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| modeling and analysis of dynamic systems: Modeling and Analysis of Dynamic Systems Charles M. Close, Dean K. Frederick, Jonathan C. Newell, 2001-08-20 The third edition of Modeling and Anaysis of Dynamic Systems continues to present students with the methodology applicable to the modeling and analysis of a variety of dynamic systems, regardless of their physical origin. It includes detailed modeling of mechanical, electrical, electro-mechanical, thermal, and fluid systems. Models are developed in the form of state-variable equations, input-output differential equations, transfer functions, and block diagrams. The Laplace transform is used for analytical solutions. Computer solutions are based on MATLAB and Simulink. Examples include both linear and nonlinear systems. An introduction is given to the modeling and design tools for feedback control systems. The text offers considerable flexibility in the selection of material for a specific course. Students majoring in many different engineering disciplines have used the text. Such courses are frequently followed by control-system design courses in the various disciplines. |
| modeling and analysis of dynamic systems: Modeling and Analysis of Dynamic Systems Charles M. Close, Dean K. Frederick, 1993 This text is intended for a first course in dynamic systems and is designed for use by sophomore and junior majors in all fields of engineering, but principally mechanical and electrical engineers. All engineers must understand how dynamic systems work and what responses can be expected from various physical systems. |
| modeling and analysis of dynamic systems: Modeling and Analysis of Dynamic Systems Ramin S. Esfandiari, Bei Lu, 2018-01-29 Modeling and Analysis of Dynamic Systems, Third Edition introduces MATLAB®, Simulink®, and SimscapeTM and then utilizes them to perform symbolic, graphical, numerical, and simulation tasks. Written for senior level courses/modules, the textbook meticulously covers techniques for modeling a variety of engineering systems, methods of response analysis, and introductions to mechanical vibration, and to basic control systems. These features combine to provide students with a thorough knowledge of the mathematical modeling and analysis of dynamic systems. The Third Edition now includes Case Studies, expanded coverage of system identification, and updates to the computational tools included. |
| modeling and analysis of dynamic systems: Dynamic Systems Bingen Yang, Inna Abramova, 2022-11-24 Presenting students with a comprehensive and efficient approach to the modelling, simulation, and analysis of dynamic systems, this textbook addresses mechanical, electrical, thermal and fluid systems, feedback control systems, and their combinations. It features a robust introduction to fundamental mathematical prerequisites, suitable for students from a range of backgrounds; clearly established three-key procedures – fundamental principles, basic elements, and ways of analysis – for students to build on in confidence as they explore new topics; over 300 end-of-chapter problems, with solutions available for instructors, to solidify a hands-on understanding; and clear and uncomplicated examples using MATLAB®/Simulink® and Mathematica®, to introduce students to computational approaches. With a capstone chapter focused on the application of these techniques to real-world engineering problems, this is an ideal resource for a single-semester course in dynamic systems for students in mechanical, aerospace and civil engineering. |
| modeling and analysis of dynamic systems: Solutions Manual [to] Modeling and Analysis of Dynamic Systems Charles M. Close, Dean K. Frederick, 1978 |
| modeling and analysis of dynamic systems: Modeling, Analysis, and Control of Dynamic Systems William John Palm, 1983-01-28 An integrated presentation of both classical and modern methods of systems modeling, response and control. Includes coverage of digital control systems. Details sample data systems and digital control. Provides numerical methods for the solution of differential equations. Gives in-depth information on the modeling of physical systems and central hardware. |
| modeling and analysis of dynamic systems: Understanding Dynamic Systems C. Nelson Dorny, 1993 A textbook that embraces the whole of engineering in a unified context, promoting system thinking by breaking down unnecessary barriers between disciplines. The six chapters address design insights, lumped-network models of systems, lumped-network behavior, equivalence and superposition in linear networks, frequency-response models, and coupling devices. The author uses the text for a two- semester first course in engineering; it has also been used as an integrative course for seniors, primarily in mechanical engineering. Annotation copyright by Book News, Inc., Portland, OR |
| modeling and analysis of dynamic systems: Dynamic Systems Craig A. Kluever, 2019-12-24 The simulation of complex, integrated engineering systems is a core tool in industry which has been greatly enhanced by the MATLAB® and Simulink® software programs. The second edition of Dynamic Systems: Modeling, Simulation, and Control teaches engineering students how to leverage powerful simulation environments to analyze complex systems. Designed for introductory courses in dynamic systems and control, this textbook emphasizes practical applications through numerous case studies—derived from top-level engineering from the AMSE Journal of Dynamic Systems. Comprehensive yet concise chapters introduce fundamental concepts while demonstrating physical engineering applications. Aligning with current industry practice, the text covers essential topics such as analysis, design, and control of physical engineering systems, often composed of interacting mechanical, electrical, and fluid subsystem components. Major topics include mathematical modeling, system-response analysis, and feedback control systems. A wide variety of end-of-chapter problems—including conceptual problems, MATLAB® problems, and Engineering Application problems—help students understand and perform numerical simulations for integrated systems. |
| modeling and analysis of dynamic systems: Modeling, Analysis and Control of Dynamic Systems William J. Palm, 1983 |
