Advanced Textbooks in Control and Signal Processing – serie

Following an introduction on system theory, this book shows the reader how to approach the system identification problem in a systematic fashion. It aims to teach students the fundamentals of systems identification without unduly complicated mathematics.

Robust Control Design with MATLAB® (second edition) helps the student to learn how to use well-developed advanced robust control design methods in practical cases.

Modern power electronic converters are involved in a very broad spectrum of applications: switched-mode power supplies, electrical-machine-motion-control, active power filters, distributed power generation, flexible AC transmission systems, renewable energy conversion systems and vehicular technology, among them.Power Electronics Converters Modeling and Control teaches the reader how to analyze and model the behavior of converters and so to improve their design and control. Dealing with a set of confirmed algorithms specifically developed for use with power converters, this text is in two parts: models and control methods. The first is a detailed exposition of the most usual power converter models:·        switched and averaged models;·        small/large-signal models; and·        time/frequency models.The second focuses on three groups of control methods:·        linear control approaches normally associated with power converters;·        resonant controllers because of their significance in grid-connected applications; and·        nonlinear control methods including feedback linearization, stabilizing, passivity-based, and variable-structure control.Extensive case-study illustration and end-of-chapter exercises reinforce the study material.Power Electronics Converters Modeling and Control addresses the needs of graduate students interested in power electronics, providing a balanced understanding of theoretical ideas coupled with pragmatic tools based on control engineering practice in the field. Academics teaching power electronics will find this an attractive course text and the practical points make the book useful for self tuition by engineers and other practitioners wishing to bring their knowledge up to date.

This book develops the understanding and skills needed to be able to tackle original control problems. The general approach to a given control problem is to try the simplest tentative solution first and, when this is insufficient, to explain why and use a more sophisticated alternative to remedy the deficiency and achieve satisfactory performance. This pattern of working gives readers a full understanding of different controllers and teaches them to make an informed choice between traditional controllers and more advanced modern alternatives in meeting the needs of a particular plant. Attention is focused on the time domain, covering model-based linear and nonlinear forms of control together with robust control based on sliding modes and the use of state observers such as disturbance estimation.Feedback Control is self-contained, paying much attention to explanations of underlying concepts, with detailed mathematical derivations being employed where necessary. Ample use is made of diagrams to aid these conceptual explanations and the subject matter is enlivened by continual use of examples and problems derived from real control applications. Readers’ learning is further enhanced by experimenting with the fully-commented MATLAB®/Simulink® simulation environment made accessible at insert URL here to produce simulations relevant to all of the topics covered in the text. A solutions manual for use by instructors adopting the book can also be downloaded from insert URL here.Feedback Control is suitable as a main textbook for graduate and final-year undergraduate courses containing control modules; knowledge of ordinary linear differential equations, Laplace transforms, transfer functions, poles and zeros, root locus and elementary frequency response analysis, and elementary feedback control is required. It is also a useful reference source on control design methods for engineers practicing in industry and for academic control researchers.

Receding Horizon Control (RHC) introduces the essentials of a successful feedback strategy that has emerged in many industrial fields. RHC has several advantages over other types of control: greater adaptability to parametric changes than infinite horizon control; better tracking than PID and good constraint handling among others. The text builds understanding starting with optimal controls for simple linear systems and working through constrained systems to nonlinear cases. RHC is applied to discrete-time systems for easier computer application and its techniques are unified using the state-space framework. Examples and exercises allow you to practise as you go with MATLAB files for the solution of selected examples. Students following masters and doctoral courses in control will find this book an excellent companion to tuition and research. Tutors and academics researching predictive control can use it not only as a scholarly textbook but as a co-ordinated reference for its wide range of RHC schemes.

The classic text on robot manipulators now covers visual control, motion planning and mobile robots too!Based on the successful Modelling and Control of Robot Manipulators by Sciavicco and Siciliano (Springer, 2000), Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control.

