Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser.
1992
In this paper a hierarchical control structure using a fuzzy system for coordination of the control actions is studied. The architecture involves two levels of control: a coordination level and an execution level. Numerical experiments will be utilized to illustrate the behavior of the controller when it is applied to a nonlinear plant. (Author)
International Standard Book Number-10: 0-8493-3747-X (Hardcover) International Standard Book Number-13: 978-0-8493-3747-5 (Hardcover) Library of Congress Card Number 2005054270
2014
The paper deals with the development of a simple fuzzy controller structure. Compared to the common structure it does not require information on the derivation of the controlled system output variable because obtaining of information on derivation is often difficult or too costly. A possible fuzzy regulator structure only requires information about the output variable and its integrals for its operation. The properties of the proposed controller have been verified on two types of fuzzy controllers: Mandami fuzzy controller and Sugeno fuzzy controller. The presented controller shows dynamic properties suitable for all the fundamental electric drive control requirements. In view of speed control the presented controller has a PI character but from point of view of position control the presented controller has a PD charakter. Its properties are verified on the basic operational states of a drive with a separately excited DC motor by simulation with the MATLAB programming package. The m...
NASA. Johnson Space Center, Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic,, 1993
Fuzzy control has been recognized as an alternative to conventional control techniques in situations where the plant model is not sufficiently well known to warrant the application of conventional control techniques. Precisely what fuzzy control does and how it does what it does is not quite clear, however. This important issue is discussed and in particular it is shown how a given fuzzy control scheme can resolve into a nonlinear control law and that in those situations the success of fuzzy control hinges on its ability to compensate for ...
Overall intelligent control system which runs on fuzzy, genetic and neural algorithm is a promising engine for large –scale development of control systems . Its development relies on creating environments where anthropomorphic tasks can be performed autonomously or proactively with a human operator. Certainly, the ability to control processes with a degree of autonomy is depended on the quality of an intelligent control system envisioned. In this paper, a summary of published techniques for intelligent fuzzy control system is presented to enable a design engineer choose architecture for his particular purpose. Published concepts are grouped according to their functionality. Their respective performances are compared. The various fuzzy techniques are analyzed in terms of their complexity, efficiency, flexibility, start-up behavior and utilization of the controller with reference to an optimum control system condition.
Proceedings of Thirtieth Southeastern Symposium on System Theory, 1998
To apply the newly developed algorithm, uncertain nonlinear dynami -
IEEE Transactions on Systems, Man, and Cybernetics, 1995
In this study, a fuzzy logic controller is developed using a new methodology for designing its rule-base. This controller consists of two rule-base blocks and a logical switch in between. The rule-base blocks admit two inputs one of which is newly devised and called "normalized acceleration" and the other one is the classical "error". The newly devised input gives a relative value about the "fastness" or "slowness" of the system response. The robustness and effectiveness of the new fuzzy logic controller over the typical MacVicar-Whelan controller has also been illustrated by simulations done on a system under various disturbances and time delays.
2003
The paper presents structures and a systematical development method for two degree of freedom fuzzy controllers with non-homogenous dynamics with respect to the two input channels. The proposed controller structures are meant for a low order plant, which is specific to the field of electrical drives. The design relations result because fuzzy controllers can be, in some certain conditions, well approximated by linear controllers and many development methods are applicable for this situation. The analysis points out that the proposed two degree of freedom fuzzy controllers can ensure better control system performance with respect to the reference input in comparison with other structures containing conventional controllers.
Fuzzy Sets and Systems, 1992
This paper brings back some ideas related with the linear analysis of fuzzy controllers. A fuzzy control algorithm does not go further than a non-linear function described by its inference map. Firstly, it is possible to obtain the inference map corresponding to a classical PID controller by choosing adequately the linguistic terms, the membership functions and the table of rules. Then the inverse problem is presented: to obtain the closer PID to a given fuzzy controller. From these ideas, the paper focuses its attention on the obtainment of a useful tool to design fuzzy controllers at least as good as the PID that allows the system to follow a specified behaviour. The next step is to improve the fuzzy controller parameters. Finally, an industrial application of these ideas is shown. The design of a fuzzy controller over a clinker cooler with grill, improving P and PI controllers already functioning, is discussed.