| modeling and analysis of dynamic systems: Modeling Dynamic Systems Diana M. Fisher, 2007 |
| modeling and analysis of dynamic systems: Dynamic Systems Bingen Yang, Inna Abramova, 2022 A dynamic system is a combination of components or subsystems, which, with temporal characteristics, interact with each other to perform a specified objective. There exists such a variety of dynamic systems in applications, as machines, devices, appliances, equipment, structures, and industrial processes. Mathematically, a dynamic system is characterized by time-dependent functions or variables, which are governed by a set of differential equations. Physically, the components of a dynamic system may fall in different fields of science and engineering, such as mechanics, thermodynamics, fluid dynamics, vibrations, elasticity, electronics, acoustics, optics, and controls. As an example, an electric motor is a dynamic system consisting of mechanical components (like rotating shaft, bearing and housing), electromagnetic components (such as magnets, coils and electrical interconnects), and components for controlling the motor speed (including speed sensor, control logic board and driver). These components interact with each other to achieve a desired motor speed. The rotation speed and circuit currents are time-dependent variables of the motor that are governed by differential equations in the fields of dynamics and electromagnetism-- |
| modeling and analysis of dynamic systems: The Art of Modeling Dynamic Systems Foster Morrison, 2012-03-07 This text illustrates the roles of statistical methods, coordinate transformations, and mathematical analysis in mapping complex, unpredictable dynamical systems. It describes the benefits and limitations of the available modeling tools, showing engineers and scientists how any system can be rendered simpler and more predictable. Written by a well-known authority in the field, this volume employs practical examples and analogies to make models more meaningful. The more universal methods appear in considerable detail, and advanced dynamic principles feature easy-to-understand examples. The text draws careful distinctions between mathematical abstractions and observable realities. Additional topics include the role of pure mathematics, the limitations of numerical methods, forecasting in the presence of chaos and randomness, and dynamics without calculus. Specialized techniques and case histories are coordinated with a carefully selected and annotated bibliography. The original edition was a Library of Science Main Selection in May, 1991. This new Dover edition features corrections by the author and a new Preface. |
| modeling and analysis of dynamic systems: Modeling and Analysis of Dynamic Systems Ramin S. Esfandiari, Bei Lu, 2014-04-24 Modeling and Analysis of Dynamic Systems, Second Edition introduces MATLAB®, Simulink®, and SimscapeTM and then uses them throughout the text to perform symbolic, graphical, numerical, and simulation tasks. Written for junior or senior level courses, the textbook meticulously covers techniques for modeling dynamic systems, methods of response analysis, and provides an introduction to vibration and control systems. These features combine to provide students with a thorough knowledge of the mathematical modeling and analysis of dynamic systems. See What’s New in the Second Edition: Coverage of modeling and analysis of dynamic systems ranging from mechanical to thermal using Simscape Utilization of Simulink for linearization as well as simulation of nonlinear dynamic systems Integration of Simscape into Simulink for control system analysis and design Each topic covered includes at least one example, giving students better comprehension of the subject matter. More complex topics are accompanied by multiple, painstakingly worked-out examples. Each section of each chapter is followed by several exercises so that students can immediately apply the ideas just learned. End-of-chapter review exercises help in learning how a combination of different ideas can be used to analyze a problem. This second edition of a bestselling textbook fully integrates the MATLAB Simscape Toolbox and covers the usage of Simulink for new purposes. It gives students better insight into the involvement of actual physical components rather than their mathematical representations. |
| modeling and analysis of dynamic systems: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| modeling and analysis of dynamic systems: Modeling Dynamic Economic Systems Matthias Ruth, Bruce Hannon, 2012-02-09 This book explores the dynamic processes in economic systems, concentrating on the extraction and use of the natural resources required to meet economic needs. Sections cover methods for dynamic modeling in economics, microeconomic models of firms, modeling optimal use of both nonrenewable and renewable resources, and chaos in economic models. This book does not require a substantial background in mathematics or computer science. |
| modeling and analysis of dynamic systems: Dynamic Systems Hung V. Vu, Ramin S. Esfandiari, 1998 |
| modeling and analysis of dynamic systems: Modelling and Parameter Estimation of Dynamic Systems J.R. Raol, G. Girija, J. Singh, 2004-08-13 This book presents a detailed examination of the estimation techniques and modeling problems. The theory is furnished with several illustrations and computer programs to promote better understanding of system modeling and parameter estimation. |
| modeling and analysis of dynamic systems: Modeling and Simulation of Dynamic Systems Robert L. Woods, Kent L. Lawrence, 1997 Introduction to modeling and simulation - Models for dynamic systems and systems similarity - Modeling of engineering systems - Mechanical systems - Electrical systems - Fluid systems - Thermal systems - Mixed discipline systems - System dynamic response analysis - Frequency response - Time response and digital simulation - Engineering applications - System design and selection of components. |
