| Distributed Fuzzy Control of Multivariable Systems 1996 Edition Contributor(s): Gegov, Alexander (Author) |
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ISBN: 0792338510 ISBN-13: 9780792338512 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: January 1996 Annotation: This research monograph presents the fundamentals of fuzzy control theory and recent results in extending fuzzy control theory for multivariable and large scale systems.Among the topics presented are: the problem of dimensional reduction of fuzzy relations; the possibilities for decomposing multivariable systems for the purpose of distributed fuzzy control; a method of two-level hierarchical fuzzy control, based on local and global control components; methods of decentralized fuzzy control by different types of spatial decomposition of the original system into subsystems, and, analogously, methods of multilayer fuzzy control on the basis of several types of temporal decomposition of the original system into layers.The theoretical results are illustrated by numerical examples and two case studies. It is shown that the number of fuzzy relations, respectively on-line computations, can be significantly reduced and in this way real time control implementation can be facilitated.The book is written using a structured method-algorithm-example format, and offers solutions to existing problems, as well as pointing a way to further investigations.Audience: This volume will be of interest to graduate students and researchers in the field of applied mathematics, industrial engineering and informatics. |
| Additional Information |
| BISAC Categories: - Technology & Engineering | Robotics - Mathematics | Logic - Mathematics | Applied |
| Dewey: 629.831 |
| LCCN: 95047422 |
| Series: International Intelligent Technologies |
| Physical Information: 0.69" H x 6.94" W x 9.08" (0.96 lbs) 186 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: It is known that many control processes are characterized by both quantitative and qualitative complexity. Tbe quantitative complexity is usually expressed in a large number of state variables, respectively high dimensional mathematical model. Tbe qualitative complexity is usually associated with uncertain behaviour, respectively approximately known mathematical model. If the above two aspects of complexity are considered separately, the corresponding control problem can be easily solved. On one hand, large scale systems theory has existed for more than 20 years and has proved its capabilities in solving high dimensional control problems on the basis of decomposition, hierarchy, decentralization and multilayers. On the other hand, the fuzzy linguistic approach is almost at the same age and has shown its advantages in solving approximately formulated control problems on the basis of linguistic reasoning and logical inference. However, if both aspects of complexity are considered together, the corresponding control problem becomes non-trivial and does not have an easy solution. Modem control theory and practice have reacted accordingly to the above mentioned new cballenges of tbe day by utilizing the latest achievements in computer technology and artificial intelligence distributed computation and intelligent operation. In this respect, a new field has emerged in the last decade, called " Distributed intelligent control systems" . However, the majority of the familiar works in this field are still either on an empirical or on a conceptual level and this is a significant drawback. |