structural Constraints and Uncertainty" focuses on control design under information structure constraints, with a particular emphasis on large-scale systems. The complexity of such systems poses serious computational challenges and severely restricts the types of feedback laws that can be used in practice. This book systematically addresses the main issues, and provides a number of applications that illustrate potential design methods, most which use Linear Matrix Inequalities (LMIs), which have become a popular design tool over the past two decades. Authors Aleksandar I. Zecevic and Dragoslav D. Siljak use their years of experience in the control field to also: Address the issues of large-scale systems as they relate to robust control and linear matrix inequalities Discuss a new approach to applying standard LMI techniques to large-scale systems, combining graphic-theoretic decomposition techniques with appropriate low-rank numerical approximations and dramatically reducing the computational effortProviding numerous examples and a wide variety of applications, ranging from electric power systems and nonlinear circuits to mechanical problems and dynamic Boolean networks"Control of Complex Systems: Structural Constraints and Uncertainty" will appeal to practicing engineers, researchers and students working in control design and other related areas.

Ad ap tive Contro l provid es tec hniques for aut omatic adjustment in real t ime of cont roller parameter s in order to achieve or to maintain a desired level for the performance of cont rol systems when the dyn amic param et ers of t he process are unknown and/or var y in time. These techniques have as a main feature t he abilit y to ext ract significant information from real dat a in ord er to tune the cont roller and they feature a me chanism for adjust ing parameter s (of a plant model or of a cont roller). While the history of Adaptive Control is lon g, significant progr ess in under - standing and applying Adaptive Con trol began in the early sevent ies. The growing availability of digital com puters have also contributed to the devel- opment of the field . The earlier applicat ions in the sevent ies and beginning of the eight ies, provided important feedback for the development of the field . Theoretical developments allowed a number of basic problems to be solved . The aim of this book is to provide a coherent and comprehensive treatment of the field .The presentation takes the read er from the basic problem formu- lation to the analytical solutions who se prac tical significance is illustrated by applications.

Much has been written about the general difficulty of developing the models required for model-based control of processes whose dynamics exhibit signif­ icant nonlinearity (for further discussion and references, see Chapter 1). In fact, the development ofthese models stands as a significant practical imped­ iment to widespread industrial application oftechniques like nonlinear model predictive control (NMPC), whoselinear counterpart has profoundly changed industrial practice. One ofthe reasons for this difficulty lies in the enormous variety of "nonlinear models," different classes of which can be less similar to each other than they are to the class of linear models. Consequently, it is a practical necessity to restrict consideration to one or a few specific nonlinear model classes if we are to succeed in developing, understanding, and using nonlinear models as a basis for practical control schemes. Because they repre­ sent a highly structured extension ofthe class oflinear finite impulse response (FIR) models on which industrially popular linear MPC implementations are based, this book is devoted to the class of discrete-time Volterra models and a fewother, closelyrelated, nonlinear model classes. The objective ofthis book is to provide a useful reference for researchers in the field of process control and closely related areas, collecting a reasonably wide variety of results that may be found in different parts of the large literature that exists on the gen­ eral topics of process control, nonlinear systems theory, statistical time-series models, biomedical engineering, and digital signal processing, among others.

Control Theory for Linear Systems deals with the mathematical theory of feedback control of linear systems. It treats a wide range of control synthesis problems for linear state space systems with inputs and outputs. The book provides a treatment of these problems using state space methods, often with a geometric flavour. Its subject matter ranges from controllability and observability, stabilization, disturbance decoupling, and tracking and regulation, to linear quadratic regulation, H2 and H-infinity control, and robust stabilization. Each chapter of the book contains a series of exercises, intended to increase the reader's understanding of the material. Often, these exercises generalize and extend the material treated in the regular text.

Such methods often come with easy-to-use error bounds, and in the case of controller simplification methods, such error bounds will usually be related to closed-loop properties. This book is the first comprehensive treatment of approximation methods for filters and controllers.

This book deals with the application of modern control theory to some important underactuated mechanical systems, from the inverted pendulum to the helicopter model. It will help readers gain experience in the modelling of mechanical systems and familiarize with new control methods for non-linear systems.

The field of intelligent control has recently emerged as a response to the challenge of controlling highly complex and uncertain nonlinear systems.

