- brings together all basic techniques for modeling of dynamic systems — with even stress on physical modeling and system identification.
- describes how to create physical models and — in cases where these physical models contain unknown parameters — how to estimate these from data from actual experiments of a system.
- explains how to form models from basic physical equations. Shows in a specific example techniques that can be applied to a multitude of situations.
- shows how to use data to develop nonlinear models in a simple fashion.
- considers how to identify systems in a closed loop. Describes the problems that arise when data are collected under output feedback and describes how to deal with such a situation successfully.
- Copyright 1994
- Dimensions: 7 x 9 1/4
- Pages: 368
- Edition: 1st
- ISBN-10: 0-13-597097-0
- ISBN-13: 978-0-13-597097-3
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.
Table of Contents
I. MODELS. 1. Systems and Models. 2. Examples of Models. 3. Models for Systems and Signals.
II. PHYSICAL MODELLING. 4. Basic Principles for Physical Modelling. 5. Some Basic Physical Analogies. 6. Bond-graphs. 7. Computer Support for Physical Modelling.
III. SYSTEM IDENTIFICATION. 8. Estimation of Transient Response, Spectra and Frequency Functions. 9. Parameter Estimation of Dynamical Models. 10. System Identification as Tool for Modeling.
IV. SIMULATION AND MODEL APPLICATIONS. 11. Simulation. 12. Simulators. 13. Model Validation and Model Use. Appendix A: Linear Systems — Description and Properties. Appendix B: Linearization. Appendix C: Signal Spectra.