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Showing papers on "Fuzzy control system published in 1990"


Journal Article
TL;DR: The fuzzy logic controller (FLC) based on fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy.
Abstract: During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for research in the applications of fuzzy set theory. Fuzzy control is based on fuzzy logic. The fuzzy logic controller (FLC) based on fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy. A survey of the FLC is presented; a general methodology for constructing an FLC and assessing its performance is described; and problems that need further research are pointed out

4,830 citations


Journal ArticleDOI
TL;DR: It is proved theoretically that such a fuzzy controller, the smallest possible, with two inputs and a nonlinear defuzzification algorithm is equivalent to a nonfuzzy nonlinear proportional-integral (PI) controller with proportional-gain and integral-gain changing with error and rate change of error about a setpoint.

476 citations


Journal ArticleDOI
TL;DR: An approach to intelligent PID (proportional integral derivative) control of industrial systems which is based on the application of fuzzy logic is presented, and it is possible to determine small changes on these values during the system operation, and these lead to improved performance of the transient and steady behavior of the closed-loop system.
Abstract: An approach to intelligent PID (proportional integral derivative) control of industrial systems which is based on the application of fuzzy logic is presented. This approach assumes that one has available nominal controller parameter settings through some classical tuning technique (Ziegler-Nichols, Kalman, etc.). By using an appropriate fuzzy matrix (similar to Macvicar-Whelan matrix), it is possible to determine small changes on these values during the system operation, and these lead to improved performance of the transient and steady behavior of the closed-loop system. This is achieved at the expense of some small extra computational effort, which can be very easily undertaken by a microprocessor. Several experimental results illustrate the improvements achieved. >

271 citations



Proceedings ArticleDOI
05 Dec 1990
TL;DR: It is shown that the direct method of Lyapunov can be used to determine sufficient conditions for global stability of a broad class of fuzzy control schemes and a measure of robustness is proposed that can be use to evaluate and possibly redesign a given fuzzy control system so as to enhance the range of its stable operation.
Abstract: A new approach to the stability analysis of fuzzy linguistic control (FLC) systems is presented. Specifically, it is shown that the direct method of Lyapunov can be used to determine sufficient conditions for global stability of a broad class of fuzzy control schemes. Moreover, a measure of robustness is proposed that can be used to evaluate and possibly redesign a given fuzzy control system so as to enhance the range of its stable operation. Finally, the application of the proposed methodology is shown and its implications in terms of control design are demonstrated by means of numeric examples. >

153 citations


Journal ArticleDOI
TL;DR: Fuzzy logic is examined, and its application to control systems is discussed, and the possibility of interfacing fuzzy logic to existing control Systems is noted.
Abstract: Fuzzy logic is examined, and its application to control systems is discussed. The steps taken to design a fuzzy controller are described, and the possibility of interfacing fuzzy logic to existing control systems is noted. Tools for developing and modeling fuzzy control systems are described. >

147 citations


Book
01 Sep 1990
TL;DR: A practical, hands-on, applications-oriented approach, it develops computer models for applications to decision-making processes, introducing the basic notion of relative grades via the fuzzy set theoretic approach.
Abstract: Until this book, the available literature on fuzzy sets has been, at best, scattered throughout industrial and university libraries. Encapsulated here is a sound discussion of the basic theoretical and practical aspects involved in fuzzy database systems. With a practical, hands-on, applications-oriented approach, it develops computer models for applications to decision-making processes, introducing the basic notion of relative grades via the fuzzy set theoretic approach. Also covers fuzzy relational databases and their calculus, and the fuzzy relational (structured) query language (FSQL). The last sections present methods for treating the incomplete information in fuzzy PROLOG database (FPDB) systems. Several examples of knowledge representation, expert systems, fuzzy control, and fuzzy clustering and information retrieval illustrate the theory. An extended sample database is used throughout the book.

