5 edition of Distributed fuzzy control of multivariable systems found in the catalog.
Includes bibliographical references (p. -181) and index.
|Statement||by Alexander Gegov.|
|Series||International series in intelligent technologies ;, 6|
|LC Classifications||TJ213 .G379 1996|
|The Physical Object|
|Pagination||xiii, 185 p. :|
|Number of Pages||185|
|LC Control Number||95047422|
Zhang X, Zhao L, Li J, Cao G and Wang B () Space-decomposition based 3D fuzzy control design for nonlinear spatially distributed systems with multiple control sources using multiple single-output SVR learning, Applied Soft Computing, C, (), Online publication date: 1-Oct Distributed Fuzzy Control of Multivariable Systems. The book is written using a structured method-algorithm-example format, and offers solutions to existing problems, as well as pointing a way.
In control theory, Advanced process control (APC) refers to a broad range of techniques and technologies implemented within industrial process control systems. Advanced process controls are usually deployed optionally and in addition to basic process controls. Basic process controls are designed and built with the process itself, to facilitate basic operation, control and automation requirements. Fuzzy State Space Model of Multivariable Control Systems Article (PDF Available) in Computer and Information Science 2(2) April with Reads How we measure 'reads'.
Multi-freedom prosthetic hand is multivariable system which has many inputs and outputs. The design of fuzzy logic controller is very complex because so many fuzzy control systems have to be designed based on conventional idea. Thus, a multivariable fuzzy control is proposed. An effective strategy is that all variables share a common membership function. Yu, H.j., Wei, C.: Decoupling for multivariable fuzzy Control System. Tianjin University Journal 37(5), () Fuzzy neural network control of multivariable system.
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Distributed Fuzzy Control of Multivariable Systems (International Series in Intelligent Technologies) th Edition by Alexander Gegov (Author)Cited by: 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. Distributed Fuzzy Control of Multivariable Systems. Authors (view affiliations) k Downloads; Part of the International Series in Intelligent Technologies book series (ISIT, volume 6) Log in to check access.
Buy eBook. USD In this respect, a new field has emerged in the last decade, called " Distributed intelligent control. 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 Distributed fuzzy control of multivariable systems book of the original system into subsystems, and, analogously, methods of multilayer fuzzy control.
Decomposition of Multivariable Systems for Distributed Fuzzy Control. Hierarchical Fuzzy Control of Multivariable Systems. Decentralized Fuzzy Control of Multivariable Systems by Passive Decomposition. Decentralized Fuzzy Control of Multivariable Systems by Active Decomposition.
A review of: “ DISTRIBUTED FUZZY CONTROL OF MULTIVARIABLE SYSTEMS ”, by Alexander Gegov. Kluwer, Boston, xiii + pages; ISBN International Journal of General Systems Vol - Issue 4. This research monograph presents some recent results in distributed fuzzy control of multivariable systems.
The results are obtained within a systematic investigation, extending over the empirical and conceptual level of most familiar works. Large scale systems theory and. Abstract: The paper describes briefly some recent results in distributed fuzzy control of multivariable processes.
DefInitions and theorems with regard to this type of control are formulated. Methods of decentralized and multilayer fuzzy control are presented.
Numerical examples are shown for illustration of the theoretical results. Gegov A. () Decentralized Fuzzy Control of Multivariable Systems by Active Decomposition. In: Distributed Fuzzy Control of Multivariable Systems.
International Series. Gegov A. () Multilayer Fuzzy Control of Multivariable Systems By Passive Decomposition. In: Distributed Fuzzy Control of Multivariable Systems. International Series. Abstract A multi-variable fuzzy logic controller (FLC) is proposed to control a class of distributed parameter systems (DPSs).
When a DPS is transformed into finite-dimensional ordinary differential equations (ODEs) by using time/space separation, each ODE can be considered as a subsystem. Abstract The paper considers the problem of decomposition of multivariable systems for the purpose of distributed fuzzy control.
Some terms and definitions with regard to this problem are given. A decomposition method, decreasing the strength and the number of interactional fuzzy relations among subsystems, is proposed.
The control law () corresponds to a centralized consideration of the multivariable system () and it is evident that the whole number of fuzzy relations in this case will be n.m.
However, this may be quite a great number and therefore time constraints in on-line control computations may be violated. In this work, a novel method for decomposing an m-input/n-output self-organizing fuzzy logic control (SOFLC) structure to many 2-input/1-output sets has been designed for controlling general anesthesia and muscle relaxation for the operating theatre.
Distributed fuzzy control of multivariable systems. By Alexander Emilov Gegov. Get PDF ( KB) Cite. BibTex; Full citation fuzzy control, multivariable systems. Chapter 1, “Introduction to Expert Systems,” introduces the conceptual framework, based on a division of the main tasks of fuzzy and neural control systems: structure, computing, and learning.
These are a recasting of the traditional cognitive tasks defined by symbolic artificial intelligence: knowledge representation, reasoning, and.
Publisher Summary. This chapter proposes a new approach to fuzzy adaptive controller design using only system input–output data. The design procedure consists of three steps: First, a fuzzy ARMAX model is identified using the available data; then, a fuzzy controller is derived based on a combination of sliding mode control (SMC) theory and fuzzy control methodology; finally, the controller.
Based on Takagi-Sugeno fuzzy model, employing parallel distributed compensation, the multivariable integral control and state observer in the linear system theory are applied to nonlinear systems for the problem of output tracking and rejecting disturbance with reference and/or disturbance signals in the form of step, and the fuzzy integral controller and fuzzy state observer are synthesized.
The traditional fuzzy logic controller (FLC) developed from this 2-D fuzzy set should not be able to control the distributed parameter system that has the tempo-spatial nature.
A Control Engineering Approach to Fuzzy Control r 1 Outline of This Book r 2 2 TAKAGI-SUGENO FUZZY MODEL AND PARALLEL DISTRIBUTED COMPENSATION 5 Takagi-Sugeno Fuzzy Model r 6 Construction of Fuzzy Model r 9 Sector Nonlinearity r 10 Local Approximation in Fuzzy Partition Spaces r 23 Parallel Distributed.
Reviewer: Heinrich W. Guggenheimer Fuzzy systems work with sets and membership functions. In this book, which is an elaboration of the author's thesis, the only membership function considered in practical applications is an average of Gaussian functions e exp x- x 2/ s 2.
In this paper, a combined multivariable cascade advanced fuzzy control system has been developed for two flow industrial petroleum refinery furnaces. The following sections present the proposed algorithm in detail.
The simulation results are then given. Finally, the conclusion is drawn.The plant (container crane) is a nonlinear complex system that, in respect of its fuzzy control, is formed like a multivariable system with 2 input variables and 5 output variables.