| modeling and analysis of dynamic systems: Dynamic Modeling Bruce Hannon, Matthias Ruth, 2013-11-11 Dynamic Modeling introduces an approach to modeling that makes it a more practical, intuitive endeavour. The book enables readers to convert their understanding of a phenomenon to a computer model, and then to run the model and let it yield the inevitable dynamic consequences built into the structure of the model. Part I provides an introduction to modeling dynamic systems, while Part II offers general methods for modeling. Parts III through to VIII then apply these methods to model real-world phenomena from chemistry, genetics, ecology, economics, and engineering. To develop and execute dynamic simulation models, Dynamic Modeling comes with STELLA II run- time software for Windows-based computers, as well as computer files of sample models used in the book. A clear, approachable introduction to the modeling process, of interest in any field where real problems can be illuminated by computer simulation. |
| modeling and analysis of dynamic systems: Dynamic Modeling and Control of Engineering Systems Bohdan T. Kulakowski, John F. Gardner, J. Lowen Shearer, 2014-04-30 This textbook is ideal for an undergraduate course in Engineering System Dynamics and Controls. It is intended to provide the reader with a thorough understanding of the process of creating mathematical (and computer-based) models of physical systems. The material is restricted to lumped parameter models, which are those models in which time is the only independent variable. It assumes a basic knowledge of engineering mechanics and ordinary differential equations. The new edition has expanded topical coverage and many more new examples and exercises. |
| modeling and analysis of dynamic systems: Mechanism Analysis Lyndon O. Barton, 2016-04-19 This updated and enlarged Second Edition provides in-depth, progressive studies of kinematic mechanisms and offers novel, simplified methods of solving typical problems that arise in mechanisms synthesis and analysis - concentrating on the use of algebra and trigonometry and minimizing the need for calculus.;It continues to furnish complete coverag |
| modeling and analysis of dynamic systems: Dynamic Response of Linear Mechanical Systems Jorge Angeles, 2011-09-15 Dynamic Response of Linear Mechanical Systems: Modeling, Analysis and Simulation can be utilized for a variety of courses, including junior and senior-level vibration and linear mechanical analysis courses. The author connects, by means of a rigorous, yet intuitive approach, the theory of vibration with the more general theory of systems. The book features: A seven-step modeling technique that helps structure the rather unstructured process of mechanical-system modeling A system-theoretic approach to deriving the time response of the linear mathematical models of mechanical systems The modal analysis and the time response of two-degree-of-freedom systems—the first step on the long way to the more elaborate study of multi-degree-of-freedom systems—using the Mohr circle Simple, yet powerful simulation algorithms that exploit the linearity of the system for both single- and multi-degree-of-freedom systems Examples and exercises that rely on modern computational toolboxes for both numerical and symbolic computations as well as a Solutions Manual for instructors, with complete solutions of a sample of end-of-chapter exercises Chapters 3 and 7, on simulation, include in each “Exercises” section a set of miniprojects that require code-writing to implement the algorithms developed in these chapters |
| modeling and analysis of dynamic systems: Modeling and Control of Discrete-event Dynamic Systems Branislav Hrúz, MengChu Zhou, 2007-08-17 Discrete-event dynamic systems (DEDs) permeate our world. They are of great importance in modern manufacturing processes, transportation and various forms of computer and communications networking. This book begins with the mathematical basics required for the study of DEDs and moves on to present various tools used in their modeling and control. Industrial examples illustrate the concepts and methods discussed, making this book an invaluable aid for students embarking on further courses in control, manufacturing engineering or computer studies. |
| modeling and analysis of dynamic systems: Modeling Dynamic Biological Systems Bruce Hannon, Matthias Ruth, 2012-12-06 Models help us understand the dynamics of real-world processes by using the computer to mimic the actual forces that are known or assumed to result in a system's behavior. This book does not require a substantial background in mathematics or computer science. |
| modeling and analysis of dynamic systems: Modelling and Simulation Louis G. Birta, Gilbert Arbez, 2007-09-07 This book provides a balanced and integrated presentation of modelling and simulation activity for both Discrete Event Dynamic Systems (DEDS) and Continuous Time Dynamic Systems (CYDS). The authors establish a clear distinction between the activity of modelling and that of simulation, maintaining this distinction throughout. The text offers a novel project-oriented approach for developing the modelling and simulation methodology, providing a solid basis for demonstrating the dependency of model structure and granularity on project goals. Comprehensive presentation of the verification and validation activities within the modelling and simulation context is also shown. |
| modeling and analysis of dynamic systems: Modeling, Analysis and Control of Dynamic Elastic Multi-Link Structures J.E. Lagnese, Günter Leugering, E.J.P.G. Schmidt, 2012-12-06 The purpose of this monograph is threefold. First, mathematical models of the transient behavior of some or all of the state variables describing the motion of multiple-link flexible structures will be developed. The structures which we have in mind consist of finitely many interconnected flexible ele ments such as strings, beams, plates and shells or combinations thereof and are representative of trusses, frames, robot arms, solar panels, antennae, deformable mirrors, etc. , currently in use. For example, a typical subsys tem found in almost all aircraft and space vehicles consists of beam, plate and/or shell elements attached to each other in a rigid or flexible manner. Due to limitations on their weights, the elements themselves must be highly flexible, and due to limitations on their initial configuration (i. e. , before de ployment), those aggregates often have to contain several links so that the substructure may be unfolded or telescoped once it is deployed. The point of view we wish to adopt is that in order to understand completely the dynamic response of a complex elastic structure it is not sufficient to con to take into account the sider only its global motion but also necessary flexibility of individual elements and the interaction and transmission of elastic effects such as bending, torsion and axial deformations at junctions where members are connected to each other. The second object of this book is to provide rigorous mathematical analyses of the resulting models. |