The topics of control engineering and signal processing continue to ?o- ish and develop. In common with general scienti?c investigation, new ideas, concepts and interpretations emerge quite spontaneously and these are then discussed,used,discardedorsubsumedinto theprevailingsubjectparadigm. Sometimes these innovative concepts coalesce into a new sub-discipline within the broad subject tapestry of control and signal processing. This p- liminary battle between old and new usually takes place at conferences, through the Internet and in the journals of the discipline. After a little more maturity has been acquired by the new concepts then archival publication as a scienti?c or engineering monograph may occur. A new concept in control and signal processing is known to have - rived when suf?cient material has evolved for the topic to be taught as a specialised tutorial workshop or as a course to undergraduate, graduate or industrial engineers. Advanced Textbooks in Control and Signal Processing are designed as a vehicle for the systematic presentation of course material for both popularand innovative topics in the discipline. It is hoped that prosp- tive authors will welcome the opportunity to publish a structured and s- tematic presentation of some of the newer emerging control and signal p- cessing technologies in the textbook series.

Discrete-event dynamic systems (DEDs) permeate our world, being of great importance in modern manufacturing processes, transportation and various forms of computer and communications networking.“Modeling and Control of Discrete-event Dynamic Systems” begins with the mathematical basics required for the study of DEDs and moves on to present various tools used in their modeling and control. Among the instruments explained are many forms of Petri net, the sequential function chart, state charts, formal languages and max-plus algebra. Industrial examples illustrate the concepts and methods discussed. Using them, readers will be able to understand DEDs quickly and to master the control methods to analyze and improve the performance of their systems.Final-year undergraduates and graduates embarking on further courses in control, manufacturing and process engineering, computer studies or operations research will find this text an invaluable aid in learning about the control of this important class of systems.

The classic text on robot manipulators now covers visual control, motion planning and mobile robots too!Based on the successful Modelling and Control of Robot Manipulators by Sciavicco and Siciliano (Springer, 2000), Robotics provides the basic know-how on the foundations of robotics: modelling, planning and control.

The practical application of Genetic Algorithms to the solution of engineering problems, is rapidly becoming an established approach in the fields of control and signal processing. This book provides comprehensive coverage of the techniques involved, describing the intrinsic characteristics, advantages and constraints of genetic algorithms, as well as discussing genetic operations such as crossover, mutation and reinsertion. In addition, the principle of multiobjective optimization and computing parallelism are discussed. These features are fully illustrated by real-world applications. Also described is a newly proposed and unique hierarchical genetic algorithm designed to address the problems in determining system topology. For added value, a 3.5" disk accompanies the book, that provides the reader with an interactive Genetic Algorithms demonstration programme.

Developed from a set of lecture notes by Professor Kamen and since developed and refined by both authors, this introductory yet comprehensive study is a prime example in its field. There are examples in the book that use MATLAB® and many of the problems discussed require the use of MATLABâ. The primary objective is to provide students with an extensive coverage of Wiener and Kalman filtering along with the development of least squares estimation, maximum likelihood estimation and maximum a posteriori estimation, based on discrete-time measurements. In the study of these estimation techniques there is a strong emphasis on how they interrelate and fit together to form a systematic development of optimal estimation. Also included in the text is a chapter on nonlinear filtering focusing on the extended Kalman filter and a recently-developed nonlinear estimator based on a block-form version of the Levenberg-Marquardt algorithm.

This text is intended to be used by postgraduate and advanced undergraduate students in the study of discrete-time integrals. It focuses on an algebraic approach to the analysis and design of discrete-time signal processing algorithms which renders the Laplace and z-transforms redundant for this purpose. While the material on difference equations is developed slowly, from an elementary base, it is assumed that the reader has a working knowledge of complex numbers and has completed a first course in vector and matrix analysis.Various subjects and problems are discussed including: - The solution of both difference and state space equations - Provides an alternative approach to the use of Laplace and z-transformations - Linear-algebraic ideas for the analysis and design of linear signals - The Design of recursive (IIR) and nonrecursive (FIR) digital filters - The design of classical Butterworth, Chebyshev and Elliptical analog filters and the transformation of analog filters - The Discrete Fourier Transform and its implementation through the Fast Fourier Transform - Applications of least-squares analysis to the design of linear phase FIR filters, convolution (FIR and IIR) signal models, and state space signal models - Design of digital filters via discrete approximation of analog filters, digital transformation of analog filter, and digital transformation of digital filter - Finite wordlength (FWL) implementation of IIR digital filters including the basic properties of both fixed and floating point arithmetic representations, implication of arithmetic quantizations and filter structure on accuracy and speed of real-time implementation - Written with clarity and containing the latest, detailed results based on comprehensive research, this book is an important textbook for all students interested in discrete-time signals - The text supports the use of algebra-based software packages, such as MATLAB .

