Dynamical Systems Modelling

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.

The purpose of this book is to expose undergraduate students to the use of applied mathematics and physical argument as a basis for developing an understanding of the response characteristics, from a systems viewpoint, of a broad class of dynamic physical processes. This book was developed for use in the course ECE 355, Dynamic Systems and Modeling, in the Department of Electrical and Computer Engineering at the University of Michigan, Ann Arbor. The course ECE 355 has been elected primarily by junior and senior level students in computer engineering or in electrical engineering. Occasionally a student from outside these two programs elected the course. Thus the book is written with this class of students in mind. It is assumed that the reader has previous background in mathematics through calculus, differential equations, and Laplace transforms, in elementary physics, and in elemen tary mechanics and circuits. Although these prerequisites indicate the orientation of the material, the book should be accessible and of interest to students with a much wider spectrum of experience in applied mathemati cal topics. The subject matter of the book can be considered to form an introduc tion to the theory of mathematical systems presented from a modern, as opposed to a classical, point of view. A number of physical processes are examined where the underlying systems concepts can be clearly seen and grasped. The organization of the book around case study examples has evolved as a consequence of student suggestions.

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.

These authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems. First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems is presented. It is illustrated with examples on the PROLOG programming la

This book gives an in-depth introduction to the areas of modeling, identification, simulation, and optimization. These scientific topics play an increasingly dominant part in many engineering areas such as electrotechnology, mechanical engineering, aerospace, and physics. This book represents a unique and concise treatment of the mutual interactions among these topics. Techniques for solving general nonlinear optimization problems as they arise in identification and many synthesis and design methods are detailed. The main points in deriving mathematical models via prior knowledge concerning the physics describing a system are emphasized. Several chapters discuss the identification of black-box models. Simulation is introduced as a numerical tool for calculating time responses of almost any mathematical model. The last chapter covers optimization, a generally applicable tool for formulating and solving many engineering problems.

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.

"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"--

Written by a recognized authority in the field of identification and control, this book draws together into a single volume the important aspects of system identification AND physical modelling. KEY TOPICS: Explores techniques used to construct mathematical models of systems based on knowledge from physics, chemistry, biology, etc. (e.g., techniques with so called bond-graphs, as well those which use computer algebra for the modeling work). Explains system identification techniques used to infer knowledge about the behavior of dynamic systems based on observations of the various input and output signals that are available for measurement. Shows how both types of techniques need to be applied in any given practical modeling situation. Considers applications, primarily simulation. MARKET: For practicing engineers who are faced with problems of modeling.