143 citations


Journal ArticleDOI
01 May 1990
TL;DR: A new type of power system stabiliser based on fuzzy set theory is proposed to improve the dynamic performance of a multimachine power system and is of decentralised output feedback form and is easy for practical implementation.
Abstract: A new type of power system stabiliser based on fuzzy set theory is proposed to improve the dynamic performance of a multimachine power system. To have good damping character istic over a wide range of operating conditions, speed deviation (δω) and acceleration (δω) of a machine are chosen as the input signals to the fuzzy stabiliser on that particular machine. These input signals are first characterised by a set of linguistic variables using fuzzy set notations. The fuzzy relation matrix, which gives the relationship between stabiliser inputs and stabiliser output, allows a set of fuzzy logic operations that are per formed on stabiliser inputs to obtain the desired stabiliser output. Since only local measurements are required by the fuzzy stabiliser on each generating unit, the proposed stabiliser is of decentralised output feedback form and is easy for practical implementation. The effectiveness of the proposed fuzzy stabiliser is demonstrated by a multimachine system example.

110 citations


Journal ArticleDOI
TL;DR: The accuracy of these circuits is better than the accuracy of binary tree realizations using two-input max/min circuits because no accumulation of errors occurs; furthermore, the operation speed is higher than the speed of the binary tree realization.
Abstract: Multiple-input maximum and minimum circuits in current mode are proposed. The operation of these circuits is formulated using simultaneous bounded-difference equations. The exact analyses are performed by solving the bounded-difference equations. The accuracy of these circuits is better than the accuracy of binary tree realizations using two-input max/min circuits because no accumulation of errors occurs; furthermore, the operation speed is higher than the speed of the binary tree realization. The proposed circuits consist of only MOS transistors and are compatible with standard MOS fabrication processes. These circuits are useful building blocks for a real-time fuzzy controller and a fuzzy computer. >

107 citations


Proceedings ArticleDOI
17 Jun 1990
TL;DR: It is concluded that fuzzy control shows optimal truck backing-up performance and compares favorably with the neural controller in terms of black-box computation load, smoothness of truck trajectories, and robustness.
Abstract: A simple fuzzy control system and a simple neural control system for backing up a truck in an open parking lot are developed. The choice of control problem was prompted by the recent, successful, neural network truck backer-upper simulation of Nguyen and Widrow (Proc. Int. Joint Conference on Neural Networks, vol.2, p.357-363, June, 1989). The authors were unable to exactly replicate the neural network they used. Instead the authors built the best backpropagation network they could with essentially the same kinematics and compared it to the best fuzzy controller they could develop. The fuzzy controller compares favorably with the neural controller in terms of black-box computation load, smoothness of truck trajectories, and robustness. Robustness of the fuzzy controller is studied by deliberately adding confusing FAM (fuzzy associative memory), rules-sabotage rules-to the system and by randomly removing different subsets of FAM rules. Robustness of the neural controller is studied by randomly removing different portions of the training data. It is concluded that fuzzy control shows optimal truck backing-up performance

99 citations


Journal ArticleDOI
01 Sep 1990
TL;DR: Simulations show that the fuzzy logic controller (FLC) yields better results than the conventional PD controller and a self-paced fuzzy tracking controller (SPFTC) designed for two-dimensional path tracking is presented.
Abstract: A heuristic controller is presented that takes the form of a set of fuzzy linguistic rules. Simulations show that the fuzzy logic controller (FLC) yields better results than the conventional PD controller. A self-paced fuzzy tracking controller (SPFTC) designed for two-dimensional path tracking is also presented. The SPFTC adjusts the tracking speed in accordance with contour conditions such as curvature; the fuzzy-rule-based adjustment of the tracking speed improves performance in terms of tracking precision and travel time. Advantages of the FLC and SPFTC are demonstrated by a simulation study. >

Proceedings Article
26 Nov 1990
TL;DR: This paper proposes ajuzzy neural expert system (FNES) with the following two functions: generalization of the information derived from the training data and embodiment of knowledge in the form of the fuzzy neural network.
Abstract: This paper proposes a fuzzy neural expert system (FNES) with the following two functions: (1) Generalization of the information derived from the training data and embodiment of knowledge in the form of the fuzzy neural network; (2) Extraction of fuzzy If-Then rules with linguistic relative importance of each proposition in an antecedent (If-part) from a trained neural network. This paper also gives a method to extract automatically fuzzy If-Then rules from the trained neural network. To prove the effectiveness and validity of the proposed fuzzy neural expert system, a fuzzy neural expert system for medical diagnosis has been developed.


