For the first time in monograph form, the reader can learn the use of fuzzy models in predictive control
The reader will learn a novel approach to the construction of fuzzy models which compounds the data from real situations with the consideration of linguistic integrity which is increasingly beneficial in dealing with the vagaries of real-life control
For the first time in monograph form, the reader can learn the use of fuzzy models in predictive control
The reader will learn a novel approach to the construction of fuzzy models which compounds the data from real situations with the consideration of linguistic integrity which is increasingly beneficial in dealing with the vagaries of real-life control
Includes supplementary material: sn.pub/extras
Jairo Espinosa had a considerable experience of the practitioner side of advanced control systems and fuzzy systems in particular working with such companies as Zenith Data Systems in his native Colombia. There, he also won prizes for his academic work and for electronic design. He now works for IPCOS a company specialising in the design of advanced control systems for many process industries. This wil allow the author to draw on a good selection of industrial situations in writing the book.
Vincent Wertz is now head of the Automatic Control Group at Louvain where he is particularly active in Ph.D. supervision work (his contributions to the book will ensure relevance to the graduate market) and the book reflects all of his main research interests.
Modern industrial processes and systems require adaptable advanced control protocols able to deal with circumstances demanding "judgement” rather than simple "yes/no”, "on/off” responses: circumstances where a linguistic deillegalscription is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious for this purpose.
Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real industrial systems and simulations. The second part exploits such models to design control systems employing techniques like data mining.
This monograph presents a combination of fuzzy control theory and industrial serviceability that will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.
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The complexity and sensitivity of modern industrial processes and systems increasingly require adaptable advanced control protocols. These controllers have to be able to deal with circumstances demanding "judgement" rather than simple "yes/no", "on/off" responses, circumstances where an imprecise linguistic deillegalscription is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious in this form of expert control system.
Divided into two parts, Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real-world industrial systems and simulations. The second part demonstrates the exploitation of such models to design control systems employing techniques like data mining.
Fuzzy Logic, Identification and Predictive Control is a comprehensive introduction to the use of fuzzy methods in many different control paradigms encompassing robust, model-based, PID-like and predictive control. This combination of fuzzy control theory and industrial serviceability will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.
Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
|Demand for this book will come from research engineers looking to find ways to deal with systems (especially non-linear systems) which have been difficult to deal with in traditional control and from industrial control engineers looking to take advantage of the low-solution costs associated with the implementation of fuzzy methods in improving existing control protocols.
Part I: Fuzzy Modelling Fuzzy Modeling Constructing Fuzzy Models from Input-Output Data Fuzzy Modelling with Linguistic Integrity: A Tool for Data Mining Nonlinear Identification Using Fuzzy Models Part II: Fuzzy Control Fuzzy Control Predictive Control Based on Fuzzy Models Robust Nonlinear Predictive Control Using Fuzzy Models Conclusions and Future Perspectives Part III: Appendices A. Fuzzy Set Theory B. Clustering Methods C. Gradients Used in Identification with Fuzzy Models D. Discrete Linear Dynamic Approximation Theorem E. Fuzzy Control for a CVT
From the reviews:
"New insights into the transfer of fuzzy methods into the modern control paradigms encompassing robust, model-based, PID-like, and predictive control are presented in this book. ... Five appendices support the already extensive results of the chapters by proofs, explanations and illustrative examples. The book (263 pages, 138 figures, 95 references) is of interest to researchers in the field of data mining, artificial intelligence, modeling, and control. Also, the realistic examples provide good material to graduate students and engineers." (Ingmar Randvee, Zentralblatt MATH, Vol. 1061 (12), 2005)
Modern industrial processes and systems require adaptable advanced control protocols able to deal with circumstances demanding "judgement" rather than simple "yes/no", "on/off" responses: circumstances where a linguistic deillegalscription is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious for this purpose.
Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real industrial systems and simulations. The second part exploits such models to design control systems employing techniques like data mining.
This monograph presents a combination of fuzzy control theory and industrial serviceability that will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.
Fuzzy Modeling.- Fuzzy Modeling.- Constructing Fuzzy Models from Input-Output Data.- Fuzzy Modeling with Linguistic Integrity: A Tool for Data Mining.- Nonlinear Identification Using Fuzzy Models.- Fuzzy Control.- Fuzzy Control.- Predictive Control Based on Fuzzy Models.- Robust Nonlinear Predictive Control Using Fuzzy Models.- Conclusions and Future Perspectives.
From the reviews:
"New insights into the transfer of fuzzy methods into the modern control paradigms encompassing robust, model-based, PID-like, and predictive control are presented in this book. ... Five appendices support the already extensive results of the chapters by proofs, explanations and illustrative examples. The book (263 pages, 138 figures, 95 references) is of interest to researchers in the field of data mining, artificial intelligence, modeling, and control. Also, the realistic examples provide good material to graduate students and engineers." (Ingmar Randvee, Zentralblatt MATH, Vol. 1061 (12), 2005)
Jairo Espinosa had a considerable experience of the practitioner side of advanced control systems and fuzzy systems in particular working with such companies as Zenith Data Systems in his native Colombia. There, he also won prizes for his academic work and for electronic design. He now works for IPCOS a company specialising in the design of advanced control systems for many process industries. This wil allow the author to draw on a good selection of industrial situations in writing the book.
Vincent Wertz is now head of the Automatic Control Group at Louvain where he is particularly active in Ph.D. supervision work (his contributions to the book will ensure relevance to the graduate market) and the book reflects all of his main research interests.
Über den Autor
Jairo Espinosa had a considerable experience of the practitioner side of advanced control systems and fuzzy systems in particular working with such companies as Zenith Data Systems in his native Colombia. There, he also won prizes for his academic work and for electronic design. He now works for IPCOS a company specialising in the design of advanced control systems for many process industries. This wil allow the author to draw on a good selection of industrial situations in writing the book.
Vincent Wertz is now head of the Automatic Control Group at Louvain where he is particularly active in Ph.D. supervision work (his contributions to the book will ensure relevance to the graduate market) and the book reflects all of his main research interests.
Inhaltsverzeichnis
Part I: Fuzzy Modelling
Fuzzy Modeling
Constructing Fuzzy Models from Input-Output Data
Fuzzy Modelling with Linguistic Integrity: A Tool for Data Mining
Nonlinear Identification Using Fuzzy Models
Part II: Fuzzy Control
Fuzzy Control
Predictive Control Based on Fuzzy Models
Robust Nonlinear Predictive Control Using Fuzzy Models
Conclusions and Future Perspectives
Part III: Appendices
A. Fuzzy Set Theory
B. Clustering Methods
C. Gradients Used in Identification with Fuzzy Models
D. Discrete Linear Dynamic Approximation Theorem
E. Fuzzy Control for a CVT
Klappentext
Modern industrial processes and systems require adaptable advanced control protocols able to deal with circumstances demanding "judgement" rather than simple "yes/no", "on/off" responses: circumstances where a linguistic deillegalscription is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious for this purpose. rnrnFuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real industrial systems and simulations. The second part exploits such models to design control systems employing techniques like data mining. rnrnThis monograph presents a combination of fuzzy control theory and industrial serviceability that will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.
Demand for this book will come from research engineers looking to find ways to deal with systems (especially non-linear systems) which have been difficult to deal with in traditional control and from industrial control engineers looking to take advantage of the low-solution costs associated with the implementation of fuzzy methods in improving existing control protocols.