| modeling and analysis of dynamic systems: Analytical Methods for Dynamic Modelers Hazhir Rahmandad, Rogelio Oliva, Nathaniel D. Osgood, 2015-11-27 A user-friendly introduction to some of the most useful analytical tools for model building, estimation, and analysis, presenting key methods and examples. Simulation modeling is increasingly integrated into research and policy analysis of complex sociotechnical systems in a variety of domains. Model-based analysis and policy design inform a range of applications in fields from economics to engineering to health care. This book offers a hands-on introduction to key analytical methods for dynamic modeling. Bringing together tools and methodologies from fields as diverse as computational statistics, econometrics, and operations research in a single text, the book can be used for graduate-level courses and as a reference for dynamic modelers who want to expand their methodological toolbox. The focus is on quantitative techniques for use by dynamic modelers during model construction and analysis, and the material presented is accessible to readers with a background in college-level calculus and statistics. Each chapter describes a key method, presenting an introduction that emphasizes the basic intuition behind each method, tutorial style examples, references to key literature, and exercises. The chapter authors are all experts in the tools and methods they present. The book covers estimation of model parameters using quantitative data; understanding the links between model structure and its behavior; and decision support and optimization. An online appendix offers computer code for applications, models, and solutions to exercises. Contributors Wenyi An, Edward G. Anderson Jr., Yaman Barlas, Nishesh Chalise, Robert Eberlein, Hamed Ghoddusi, Winfried Grassmann, Peter S. Hovmand, Mohammad S. Jalali, Nitin Joglekar, David Keith, Juxin Liu, Erling Moxnes, Rogelio Oliva, Nathaniel D. Osgood, Hazhir Rahmandad, Raymond Spiteri, John Sterman, Jeroen Struben, Burcu Tan, Karen Yee, Gönenç Yücel |
| modeling and analysis of dynamic systems: Distributed-Order Dynamic Systems Zhuang Jiao, YangQuan Chen, Igor Podlubny, 2012-02-26 Distributed-order differential equations, a generalization of fractional calculus, are of increasing importance in many fields of science and engineering from the behaviour of complex dielectric media to the modelling of nonlinear systems. This Brief will broaden the toolbox available to researchers interested in modeling, analysis, control and filtering. It contains contextual material outlining the progression from integer-order, through fractional-order to distributed-order systems. Stability issues are addressed with graphical and numerical results highlighting the fundamental differences between constant-, integer-, and distributed-order treatments. The power of the distributed-order model is demonstrated with work on the stability of noncommensurate-order linear time-invariant systems. Generic applications of the distributed-order operator follow: signal processing and viscoelastic damping of a mass–spring set up. A new general approach to discretization of distributed-order derivatives and integrals is described. The Brief is rounded out with a consideration of likely future research and applications and with a number of MATLAB® codes to reduce repetitive coding tasks and encourage new workers in distributed-order systems. |
| modeling and analysis of dynamic systems: Modelling and Control of Dynamic Systems Using Gaussian Process Models Juš Kocijan, 2015-11-21 This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download. |
| modeling and analysis of dynamic systems: Modeling, Analysis And Control Of Dynamical Systems With Friction And Impacts Pawel Olejnik, Jan Awrejcewicz, Michal Feckan, 2017-07-07 This book is aimed primarily towards physicists and mechanical engineers specializing in modeling, analysis, and control of discontinuous systems with friction and impacts. It fills a gap in the existing literature by offering an original contribution to the field of discontinuous mechanical systems based on mathematical and numerical modeling as well as the control of such systems. Each chapter provides the reader with both the theoretical background and results of verified and useful computations, including solutions of the problems of modeling and application of friction laws in numerical computations, results from finding and analyzing impact solutions, the analysis and control of dynamical systems with discontinuities, etc. The contents offer a smooth correspondence between science and engineering and will allow the reader to discover new ideas. Also emphasized is the unity of diverse branches of physics and mathematics towards understanding complex piecewise-smooth dynamical systems. Mathematical models presented will be important in numerical experiments, experimental measurements, and optimization problems found in applied mechanics. |
| modeling and analysis of dynamic systems: Fractional-order Modeling and Control of Dynamic Systems Aleksei Tepljakov, 2017-02-08 This book reports on an outstanding research devoted to modeling and control of dynamic systems using fractional-order calculus. It describes the development of model-based control design methods for systems described by fractional dynamic models. More than 300 years had passed since Newton and Leibniz developed a set of mathematical tools we now know as calculus. Ever since then the idea of non-integer derivatives and integrals, universally referred to as fractional calculus, has been of interest to many researchers. However, due to various issues, the usage of fractional-order models in real-life applications was limited. Advances in modern computer science made it possible to apply efficient numerical methods to the computation of fractional derivatives and integrals. This book describes novel methods developed by the author for fractional modeling and control, together with their successful application in real-world process control scenarios. |