Proceedings ArticleDOI
01 Feb 1990
TL;DR: High-performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of a spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics.
Abstract: The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. In this paper, high-performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of a spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

Journal ArticleDOI
TL;DR: The authors address the implementation of a fuzzy simulator (FSIM) and discuss architectures for a general-purpose VLSI fuzzy-inference processor and describe an FPS example from conceptualization to implementation on silicon chips.
Abstract: The authors address the implementation of a fuzzy simulator (FSIM) and discuss architectures for a general-purpose VLSI fuzzy-inference processor The FSIM tool aids in the rapid prototyping of fuzzy production system (FPS), and represents a convenient transitional step in the implementation of an FPS on silicon They present a brief theoretical review of fuzzy reasoning, introduce the FSIM, and discuss FPS development using the FSIM An overall picture of the various stages involved in developing a fuzzy-inference processor is provided The authors then outline the general architecture of a VLSI inference processor for FPSs To further illustrate the development of a fuzzy inference processor, they describe an FPS example from conceptualization to implementation on silicon chips >

Journal ArticleDOI
TL;DR: This paper fuzzified Campbell's statement that N street-lighting posts have to be provided continually with lamps such that the system failure is defined in a fuzzy way, and concludes that a complex system may be as reliable as a simple one in view of the system fuzzy reliability.

Journal ArticleDOI
TL;DR: A method of fuzzy control of systems based on a concept of human thinking is investigated, which consists of a knowledge base containing a number of time responses of a process that contains a model of the process to be controlled.
Abstract: A method of fuzzy control of systems based on a concept of human thinking is investigated. The fuzzy controller consists of a knowledge base containing a number of time responses of a process. The elements of this base are cells in which an input and a corresponding output of a system are stored. The cells are organized according to different types of relations. The result is a knowledge structure which contains a model of the process to be controlled. The following phases are distinguished: the learning phase, during which the relation between the imput and the output of a system is learned and the knowledge structure is constructed; and a phase in which the knowledge structure is applied to control the process. The method also provides an indication of the reliability of the control action. >

Journal ArticleDOI
TL;DR: Simulation results are presented for comparing the performance of the controllers proposed, and to illustrate the advantages of such fuzzy PID controllers over that of the conventional PID controller.

Journal ArticleDOI
TL;DR: This paper introduces a method of generating control rules for fuzzy logic controllers, called parametric functions method, which can obtain control rules by adjusting several parameters.

Proceedings ArticleDOI
23 May 1990
TL;DR: A design technique and the stability analysis of fuzzy-logic controllers for a class of nonlinear systems are studied and the design procedure of the phase portrait assignment algorithm is followed by stability analysis for nonlinear fuzzy regulators.
Abstract: A design technique and the stability analysis of fuzzy-logic controllers for a class of nonlinear systems are studied. A new algorithm called `the phase portrait assignment algorithm', is introduced in order to establish a control rulebase that stabilizes a nonlinear feedback system by using the vector fields in the state space. This technique can substitute for classical control methods when the latter suffer from uncertainties since it provides flexibility, robustness, and asymptotic stability. The methodology provides a bridge between qualitative reasoning and stability of nonlinear feedback control systems even if a priori information is relatively unreliable. The design procedure of the phase portrait assignment algorithm is followed by stability analysis for nonlinear fuzzy regulators. As an example, a one degree-of-freedom robot arm is used for demonstrating the performance and the merits of the proposed fuzzy control mechanism.

