Identification of Dynamical Systems : An Introduction with Applications (Advanced Textbooks in Control and Signal Processing)

Isermann, R./ Muenchhof, M.

Springer（2010/07発売）

• 製本 Hardcover:ハードカバー版／ページ数 550 p.
• 言語 ENG
• 商品コード 9783540788782

基本説明

After a short introduction into the required methodology of continuous-time and discrete-time linear systems, the focus is first on the identification of non-parametric models with continuoustime signals employing methods such as Fourier transform, measurement of the frequency response and correlation analysis.

Full Description

Precise dynamic models of processes are required for many applications, ranging from control engineering to the natural sciences and economics. Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. 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. Among others, 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.

Contents

Introduction .- 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 with Non-Parametric Models - Continuous and Discrete Time

Part III: Identification with Parametric Models - Discrete Time Signals

Part IV: Identification with Parametric Models - Continuous Time Signals

PartV: Identification of Multi-Variable Systems

Part VI: Identification of Non-Linear Systems

Part VII: Miscellaneous Issues

Part VIII Applications

Part IX Appendix.