In the last decade a dynamic development in positive systems has been observed. Roughly speaking, positive systems are systems whose inputs, state variables and outputs take only nonnegative values. Examples of positive systems are industrial processes involving chemical reactors, heat exchangers and distillation columns, storage systems, compartmental systems, water and atmospheric pollution models. A variety of models having positive linear system behaviour can be found in engineering, management science, economics, social sciences, biology and medicine, etc. The basic mathematical tools for analysis and synthesis of linear systems are linear spaces and the theory of linear operators. Positive linear systems are defined on cones and not on linear spaces. This is why the theory of positive systems is more complicated and less advanced. The theory of positive systems has some elements in common with theories of linear and non-linear systems. Schematically the relationship between the theories of linear, non-linear and positive systems is shown in the following figure Figure 1.

New results, fresh ideas and new applications in automotive and flight control systems are presented in this second edition of Robust Control.

This book reports on recent achievements in stability and feedback stabilization of infinite systems. Various control methods such as sensor feedback control and dynamic boundary control are applied to stabilize the equations. Many new theorems and methods are included in the book.

This is a unified collection of important recent results for the design of robust controllers for uncertain systems, primarily based on H8 control theory or its stochastic counterpart, risk sensitive control theory. Two practical applications are used to illustrate the methods throughout.

Digital elements such as signal processors, microcontrollers or industrial PCs are widely used for filtering and control of continuous-time processes or plants. Because of the serial operation mode of these digital elements the continuous­ time processes cannot be served continuously. That is why continuous-time and discrete-time signals occur simultaneously in systems of this kind. The continuous (real) time also contains the discrete time instants - therefore, a complete and accurate description of such systems in continuous time is pos­ sible. However, this description proves to be more complicated, because the hybrid system becomes time-variable, also in cases, where all continuous-time processes and all digital elements are time invariant. Often digital elements operate with a fixed time period T, that leads to a periodically time-variable system. Besides the time sampling the use of analogue to digital and digital to analogue converters together with the finite numerical precision of the processors also results in amplitude quantization of the measuring and control signals. Taking into account these effects would lead to nonlinear models. Because these effects are often of minor influence but difficult in handling, in theory and practice it is generally accepted to consider mainly the pure time-quantization effect. Systems containing both continuous-time and discrete-time signals are usually called sampled-data systems (SD systems).

The purpose of this book is to present a self-contained and coordinated de­ scription of several design methods for nonlinear control systems, with special emphasis on the problem of achieving stability, globally or on arbitrarily large domains, in the presence of model uncertainties.

1) f(x, t) 2: 0 where x E JRn is the system's state vector, u E JRm is the vector of inputs, and the function f (-, . 2) f(q, t) 2: 0 where q E JRn is the vector of generalized coordinates of the system and u is an in­ put (or controller) that generally involves a state feedback loop, i.

Ever since the beginnings of mankind eons ago, the desire to control, regulate, and track even under persistent disturbances has been a dominating influence in the development of human civilization. It is so also in the development of Automatic Control Theory and its Applications. The subject of output regula­ tion occupies a central theme in all endeavors of theoreticians and practition­ ers alike. Yet there is no book or monograph that brings all essential modem developments on output regulation under a single cover. This book is intended to fill this void. Main topics that are brought together in this book include among oth­ ers, classical exact output regulation of linear systems along with its differ­ ent facets of well-posedness, internal model principle, and structural stability; output regulation of linear systems with input amplitude and rate saturation constraints; output regulation with transient performance specifications; per­ formance issues (such as H2, H , L1 and others) with an output regulation oo constraint; generalized output regulation in which the set of tracking signals as well as the set of disturbances that act on the plant are broadened beyond those that are common in classical output regulation, and thus permitting us to deal with exact as well as almost output regulation under a variety of controllers, etc.