Fundamental and technological topics are blended uniquely and developed clearly in nine chapters with a gradually increasing level of complexity. A wide variety of relevant problems is raised throughout, and the proper tools to find engineering-oriented solutions are introduced and explained, step by step.Fundamental coverage includes: Kinematics; Statics and dynamics of manipulators; Trajectory planning and motion control in free space. Technological aspects include: Actuators; Sensors; Hardware/software control architectures; Industrial robot-control algorithms.Furthermore, established research results involving description of end-effector orientation, closed kinematic chains, kinematic redundancy and singularities, dynamic parameter identification, robust and adaptive control and force/motion control are provided.To provide readers with a homogeneous background, three appendices are included on: Linear algebra; Rigid-body mechanics; Feedback control. To acquire practical skill, more than 50 examples and case studies are carefully worked out and interwoven through the text, with frequent resort to simulation.In addition, more than 80 end-of-chapter exercises are proposed, and the book is accompanied by a solutions manual containing the MATLAB code for computer problems; this is available from the publisher free of charge to those adopting this work as a textbook for courses.

The technology of neural networks has attracted much attention in recent years. Their ability to learn nonlinear relationships is widely appreciated and is utilized in many different types of applications; modelling of dynamic systems, signal processing, and control system design being some of the most common. The theory of neural computing has matured considerably over the last decade and many problems of neural network design, training and evaluation have been resolved. This book provides a comprehensive introduction to the most popular class of neural network, the multilayer perceptron, and shows how it can be used for system identification and control. It aims to provide the reader with a sufficient theoretical background to understand the characteristics of different methods, to be aware of the pit-falls and to make proper decisions in all situations. The subjects treated include: System identification: multilayer perceptrons; how to conduct informative experiments; model structure selection; training methods; model validation; pruning algorithms. Control: direct inverse, internal model, feedforward, optimal and predictive control; feedback linearization and instantaneous-linearization-based controllers. Case studies: prediction of sunspot activity; modelling of a hydraulic actuator; control of a pneumatic servomechanism; water-level control in a conical tank. The book is very application-oriented and gives detailed and pragmatic recommendations that guide the user through the plethora of methods suggested in the literature. Furthermore, it attempts to introduce sound working procedures that can lead to efficient neural network solutions. This will make the book invaluable to the practitioner and as a textbook in courses with a significant hands-on component.

In the last decade new artificial intelligence methods for the modelling and control of complex systems, namely neural networks, fuzzy logic and probabilistic reasoning have drawn the interest of researchers and engineers. Recently, the advantages achievable by using combinations of these methods, which have independent origin and evolution, have been pointed out, generating a new paradigm which is now termed "soft-computing". This new methodology subsumes the capabilities of neural networks for modelling non-linear systems and for solving classification problems, the power of fuzzy-logic to represent approximate or heuristic reasoning and the large capabilities of evolutionary computation for problem optimisation. The book presents a clear understanding of a new type of computation system, the cellular neural network (CNN), which has been successfully applied to the solution of many heavy computation problems, mainly in the fields of image processing and complex partial differential equations. CNNs' computation-based systems represent new opportunities for improving the soft-computation toolbox.The application of soft computing to complex systems and in particular to chaotic systems with the generation of chaotic dynamics by using CNN is also described. These aspects are of particular interest owing to their growing interest for research and application purposes.Specific topics covered in the text include:- fuzzy logic, control and neural networks;- artificial neural networks and their application in the modelling and control of dynamical systems;- evolutionary optimisation algorithms;- complex dynamics and cellular neural networks;- applications in urban traffic noise monitoring, robot control and rapid thermal process systems.

Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data.The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process monitoring techniques presented include: Data-driven methods - principal component analysis, Fisher discriminant analysis, partial least squares and canonical variate analysis; Analytical Methods - parameter estimation, observer-based methods and parity relations; Knowledge-based methods - causal analysis, expert systems and pattern recognition.The text demonstrates the application of all of the data-driven process monitoring techniques to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of each approach in detail. This aids the reader in selecting the right method for his process application. Plant simulator and homework problems in which students apply the process monitoring techniques to a non-trivial simulated process, and can compare their performance with that obtained in the case studies in the text are included. A number of additional homework problems encourage the reader to implement and obtain a deeper understanding of the techniques. The reader will obtain a background in data-driven techniques for fault detection and diagnosis, including the ability to implement the techniques and to know how to select the right technique for a particular application.

Modern information systems must handle huge amounts of data having varied natural or technological origins. Automated processing of these increasing signal loads requires the training of specialists capable of formalising the problems encountered. This book supplies a formalised, concise presentation of the basis of statistical signal processing. Equal emphasis is placed on approaches related to signal modelling and to signal estimation. In order to supply the reader with the desirable theoretical fundamentals and to allow him to make progress in the discipline, the results presented here are carefully justified. The representation of random signals in the Fourier domain and their filtering are considered. These tools enable linear prediction theory and related classical filtering techniques to be addressed in a simple way. The spectrum identification problem is presented as a first step toward spectrum estimation, which is studied in non-parametric and parametric frameworks. The later chapters introduce synthetically further advanced techniques that will enable the reader to solve signal processing problems of a general nature.Rather than supplying an exhaustive description of existing techniques, this book is designed for students, scientists and research engineers interested in statistical signal processing and who need to acquire the necessary grounding to address the specific problems with which they may be faced. It also supplies a well-organized introduction to the literature. The CD-ROM contains MATLAB programs in HTML format and is intended to provide simulation examples (program listings + simulation results) In addition, it also presents some basics of probability.

A typical method of dealing with input and output constraints, especially for PID control, has been the empirical design and implementation of an intuitive solution to an individual plant and its constraints. Such ad hoc solutions perform surprisingly well but often remain in the form of isolated practical know-how without a more general scientific basis.Control Systems with Input and Output Constraints develops this empirical approach by demonstrating how particular answers to particular industrial design problems can be systematized to cope with a wide variety of simple and more complex situations. Each class of design problems is presented in five parts: industrial situation; problem statement; currently available control structures; benchmark simulation to obtain typical transient response and nonlinear stability analysis to relate structure to response. Links to optimal control and model predictive control are also explored. These steps help the reader to learn how to choose or adapt a control architecture and design or analyse it on a systematic basis.Features:associated website contains many ready-made examples and case studies using Matlab/Simulink to which extensive reference is made throughout the text facilitating understanding and saving time;exercises interspersed throughout each chapter that allow the student to confirm progress as he/she proceeds;logical five-step design procedure for each problem class moving from a particular industrial example to the informed choice of generic controller and the preparation of closed loop analysis. Control Systems with Input and Output Constraints will be of great benefit to advanced students of control looking to pursue a career in process and/or plant control or in applications-oriented research. The practical basis of the presentation, the systematic design procedure, the restraint on mathematical formalism and emphasis on implementability will also appeal to industrial control engineers.

Although the computing demands of real-time signal processing and control applications are increasing rapidly, parallel processors permit several instructions to be dealt with simultaneously so that the "real-time" needed is manageable. This book introduces the advantages of this strategy and details how to use parallel processing to deal with common signal processing and control algorithms. It emphasises the relationship between the computing requirements of algorithms and the appropriate choice of architectures, while demonstrating how to identify processor capabilities and how to exploit them to the fulleSt. The text includes examples and end-of-chapter exercises to facilitate self- and group study and case studies to put theoretical concepts into a practical context. For advanced students in parallel computing, control and signal processing disciplines, it is an invaluable tool in learning to get the most from their computer systems.

