Description
This state of the art reference/text presents the most recent techniques in advanced process modeling, identification, prediction, and parameter estimation for the implementation and analysis of industrial systems providing current applications for the identification of linear and nonlinear systems, the design of generalized predictive controllers (GPCs), and the control of multivariable systems.
Exploring fixed parameter and adaptive strategies as well as unconstrained and constrained identification and control of processes, Advanced Process Identification and Control discusses the design of power series, neural networks, and fuzzy systems the Wiener and Hammerstein systems the design of a multivariable GPC based on state representation selection of the most efficient input output pairing for the design of effective distributed controllers.
Table of Content
I Identification
Introduction to Identification
Linear Regression
Linear Dynamic Systems
Non linear Systems
Non linear Dynamic Structures
Estimation of Parameters
II Control
Predictive Control
Multivariable Systems
Time-Varying and Non linear Systems
III Appendices
State-Space Representation
Fluidized Bed Combustion
Bibliography
Index
About The Author
Enso Ikonen is a Senior Assistant at the Systems Engineering Laboratory, Department of Process and Environmental Engineering, University of Oulu, Finland, and an Academy Research Fellow of the Academy of Finland, Helsinki. The author or coauthor of numerous journal articles, book chapters, and books, he received the M.Sc. degree (1991) in process engineering, and the Licentiate (1994) and Ph.D., (1997) degrees in technology from the University of Oulu, Finland.
New Product Tab
Here's your new product tab.
Reviews
There are no reviews yet.