Control of Complex and Uncertain Systems examines feedback controller design for dynamical systems. It gives a compact presentation of the evolution of feedback theory and feedback design methods, before presenting a new theoretical approach to feedback control.Traditionally, the lack of standard procedures for the design of feedback controls that will be subject to uncertain conditions, coupled with a limited understanding of how feedback mechanisms will react to those uncertain conditions, has presented a problem.This book presents a standard principle, as well as concrete methods, for feedback design. It equally aims to increase our understanding of how feedback controllers react to uncertain forces. In doing so it lays the theoretical basis for a new generation of perfect automatic systems.Topics covered include:- Synthesis of Nonlinear Controllers- Higher Degree Sliding Modes- Theory of Coordinate-Operator Feedback / Theory of Operator-Coordinate Feedback- Physical Foundations of the Compensation for Disturbances, and Stabilization of Forced Motion in Binary Systems- Signal Differentiation- Suboptimal Stabilization of an Uncertain Object

System and Control theory is one of the most exciting areas of contemporary engineering mathematics. From the analysis of Watt's steam engine governor - which enabled the Industrial Revolution - to the design of controllers for consumer items, chemical plants and modern aircraft, the area has always drawn from a broad range of tools. It has provided many challenges and possibilities for interaction between engineering and established areas of 'pure' and 'applied' mathematics. This impressive volume collects a discussion of more than fifty open problems which touch upon a variety of subfields, including: chaotic observers, nonlinear local controlability, discrete event and hybrid systems, neural network learning, matrix inequalities, Lyapunov exponents, and many other issues. Proposed and explained by leading researchers, they are offered with the intention of generating further work, as well as inspiration for many other similar problems which may naturally arise from them. With extensive references, this book will be a useful reference source - as well as an excellent addendum to the textbooks in the area.

This book deals with the issue of fundamental limitations in filtering and control system design. This issue lies at the very heart of feedback theory since it reveals what is achievable, and conversely what is not achievable, in feedback systems. The subject has a rich history beginning with the seminal work of Bode during the 1940's and as subsequently published in his well-known book Feedback Amplifier Design (Van Nostrand, 1945). An interesting fact is that, although Bode's book is now fifty years old, it is still extensively quoted. This is supported by a science citation count which remains comparable with the best contemporary texts on control theory. Interpretations of Bode's results in the context of control system design were provided by Horowitz in the 1960's. For example, it has been shown that, for single-input single-output stable open-loop systems having rela­ tive degree greater than one, the integral of the logarithmic sensitivity with respect to frequency is zero. This result implies, among other things, that a reduction in sensitivity in one frequency band is necessarily accompa­ nied by an increase of sensitivity in other frequency bands. Although the original results were restricted to open-loop stable systems, they have been subsequently extended to open-loop unstable systems and systems having nonminimum phase zeros.

Constructive Nonlinear Control presents a broad repertoire of constructive nonlinear designs not available in other works by widening the class of systems and design tools. Several streams of nonlinear control theory are merged and directed towards a constructive solution of the feedback stabilization problem. Analysis, geometric and asymptotic concepts are assembled as design tools for a wide variety of nonlinear phenomena and structures. Geometry serves as a guide for the construction of design procedures whilst analysis provides the robustness which geometry lacks. New recursive designs remove earlier restrictions on feedback passivation. Recursive Lyapunov designs for feedback, feedforward and interlaced structures result in feedback systems with optimality properties and stability margins. The design-oriented approach will make this work a valuable tool for all those who have an interest in control theory.

The advent of new high-speed microprocessor technology together with the need for high-performance robots created substantial and realistic place for control theory in the field of robotics. Since the beginning of the 80's, robotics and control theory have greatly benefited from a mutual fertiliza­ tion. On one hand, robot models (inherently highly nonlinear) have been used as good case studies for exemplifying general concepts of analysis and design of advanced control theory; on the other hand, robot manipulator by using new control algorithms. Fur­ performance has been improved thermore, many interesting robotics problems, e. g. , in mobile robots, have brought new control theory research lines and given rise to the development of new controllers (time-varying and nonlinear). Robots in control are more than a simple case study. They represent a natural source of inspiration and a great pedagogical tool for research and teaching in control theory. Several advanced control algorithms have been developed for different types of robots (rigid, flexible and mobile), based either on existing control techniques, e. g. , feedback linearization and adaptive control, or on new control techniques that have been developed on purpose. Most of those results, although widely spread, are nowadays rather dispersed in different journals and conference proceedings. The purpose of this book is to collect some of the most fundamental and current results on theory of robot control in a unified framework, by editing, improving and completing previous works in the area.