| modeling and analysis of dynamic systems: Dynamic Modeling in the Health Sciences James L. Hargrove, 1998-06-02 This book and CD-ROM package integrates the use of STELLA software into the teaching of health, nutrition and physiology, and may be used on its own in nutrition and physiology courses, or can serve as a supplement to introduce the role that simulation modelling can play. The author presents key subjects ranging from the theory of metabolic control, through weight regulation to bone metabolism, and gives readers the tools to simulate these using the STELLA software. Topics include methods for simulation of gene expression, a multi-stage model of tumour development, theories of ageing, circadian rhythms and physiological time, as well as a model for managing weight loss and preventing obesity. |
| modeling and analysis of dynamic systems: Solutions Manual, Modeling and Analysis of Dynamic Systems, Second Edition Charles M. Close, 1994-12-09 |
| modeling and analysis of dynamic systems: Dynamic Modeling of Environmental Systems Michael L. Deaton, James J. Winebrake, 2012-12-06 A primer on modeling concepts and applications that is specifically geared toward the environmental field. Sections on modeling terminology, the uses of models, the model-building process, and the interpretation of output provide the foundation for detailed applications. After an introduction to the basics of dynamic modeling, the book leads students through an analysis of several environmental problems, including surface-water pollution, matter-cycling disruptions, and global warming. The scientific and technical context is provided for each problem, and the methods for analyzing and designing appropriate modeling approaches is provided. While the mathematical content does not exceed the level of a first-semester calculus course, the book gives students all of the background, examples, and practice exercises needed both to use and understand environmental modeling. It is suitable for upper-level undergraduate and beginning-graduate level environmental professionals seeking an introduction to modeling in their field. |
| modeling and analysis of dynamic systems: Discrete-Event Modeling and Simulation Gabriel A. Wainer, Pieter J. Mosterman, 2018-09-03 Collecting the work of the foremost scientists in the field, Discrete-Event Modeling and Simulation: Theory and Applications presents the state of the art in modeling discrete-event systems using the discrete-event system specification (DEVS) approach. It introduces the latest advances, recent extensions of formal techniques, and real-world examples of various applications. The book covers many topics that pertain to several layers of the modeling and simulation architecture. It discusses DEVS model development support and the interaction of DEVS with other methodologies. It describes different forms of simulation supported by DEVS, the use of real-time DEVS simulation, the relationship between DEVS and graph transformation, the influence of DEVS variants on simulation performance, and interoperability and composability with emphasis on DEVS standardization. The text also examines extensions to DEVS, new formalisms, and abstractions of DEVS models as well as the theory and analysis behind real-world system identification and control. To support the generation and search of optimal models of a system, a framework is developed based on the system entity structure and its transformation to DEVS simulation models. In addition, the book explores numerous interesting examples that illustrate the use of DEVS to build successful applications, including optical network-on-chip, construction/building design, process control, workflow systems, and environmental models. A one-stop resource on advances in DEVS theory, applications, and methodology, this volume offers a sampling of the best research in the area, a broad picture of the DEVS landscape, and trend-setting applications enabled by the DEVS approach. It provides the basis for future research discoveries and encourages the development of new applications. |
| modeling and analysis of dynamic systems: Modeling of Dynamic Systems with Engineering Applications Clarence W. de Silva, 2017-10-16 MODELING OF DYNAMIC SYSTEMS takes a unique, up-to-date approach to systems dynamics and related controls coverage for undergraduate students and practicing engineers. It focuses on the model development of engineering problems rather than response analysis and simulation once a model is available, though these are also covered. Linear graphing and bond graph approaches are both discussed, and computational tools are integrated thoughout. Electrical, mechanical, fluid, and thermal domains are covered, as are problems of multiple domains (mixed systems); the unified and integrated approaches taken are rapidly becoming the standard in the modeling of mechatronic engineering systems. |
| modeling and analysis of dynamic systems: Reliability Analysis of Dynamic Systems Bin Wu, 2013-06-19 Featuring aerospace examples and applications, Reliability Analysis of Dynamic Systems presents the very latest probabilistic techniques for accurate and efficient dynamic system reliability analysis. While other books cover more broadly the reliability techniques and challenges related to large systems, Dr Bin Wu presents a focused discussion of new methods particularly relevant to the reliability analysis of large aerospace systems under harmonic loads in the low frequency range. Developed and written to help you respond to challenges such as non-linearity of the failure surface, intensive computational costs and complexity in your dynamic system, Reliability Analysis of Dynamic Systems is a specific, detailed and application-focused reference for engineers, researchers and graduate students looking for the latest modeling solutions. The Shanghai Jiao Tong University Press Aerospace Series publishes titles that cover the latest advances in research and development in aerospace. Its scope includes theoretical studies, design methods, and real-world implementations and applications. The readership for the series is broad, reflecting the wide range of aerospace interest and application, but focuses on engineering. Forthcoming titles in the Shanghai Jiao Tong University Press Aerospace Series: Reliability Analysis of Dynamic Systems • Wake Vortex Control • Aeroacoustics: Fundamentals and Applications in Aeropropulsion Systems • Computational Intelligence in Aerospace Design • Unsteady Flow and Aeroelasticity in Turbomachinery - Authored by a leading figure in Chinese aerospace with 20 years' professional experience in reliability analysis and engineering simulation. - Offers solutions to the challenges of non-linearity, intensive computational cost and complexity in reliability assessment. - Aerospace applications and examples used throughout to illustrate accuracy and efficiency achieved with new methods. |
