Synopses & Reviews
This book presents new approaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effective use of heterogeneous information in the form of numerical data, qualitative knowledge, and first principle models. The main methods and techniques are illustrated through several simulated examples and real-world applications from chemical and process engineering practice. Key features: * detailed review of algorithms and approaches developed for modeling and identification for control * numerous illustrations to facilitate the understanding of ideas and methods presented *extensive references give a good overview of the current state of identification and control of dynamic systems and fuzzy modeling, and suggest further reading for additional research * supporting MATLAB and Simulink files, available at the website www.fmt.vein.hu/softcomp, create a computational platform for exploration and illustration of many concepts and algorithms presented in the book. The book is aimed primarily at researchers, practitioners, and professionals in process control and identification, but it is also accessible to graduate students in electrical, chemical, and process engineering. Technical prerequisites include an undergraduate-level knowledge of control theory and linear algebra. Additional familiarity with fuzzy systems is helpful but not required.
Synopsis
Overview Since the early 1990s, fuzzy modeling and identification from process data have been and continue to be an evolving subject of interest. Although the application of fuzzy models proved to be effective for the approxima- tion of uncertain nonlinear processes, the data-driven identification offuzzy models alone sometimes yields complex and unrealistic models. Typically, this is due to the over-parameterization of the model and insufficient in- formation content of the identification data set. These difficulties stem from a lack of initial a priori knowledge or information about the system to be modeled. To solve the problem of limited knowledge, in the area of modeling and identification, there is a tendency to blend information of different natures to employ as much knowledge for model building as possible. Hence, the incorporation of different types of a priori knowledge into the data-driven fuzzy model generation is a challenging and important task. Motivated by our research into this topic, our book presents new ap- proaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effec- tive use of heterogenous information in the form of numerical data, qualita- tive knowledge and first-principle models. By exploiting the mathematical properties of the proposed model structures, such as invertibility and local linearity, new control algorithms will be presented.
Synopsis
This book presents new approaches to constructing fuzzy models for model-based control. Simulated examples and real-world applications from chemical and process engineering illustrate the main methods and techniques. Supporting MATLAB and Simulink files create a computational platform for exploration of the concepts and algorithms.
Table of Contents
Preface Introduction Fuzzy Model Structures and their Analysis Fuzzy Models of Dynamical Systems Fuzzy Model Identification Fuzzy Model Based Control Process Models Used for Case Studies References Index