Almost all process systems are nonlinear in nature. Nonlinear control is traditionally an area of interest in process systems engineering which is of great practical importance. These facts notwithstanding, many process engineers have difficulty with the paradigms and results of modern nonlinear control theory because they lack the mathematical background usually associated with such methods or because of their computational difficulty and small-scale applicability in the general case. Analysis and Control of Nonlinear Process Systems overcomes these barriers.Features:- The necessary mathematical preliminaries for readers from a process engineering background- Constant reference to the widely-known finite-dimensional linear time-invariant continuous case as a basis for extension to the nonlinear situation- The most promising theories and analytical methods for nonlinear process control laid out clearly and straightforwardly with exercises to reaffirm the techniques as they are taught- Emphasis on the importance of process knowledge and first-principles-based models in obtaining feasible and effective solutions in particular circumstances from general cases- Illustration of applications with simple examples and case studiesAnalysis and Control of Nonlinear Process Systems will interest graduate process engineers wishing to study advanced control methods either with a view to further research or application in industry as well as to academics seeking to move process control courses into more complicated but up-to-date territory. It will also be a great assistance to those in their senior undergraduate years who will form the next generation of industrial process engineers and need unfussy access to the most modern nonlinear control ideas.

Discrete-time Stochastic Systems gives a comprehensive introduction to the estimation and control of dynamic stochastic systems and provides complete derivations of key results such as the basic relations for Wiener filtering. The book covers both state-space methods and those based on the polynomial approach. Similarities and differences between these approaches are highlighted. Some non-linear aspects of stochastic systems (such as the bispectrum and extended Kalman filter) are also introduced and analysed.The books chief features are as follows: inclusion of the polynomial approach provides alternative and simpler computational methods than simple reliance on state-space methods; algorithms for analysis and design of stochastic systems allow for ease of implementation and experimentation by the reader; the highlighting of spectral factorization gives appropriate emphasis to this key concept often overlooked in the literature; explicit solutions of Wiener problems are handy schemes, well suited for computations compared with more commonly available but abstract formulations; complex-valued models that are directly applicable to many problems in signal processing and communications. Changes in the second edition include: additional information covering spectral factorisation and the innovations form; the chapter on optimal estimation being completely rewritten to focus on a posterior estimates rather than maximum likelihood; new material on fixed lag smoothing and algorithms for solving Riccati equations are improved and more up to date; new presentation of polynomial control and new derivation of linear-quadratic-Gaussian control.Discrete-time Stochastic Systems is primarily of benefit to students taking M.Sc. courses in stochastic estimation and control, electronic engineering and signal processing but may also be of assistance for self study and as a reference.

From power plants to sugar refining, model predictive control (MPC) schemes have established themselves as the preferred control strategies for a wide variety of processes. The second edition of Model Predictive Control provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners. Model Predictive Control demonstrates that a powerful technique does not always require complex control algorithms. The text features material on the following subjects: general MPC elements and algorithms; commercial MPC schemes; generalized predictive control multivariable, robust, constrained nonlinear and hybrid MPC; fast methods for MPC implementation; applications.All of the material is thoroughly updated for the second edition with the chapters on nonlinear MPC, MPC and hybrid systems and MPC implementation being entirely new. Many new exercises and examples have also been added throughout and Matlab programs to aid in their solution can be downloaded from the authors' website at http://www esi.us.es/MPCBOOK.The text is an excellent aid for graduate and advanced undergraduate students and will also be of use to researchers and industrial practitioners wishing to keep abreast of a fast-moving field.

Multivariable control techniques solve issues of complex specification and modelling errors elegantly but the complexity of the underlying mathematics is much higher than presented in traditional single-input, single-output control courses. Multivariable Control Systems focuses on control design with continual references to the practical aspects of implementation. While the concepts of multivariable control are justified, the book emphasises the need to maintain student interest and motivation over exhaustively rigorous mathematical proof. Tools of analysis and representation are always developed as methods for achieving a final control system design and evaluation. Features: - design implementation clearly laid out using extensive reference to Matlab; - combined consideration of systems (plant) and signals (mainly disturbances) in a fluent but simple presentation; - step-by-step approach from the objectives of multivariable control to the solution of complete design problems. Multivariable Control Systems is an ideal text for masters students, students beginning their Ph.D. or for final-year undergraduates looking for more depth than provided by introductory textbooks.It will also interest the control engineer practising in industry and seeking to implement robust or multivariable control solutions to plant problems in as straightforward a manner as possible.