| modeling and analysis of dynamic systems: Dynamic Data Analysis James Ramsay, Giles Hooker, 2017-06-27 This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) Functional Data Analysis. The central concept of a dynamical system as a buffer that translates sudden changes in input into smooth controlled output responses has led to applications of previously analyzed data, opening up entirely new opportunities for dynamical systems. The technical level has been kept low so that those with little or no exposure to differential equations as modeling objects can be brought into this data analysis landscape. There are already many texts on the mathematical properties of ordinary differential equations, or dynamic models, and there is a large literature distributed over many fields on models for real world processes consisting of differential equations. However, a researcher interested in fitting such a model to data, or a statistician interested in the properties of differential equations estimated from data will find rather less to work with. This book fills that gap. |
| modeling and analysis of dynamic systems: Analysis of Dynamic Psychological Systems H.E. Fitzgerald, R.L. Levine, 2013-03-08 Drawing on sources from a wide range of disciplines, this first volume of a two volume tutorial on systems theory focuses on non-linear dynamical techniques for analysis of feedback processes, information flow, decision making, control theory, and modeling of human behavioral systems. |
| modeling and analysis of dynamic systems: Fractional-Order Modeling of Dynamic Systems with Applications in Optimization, Signal Processing, and Control Ahmed G. Radwan, Farooq Ahmad Khanday, Lobna A. Said, 2021-10-22 Fractional-order Modelling of Dynamic Systems with Applications in Optimization, Signal Processing and Control introduces applications from a design perspective, helping readers plan and design their own applications. The book includes the different techniques employed to design fractional-order systems/devices comprehensively and straightforwardly. Furthermore, mathematics is available in the literature on how to solve fractional-order calculus for system applications. This book introduces the mathematics that has been employed explicitly for fractional-order systems. It will prove an excellent material for students and scholars who want to quickly understand the field of fractional-order systems and contribute to its different domains and applications. Fractional-order systems are believed to play an essential role in our day-to-day activities. Therefore, several researchers around the globe endeavor to work in the different domains of fractional-order systems. The efforts include developing the mathematics to solve fractional-order calculus/systems and to achieve the feasible designs for various applications of fractional-order systems. - Presents a simple and comprehensive understanding of the field of fractional-order systems - Offers practical knowledge on the design of fractional-order systems for different applications - Exposes users to possible new applications for fractional-order systems |
Modeling and Analysis of Dynamic Systems - ETH Zürich
Learn how to define and analyze the zero dynamics of a linear system, which correspond to its behavior for those inputs and initial conditions that make the output zero. See the equivalence between the transfer function and the state-space representation, and the influence of the zeros on the system stability and control.
Modeling and Analysis of Dynamic Systems, Second …
Modeling and Analysis of Dynamic Systems, Second Edition introduces MATLAB ®, Simulink ®, and Simscape™ and then uses them throughout the text to perform symbolic, graphical, numerical, and simulation tasks. Written for junior or senior level courses, the textbook meticulously covers techniques for modeling dynamic systems, methods of ...
Modeling and Analysis of Dynamic Systems - ETH Zürich
Introduction. System Modeling for Control. Dynamic Systems. systems that are not static, i.e., their state evolves w.r.t. time, due to: input signals, external perturbations, or naturally. For example, a dynamic system is a system which changes: its trajectory → changes in acceleration, orientation, velocity, position.
Modeling and Analysis of Dynamic Systems - ETH Zürich
Modeling and Analysis of Dynamic Systems. Dr. Guillaume Ducard. Fall 2017. Institute for Dynamic Systems and Control. ETH Zurich, Switzerland. Lagrange: 1736 -1813. Lagrange Formalism: Recipe. Define inputs and outputs. Define the generalized coordinates: q(t) = [q1(t), q2(t), . . . , qn(t)] and ̇q(t) = [ ̇q1(t), ̇q2(t), . . . , ̇qn(t)]
Modeling and Analysis of Dynamic Systems, 3rd Edition
modeling and analysis of a variety of dynamic systems, regardless of their physical origin. It includes detailed modeling of mechanical, electrical, electro-mechanical, thermal, and fluid systems.
Dynamic Systems and Control Engineering - Cambridge …
A textbook that covers the fundamentals, techniques, and tools of multi-domain modeling, analysis, simulation, and control of dynamic systems. It includes MATLAB®, Simulink®, and SimscapeTM examples and exercises for senior undergraduates and graduates.
Chapter 3 MATHEMATICAL MODELING OF DYNAMIC SYSTEMS …
Learn how to model dynamic systems using differential equations, Laplace transforms and block diagrams. Find out how to calculate and use transfer functions for linear systems, impulse response and closed loop control systems.