Fuzzy Sets and Systems, 1997
A study of the different roles played by the fuzzy operators in fuzzy control is developed in this paper. The behavior of a very large amount of fuzzy operators is analyzed and a comparison of the accuracy of many fuzzy logic controllers designed by means of these operators is carried out. In order to do that, a comparison methodology is defined and two fuzzy control applications are selected, the Inverted Pendulum problem and the fuzzy modeling of the real curve Y = X.
Information Sciences, 1985
This paper reviews the studies on fuzzy control by referring to most of the papers ever written on fuzzy control. As an introduction, the paper picks up key points in applying fuzzy control and shows very recent results in industrial applications. The paper also points out some interesting and important problems to be solved.
Fuzzy Sets and Systems, 1996
It is known that fuzzy control is a universal control tool, because an arbitrary control strategy (in particular, a control strategy that is in some sense optimal) can be obtained in principle by applying a fuzzy control methodology to some set of rules. This result has already been proved (e.g., 2,7,20,21]) for the case when a plant is described by nitely many parameters and a special type of fuzzy control methodology is used. In this paper, we prove it for arbitrary plants (including plants that are distributed systems, i.e., plants whose state requires in nitely many parameters to describe) and arbitrary fuzzy control methodologies. We also prove that there exists a universal fuzzy controller that generates an appropriate control from an input description of a plant (and the desired objective). Mathematically, we prove that fuzzy systems can approximate arbitrary continuous functionals, thus generalizing a known result about continuous functions.
Automatica, 1977
Fuzzy sets allow linguistic and inexact data to he manilntlated as a usefid tool in di[lTcult industrial process control situations as imticated l}'om a reciew of seceral practical applications together with some theoretical resuhs.
Design of a fuzzy controller requires more design decisions than usual, for example regarding rule base, inference engine, defuzzification, and data pre-and post processing. This tutorial paper identifies and describes the design choices related to single-loop fuzzy control, based on an international standard which is underway. The paper contains also a design approach, which uses a PID controller as a starting point. A design engineer can view the paper as an introduction to fuzzy controller design.
New Approaches in Automation and Robotics, 2008
Feedback, 2007
Abstract: This paper proposes the design of a cascade control using fuzzy logic. A new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The proposed controller is tested in the ...
2009
Fuzzy PI controllers are using in the place of linear PI controller with success in process control, assuring better control quality criteria. The paper presents some methods, with a higher grade of generality, to design PI fuzzy controllers, to assure internal and external stability to control systems and better control quality criteria. Families of input-output transfer characteristics and gain characteristics of fuzzy blocks are used in the design procedures. An equivalence of PI fuzzy controller with linear PID controllers is done in a first approximation. Then, the fuzzy controller is design to assure absolute global internal stability and external BIBO stability of control systems. An application is presented with modeling and simulations. Results of simulation demonstrate the advantages of these design methods, assuring better quality control criteria and robustness at error in parameter estimation and at disturbance influence for the control systems.
X Congreso Español sobre Tecnologías y Lógica Fuzzy ESTYLF'00, 2000
In this paper, an analysis of the role of fuzzy logic controllers is carried out. Its interpretation and the conditions for successful implementation in several control structures, jointly with their advantages and drawbacks with relation to other advanced control approaches are discussed.
1999
Abstract Traditional (non-fuzzy) control methodology deals with situations when we know exactly how the system behaves and how it will react to different controls, and we want to choose an appropriate control strategy. This methodology enables us to transform the description of the plant's (system's) behavior into an appropriate control strategy. In many practical situations, we do not have the exact knowledge of the system's behavior, but we have expert-supplied fuzzy rules which describe this behavior.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.