A complete course in self-tuning control, beginning with a survey of adaptive control and the formulation of adaptive control problems. Modelling and identification are dealt with before passing on to algebraic design methods. Finally, laboratory verification and experimentation will ground your theoretical knowledge in real plant control. Features: a strong emphasis on practical problem solving with control algorithms clearly laid out in easy-to-follow formats or as MATLAB functions suitable for use in project work; specially written MATLAB toolboxes for the presentation of typical control system and plant properties and ready for use in direct control of real or simulated plants; worked examples and tutorial exercises. This book shows graduate students and advanced undergraduates how to overcome the problems of putting the tools of adaptive control theory into practice. The text will also be of interest to engineers wishing to employ ideas of adaptive control in their designs and plant.

This is an ideal textbook for one-semester introductory graduate or senior undergraduate courses in adaptive and self-learning systems for signal processing. The material progresses from a short introduction to adaptive systems through modelling, classical filters and spectral analysis to adaptive control theory, nonclassical adaptive systems and applications.Features:- Comprehensive review of linear and stochastic theory.- Design guide for practical application of the least squares estimation method and Kalman filters.- Study of classical adaptive systems together with neural networks, genetic algorithms and fuzzy logic systems and their combination deals with complex problems like underwater acoustic signal processing.- Tutorial problems and exercises which identify significant points and demonstrate the practical relevance of the theory.- PDF Solutions Manual available to tutors containing answers to the tutorial problems, course outlines, sample examination material and project assignments.

Control of Robot Manipulators in Joint Space addresses robot control in depth, treating a range of model-based controllers in detail: proportional derivative; proportional integral derivative; computed torque and some adaptive variants. Combinations of the text?s four parts: robot dynamics and mathematical preliminaries; set-point model-based control; tracking model-based control; and adaptive and velocity-independent control create a complete course in robot control based on joint space for senior undergraduates or masters students. Other areas of study important to robotics, such as kinematics, receive attention within the case studies which are based around a 2-degrees-of-freedom planar articulated arm used throughout to test the controllers under examination by experimentation.In addition to the written text, auxiliary resources are available in the form of pdf projector presentations for the instructor to use in lectures and as printed class aids for students, and a pdf solutions manual.

This textbook presents theory and practice in the context of automatic control education. It presents the relevant theory in the first eight chapters,applying them later on to the control of several real plants. Each plant is studied following a uniform procedure: a) the plant’s functionis described, b) a mathematical model is obtained, c) plant construction is explained in such a way that the reader can build his or her own plant to conduct experiments, d) experiments are conducted to determine the plant’s parameters, e) a controller is designed using the theory discussed in the first eight chapters, f) practical controller implementation is performed in such a way that the reader can build the controller in practice, and g) the experimental results are presented. Moreover, the book provides a wealth of exercises and appendices reviewing the foundations of several concepts and techniques in automatic control. The control system construction proposed is based on inexpensive, easy-to-use hardware. An explicit procedure for obtaining formulas for the oscillation condition and the oscillation frequency of electronic oscillator circuits is demonstrated as well.

This method has been successfully flight-tested on research aircraft, one of various flight-control applications detailed towards the end of the book along with a hybrid adaptive flight control architecture that combines direct MRAC with least-squares indirect adaptive control.

A graduate-level textbook, Hybrid Dynamical Systems provides an accessible and comprehensive introduction to the theory of hybrid systems. It emphasizes results that are central to a good understanding of the importance and role of such systems. The authors have developed the materials in this book while teaching courses on hybrid systems, cyber-physical systems, and formal methods.This textbook helps students to become familiar with both the major approaches coloring the study of hybrid dynamical systems. The computer science and control systems points of view – emphasizing discrete dynamics and real time, and continuous dynamics with switching, respectively – are each covered in detail.The book shows how the behavior of a system with tightly coupled cyber- (discrete) and physical (continuous) elements can best be understood by a model simultaneously encompassing all the dynamics and their interconnections. The theory presented is of fundamental importance in a wide range of emerging fields from next-generation transportation systems to smart manufacturing.Features of the text include:extensive use of examples to illustrate the main concepts and to provide insights additional to those acquired from the main text;chapter summaries enabling students to assess their progress;end-of-chapter exercises, which test learning as a course proceeds;an instructor’s guide showing how different parts of the book can be exploited for different course requirements; anda solutions manual, freely available for download by instructors adopting the book for their teaching.Access to MATLAB and Stateflow is not required but would be beneficial, especially for exercises in which simulations are a key tool.