Modeling, Analysis, & Control of Dynamic Systems: …
Modeling, Analysis, & Control K. Craig 7 • Essential Features of the Study of Dynamic Systems – Deals with entire operating machines and processes rather than just isolated components. – Treats dynamic behavior of mechanical, electrical, fluid, thermal, and mixed systems. – Emphasizes the behavioral similarity between systems
Modeling and Analysis of Dynamic Systems
Title: Modeling and analysis of dynamic systems / Ramin S. Esfandiari & Bei Lu. Description: Third edition. | Boca Raton : Taylor & Francis, CRC Press, 2018. | Includes bibliographical …
Dynamic Systems - Cambridge University Press
Presenting students with a comprehensive and efficient approach to the modeling, simula-tion, and analysis of dynamic systems, this textbook addresses mechanical, electrical, ther-mal and fluid systems, feedback control systems, and their combinations.
Modeling of Multi-Domain Dynamic Systems Part I
Introduction to the concepts of dynamic systems, system decomposition, and system models. Discussion of how systems and models are classified by their key characteristics. Review of the superposition principle.
Modeling and Analysis of Dynamic Systems - ETH Zürich
Modeling and Analysis of Dynamic Systems. by Dr. Guillaume Ducard. Fall 2016. Institute for Dynamic Systems and Control. ETH Zurich, Switzerland. Lecture 4: Modeling Tools for Mechanical Systems. Lagrange Method with Kinematic Constraints Ball on Wheel. Lecture 4: Hydraulic Systems. Water Duct Compressible Duct Element.
ME 560 Modeling and Analysis of Dynamic Systems
systems. This then allows causality, as well as system analysis tools to be used to determine the correctness of the modeling assumptions. System analysis techniques include: free and forced response, model solutions, eigenvalues and eigenvectors, s-plane analysis and frequency response methods.
Modeling and Analysis of Dynamic Systems - ETH Zürich
Planning Experiments. Least Squares Methods for Linear Systems Solution of the Least Squares Problem. Iterative Least Squares. Problem Definition Least Squares with Exponential Forgetting Simplified Recursive LS Algorithm. You came up with a mathematical model of a system, which contains some parameters (ex: mass, elasticity, specific heat,...).
Modeling, Analysis & Control of Dynamic Systems - NYU …
Physical Modeling - General K. Craig 6 – In modeling dynamic systems, we consider matter and energy as being continuously, though not necessarily uniformly, distributed over the space within the system boundaries. – This is the macroscopic or continuum point of view. We consider the system variables as quantities which change continuously from
MECH 370: Modelling, Simulation and Analysis of Physical Systems
Learn how to build mathematical models of physical systems from first principles and use software tools (e.g. Matlab/Simulink) for modelling, simulation, and analysis. The course covers translational, rotational, electrical, thermal and fluid systems, as well as …
Modeling and Analysis of Dynamic Systems - ETH Zürich
Lecture 7: Fluiddynamic Systems Simplified Model of a Gas Turbine Thermodynamics Principles 1 & 2 Entropy Variation First principle of thermodynamics In many industrial applications, a continuous flow of fluids goes through the (thermodynamic) system. This is no longer a closed system, we talk about “open systems” (valve, turbine ...
Modeling, analysis, and control of dynamic systems - ICDST
This text is an introduction to modeling and analysis of dynamic systems, and to the design of controllers for such systems. It is assumed that the student has a background in calculus and college physics (mechanics, thermodynamics, and electrical circuits).
Modeling and Analysis of Dynamic Systems - ETH Zürich
Modeling and Analysis of Dynamic Systems. Dr. Guillaume Ducard. Fall 2017. Institute for Dynamic Systems and Control. ETH Zurich, Switzerland. Lecture 6: Electromechanical Systems. Recalls Case study: Loudspeaker. Lecture 6: Thermodynamics Systems. Internal Energy Enthalpy Heat Transfers. Examples.
Modeling and Analysis of Dynamic Systems, 3rd Edition
modeling and analysis of a variety of dynamic systems, regardless of their physical origin. It includes detailed modeling of mechanical, electrical, electro-mechanical, thermal, and fluid systems.
Modeling and Analysis of Dynamic Systems - ETH Zürich
Learn how to define and analyze the zero dynamics of a linear system, which correspond to its behavior for those inputs and initial conditions that make the output zero. See the equivalence between the transfer function and the state-space representation, and the influence of the zeros on the system stability and control.
Modeling and Analysis of Dynamic Systems, Second Edition
Modeling and Analysis of Dynamic Systems, Second Edition introduces MATLAB ®, Simulink ®, and Simscape™ and then uses them throughout the text to perform symbolic, graphical, numerical, and simulation tasks. Written for junior or senior level courses, the textbook meticulously covers techniques for modeling dynamic systems, methods of ...
Modeling and Analysis of Dynamic Systems - ETH Zürich
Introduction. System Modeling for Control. Dynamic Systems. systems that are not static, i.e., their state evolves w.r.t. time, due to: input signals, external perturbations, or naturally. For example, a dynamic system is a system which changes: its trajectory → changes in acceleration, orientation, velocity, position.
