"Identification of Dynamic Systems: An Introduction with Applications" by Rolf Isermann, Marco Münchhof (Repost)

This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification. Both offline and online methods are presented, i.e. methods that post-process the measured data as well as methods that provide models during the measurement.

The book is theory-oriented and application-oriented and most methods covered have been used successfully in practical applications for many different processes.
Illustrative examples in this book with real measured data range from hydraulic and electric actuators up to combustion engines.
Real experimental data is also provided on the Springer webpage, allowing readers to gather their first experience with the methods presented in this book.
The book covers the following subjects: determination of the non-parametric frequency response, (fast) Fourier transform, correlation analysis, parameter estimation with a focus on the method of Least Squares and modifications, identification of time-variant processes, identification in closed-loop, identification of continuous time processes, and subspace methods. Some methods for nonlinear system identification are also considered, such as the Extended Kalman filter and neural networks.
The different methods are compared by using a real three-mass oscillator process, a model of a drive train. For many identification methods, hints for the practical implementation and application are provided.
The book is intended to meet the needs of students and practicing engineers working in research and development, design and manufacturing.


Brief Contents
Preface
List of Symbols
1 Introduction
2 Mathematical Models of Linear Dynamic Systems and Stochastic Signals
Part I - Identification of Non-Parametric Models in the Frequency Domain — Continuous Time Signals
Part II - Identification of Non-Parametric Models with Correlation Analysis — Continuous and Discrete Time
Part III - Identification with Parametric Models — Discrete Time Signals
Part IV - Identification with Parametric Models — Continuous Time Signals
Part V - Identification of Multi-Variable Systems
Part VI - Identification of Non-Linear Systems
Part VII - Miscellaneous Issues
Part VIII - Applications
Part IX - Appendix
Index