Developed from the author's academic and industrial experiences, Modeling and Control of Engineering Systems provides a unified treatment of the modeling of mechanical, electrical, fluid, and thermal systems and then systematically covers conventional, advanced, and intelligent control, instrumentation, experimentation, and design. It includes theo
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.
A practical and straightforward exploration of the basic tools for the modeling, analysis, and design of control systems In An Introduction to System Modeling and Control, Dr. Chiasson delivers an accessible and intuitive guide to understanding modeling and control for students in electrical, mechanical, and aerospace/aeronautical engineering. The book begins with an introduction to the need for control by describing how an aircraft flies complete with figures illustrating roll, pitch, and yaw control using its ailerons, elevators, and rudder, respectively. The book moves on to rigid body dynamics about a single axis (gears, cart rolling down an incline) and then to modeling DC motors, DC tachometers, and optical encoders. Using the transfer function representation of these dynamic models, PID controllers are introduced as an effective way to track step inputs and reject constant disturbances. It is further shown how any transfer function model can be stabilized using output pole placement and on how two-degree of freedom controllers can be used to eliminate overshoot in step responses. Bode and Nyquist theory are then presented with an emphasis on how they give a quantitative insight into a control system's robustness and sensitivity. An Introduction to System Modeling and Control closes with chapters on modeling an inverted pendulum and a magnetic levitation system, trajectory tracking control using state feedback, and state estimation. In addition the book offers: A complete set of MATLAB/SIMULINK files for examples and problems included in the book. A set of lecture slides for each chapter. A solutions manual with recommended problems to assign. An analysis of the robustness and sensitivity of four different controller designs for an inverted pendulum (cart-pole). Perfect for electrical, mechanical, and aerospace/aeronautical engineering students, An Introduction to System Modeling and Control will also be an invaluable addition to the libraries of practicing engineers.
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.
Comprehension of complex systems comes from an understanding of not only the behavior of constituent elements but how they act together to form the behavior of the whole. However, given the multidisciplinary nature of complex systems, the scattering of information across different areas creates a chaotic situation for those trying to understand pos
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.
Process Modelling and Model Analysis describes the use of models in process engineering. Process engineering is all about manufacturing--of just about anything! To manage processing and manufacturing systematically, the engineer has to bring together many different techniques and analyses of the interaction between various aspects of the process. For example, process engineers would apply models to perform feasibility analyses of novel process designs, assess environmental impact, and detect potential hazards or accidents. To manage complex systems and enable process design, the behavior of systems is reduced to simple mathematical forms. This book provides a systematic approach to the mathematical development of process models and explains how to analyze those models. Additionally, there is a comprehensive bibliography for further reading, a question and answer section, and an accompanying Web site developed by the authors with additional data and exercises. - Introduces a structured modeling methodology emphasizing the importance of the modeling goal and including key steps such as model verification, calibration, and validation - Focuses on novel and advanced modeling techniques such as discrete, hybrid, hierarchical, and empirical modeling - Illustrates the notions, tools, and techniques of process modeling with examples and advances applications
Modeling Engineering Systems goes right to the heart of engineering, teaching you how to: understand and use the three basic types of engineering building blocks recognize the analogies that can be drawn between the fundamental elements of electrical, mechanical, fluid, and thermal systems develop math models for first- and higher-order systems using four fundamental methods analyze the models you develop perform frequency analysis and plot frequency responses Educated at the U.S. Coast Guard Academy and MIT, Jack W. Lewis is a registered professional engineer, his specialty is the design of automatic control and instrumental systems, especially as related to the marine industry. He is the author of numerous technical papers and articles, including national award-winning papers for the American Society of Naval Engineers (ASNE) and the Society of Naval Architects and Marine Engineers (SNAME). Lewis is a member of SNAME, ASNE, and the American Society of Mechanical Engineers (ASME). -understand and use the three basic types of engineering building blocks -recognize the analogies that can be drawn between the fundamental elements of electrical, mechanical, fluid, and thermal systems -develop math models for first- and higher-order systems using four fundamental methods
The author presents current work in bond graph methodology by providing a compilation of contributions from experts across the world that covers theoretical topics, applications in various areas as well as software for bond graph modeling. It addresses readers in academia and in industry concerned with the analysis of multidisciplinary engineering systems or control system design who are interested to see how latest developments in bond graph methodology with regard to theory and applications can serve their needs in their engineering fields. This presentation of advanced work in bond graph modeling presents the leading edge of research in this field. It is hoped that it stimulates new ideas with regard to further progress in theory and in applications.
Control Applications for Biomedical Engineering Systems presents different control engineering and modeling applications in the biomedical field. It is intended for senior undergraduate or graduate students in both control engineering and biomedical engineering programs. For control engineering students, it presents the application of various techniques already learned in theoretical lectures in the biomedical arena. For biomedical engineering students, it presents solutions to various problems in the field using methods commonly used by control engineers. - Points out theoretical and practical issues to biomedical control systems - Brings together solutions developed under different settings with specific attention to the validation of these tools in biomedical settings using real-life datasets and experiments - Presents significant case studies on devices and applications