Modeling and Analysis of Dynamic Systems - ETH Zürich
Modeling and Analysis of Dynamic Systems. Dr. Guillaume Ducard. Fall 2017. Institute for Dynamic Systems and Control. ETH Zurich, Switzerland. Lagrange: 1736 -1813. Lagrange Formalism: Recipe. Define inputs and outputs. Define the generalized coordinates: q(t) = [q1(t), q2(t), . . . , qn(t)] and ̇q(t) = [ ̇q1(t), ̇q2(t), . . . , ̇qn(t)]
Modeling and Analysis of Dynamic Systems, 3rd Edition - Wiley
modeling and analysis of a variety of dynamic systems, regardless of their physical origin. It includes detailed modeling of mechanical, electrical, electro-mechanical, thermal, and fluid systems.
Dynamic Systems and Control Engineering - Cambridge …
A textbook that covers the fundamentals, techniques, and tools of multi-domain modeling, analysis, simulation, and control of dynamic systems. It includes MATLAB®, Simulink®, and SimscapeTM examples and exercises for senior undergraduates and graduates.
Chapter 3 MATHEMATICAL MODELING OF DYNAMIC SYSTEMS
Learn how to model dynamic systems using differential equations, Laplace transforms and block diagrams. Find out how to calculate and use transfer functions for linear systems, impulse response and closed loop control systems.
Modeling, Analysis, & Control of Dynamic Systems: Introduction
Modeling, Analysis, & Control K. Craig 7 • Essential Features of the Study of Dynamic Systems – Deals with entire operating machines and processes rather than just isolated components. – Treats dynamic behavior of mechanical, electrical, fluid, thermal, and mixed systems. – Emphasizes the behavioral similarity between systems
Modeling and Analysis of Dynamic Systems
Title: Modeling and analysis of dynamic systems / Ramin S. Esfandiari & Bei Lu. Description: Third edition. | Boca Raton : Taylor & Francis, CRC Press, 2018. | Includes bibliographical …
Dynamic Systems - Cambridge University Press & Assessment
Presenting students with a comprehensive and efficient approach to the modeling, simula-tion, and analysis of dynamic systems, this textbook addresses mechanical, electrical, ther-mal and fluid systems, feedback control systems, and their combinations.
Modeling of Multi-Domain Dynamic Systems Part I
Introduction to the concepts of dynamic systems, system decomposition, and system models. Discussion of how systems and models are classified by their key characteristics. Review of the superposition principle.
Modeling and Analysis of Dynamic Systems - ETH Zürich
Modeling and Analysis of Dynamic Systems. by Dr. Guillaume Ducard. Fall 2016. Institute for Dynamic Systems and Control. ETH Zurich, Switzerland. Lecture 4: Modeling Tools for Mechanical Systems. Lagrange Method with Kinematic Constraints Ball on Wheel. Lecture 4: Hydraulic Systems. Water Duct Compressible Duct Element.
ME 560 Modeling and Analysis of Dynamic Systems
systems. This then allows causality, as well as system analysis tools to be used to determine the correctness of the modeling assumptions. System analysis techniques include: free and forced response, model solutions, eigenvalues and eigenvectors, s-plane analysis and frequency response methods.
Modeling and Analysis of Dynamic Systems - ETH Zürich
Planning Experiments. Least Squares Methods for Linear Systems Solution of the Least Squares Problem. Iterative Least Squares. Problem Definition Least Squares with Exponential Forgetting Simplified Recursive LS Algorithm. You came up with a mathematical model of a system, which contains some parameters (ex: mass, elasticity, specific heat,...).
Modeling, Analysis & Control of Dynamic Systems - NYU …
Physical Modeling - General K. Craig 6 – In modeling dynamic systems, we consider matter and energy as being continuously, though not necessarily uniformly, distributed over the space within the system boundaries. – This is the macroscopic or continuum point of view. We consider the system variables as quantities which change continuously from
MECH 370: Modelling, Simulation and Analysis of Physical Systems
Learn how to build mathematical models of physical systems from first principles and use software tools (e.g. Matlab/Simulink) for modelling, simulation, and analysis. The course covers translational, rotational, electrical, thermal and fluid systems, as well as …
Modeling and Analysis of Dynamic Systems - ETH Zürich
Lecture 7: Fluiddynamic Systems Simplified Model of a Gas Turbine Thermodynamics Principles 1 & 2 Entropy Variation First principle of thermodynamics In many industrial applications, a continuous flow of fluids goes through the (thermodynamic) system. This is no longer a closed system, we talk about “open systems” (valve, turbine ...
Modeling, analysis, and control of dynamic systems - ICDST
This text is an introduction to modeling and analysis of dynamic systems, and to the design of controllers for such systems. It is assumed that the student has a background in calculus and college physics (mechanics, thermodynamics, and electrical circuits).
Modeling and Analysis of Dynamic Systems - ETH Zürich
Modeling and Analysis of Dynamic Systems. Dr. Guillaume Ducard. Fall 2017. Institute for Dynamic Systems and Control. ETH Zurich, Switzerland. Lecture 6: Electromechanical Systems. Recalls Case study: Loudspeaker. Lecture 6: Thermodynamics Systems. Internal Energy Enthalpy Heat Transfers. Examples.
Modeling and Analysis of Dynamic Systems, 3rd Edition - Wiley
modeling and analysis of a variety of dynamic systems, regardless of their physical origin. It includes detailed modeling of mechanical, electrical, electro-mechanical, thermal, and fluid systems.
