Identification of dynamical systems: an introduction with applications

For many applications, ranging from controls engineering to natural sciences and economics, precise dynamic models must be derived. In the vast majority ofapplications, such precise models cannot be derived by theoretical considerations only. The book discusses methods, which allow the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification respectively. 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 continuous-time signals employing methods such as Fourier transform, measurement of the frequency response and correlation analysis. Then, the parameter estimation for parametric models is presented with a focus on the method of Least Squares, followed by some of its most prominent modifications. Issues such as parameter estimation for time-variant processes, parameter estimation in closed-loop, parameter estimation for differential equations, continuous time processes and efficient implementations of the algorithms are discussed. The different methods are compared and an outlook is given on non-linear system identification methods, such as neural networks and look-up tables. Real experimental data are given on CD-ROM, allowing to test many of the methods presented inthe book with real measurements. Powerpoint slides for a 12-14 week graduate level course can be made available to teachers. The book presents different system identification methods, compares the different methods and discusses their application issues Real experimental data are given on CD-ROM, allowing to test many of the methods presented in the book with real measurements Powerpoint slides for a 12-14 week graduate level course can be made available to teachers INDICE: Introduction.- Mathematical Models of Linear Dynamic Systems and Stochastics Signals.- I.) Identification with Non-Parametric Models - Continuous Time Signals.- II.) Identification with Non-Parametric Models - Discrete Time Signals.- III.) Identification with Parametric Models - Discrete Time Signals.- IV.) Identification with Parametric Models - Continuous Time Signals.- V.)Identification of Multi-Variable Systems.- VI.) Identification of Non-Linear Systems.- VII.) Appendix.

• ISBN: 978-3-540-78878-2
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 550
• Fecha Publicación: 01/12/2010
• Nº Volúmenes: 1
• Idioma: Inglés