Proceedings ArticleDOI
03 Dec 1990
TL;DR: A fuzzy neural expert system for medical diagnosis has been developed and a method to extract automatically fuzzy If-Then rules from the trained neural network is given.
Abstract: Proposes a fuzzy neural expert system (FNES) which has a feedforward fuzzy neural network whose input layer consists of fuzzy cell groups and crisp (non-fuzzy) cell groups. Here, the truthfulness of fuzzy information and crisp information of training data is represented by fuzzy cell groups and crisp cell groups, respectively. The expert system has the following two functions: generalization of the information derived from the training data and embodiment of knowledge in the form of the fuzzy neural network; and extraction of fuzzy If-Then rules with linguistic relative importance of each proposition in an antecedent (If-part) from a trained fuzzy neural network. The paper also gives a method to extract automatically fuzzy If-Then rules from the trained neural network. To prove the effectiveness and validity of the proposed fuzzy neural expert system, a fuzzy neural expert system for medical diagnosis has been developed. >

Book ChapterDOI
01 Oct 1990
TL;DR: Preliminary results show that the fuzzy classifier system can effectively create fuzzy rules that imitate the behavior of a simple static system.
Abstract: This paper presents a proposal for a machine learning system, called the fuzzy classifier system. The fuzzy classifier system allows for inputs, outputs, and internal variables to take continuous values over given ranges. The fuzzy classifier system learns by creating fuzzy rules which relate the values of the input variables to internal or output variables. It has credit assignment and conflict resolution mechanisms which reassemble those of common classifier systems, with a fuzzy nature. The fuzzy classifier system employs a genetic algorithm to evolve adequate fuzzy rules. Preliminary results show that the fuzzy classifier system can effectively create fuzzy rules that imitate the behavior of a simple static system.

Journal ArticleDOI
Isao Hayashi1, H. Tanaka
TL;DR: In the fuzzy GMDH, the estimated model by using possibilistic linear regression with a multilayer procedure is a non-linear system with parameters in the form of fuzzy numbers, which can be regarded as fuzzy interval analysis.

Journal ArticleDOI
TL;DR: In this article, a fuzzy control logic for dead-time processes such as glass melting furnaces is discussed, and the effect of this new control logic has been examined through computer simulation and application to temperature control of the glass furnace.

Proceedings ArticleDOI
H. Kamada1, S. Naoi1, Toshiyuki Gotoh1
01 Apr 1990
TL;DR: A visual control system for an unmanned vehicle that quickly recognizes markers along a road and steers the vehicle and constructed fuzzy inference rules on how position changes with time.
Abstract: A visual control system for an unmanned vehicle is developed. The system uses dynamic image processing and fuzzy logic control. It quickly recognizes markers along a road and steers the vehicle. The markers are detected in real time by pipeline processing in the color identification processor and logical filter. The marker sequence is recognized by an improved Hough transform, then fuzzy theory decides the steering angle. To use the information on the movement of the vehicle, the authors constructed fuzzy inference rules on how position changes with time. The authors developed an LSI chip for the logical filter to make the system compact and practical (A4 size*10 cm). This system is mounted on a vehicle, and it steered the vehicle around a test track successfully. >


Proceedings ArticleDOI
03 Jul 1990
TL;DR: Simulation results show that this technique improves the overall response of the system in presence of asymmetric dynamic characteristics of the process, proving that this strategy can be a suitable alternative to ordinary fuzzy control.
Abstract: Presents a self organizing fuzzy linguistic control strategy that is based on on-line modification of the control rules according to the extent of deviation of the process output from the output of a given reference model. Accordingly, the learning/adaptation algorithm, which is based on the hill climbing approach, modifies the parametrized characteristic functions of the fuzzy subsets describing the control rules, such that the meaning of each rule is iteratively changed to reflect new information regarding the behavior of the process. Simulation results show that this technique improves the overall response of the system in presence of asymmetric dynamic characteristics of the process, proving that this strategy can be a suitable alternative to ordinary fuzzy control. In order to study this self organizing scheme, the authors used a simulation model with similar characteristics as the gas metal arc welding process. This model simulates the variation of the peak surface temperature of the workpiece directly underneath the weld bead in response to changes in the arc current, or more accurately, changes in the electrode wire feedrate. >

Journal ArticleDOI
TL;DR: The pad is to be analysed in order to know what the relation is between fuzzy and classical PID controllers, and to ensure that such fuzzy controller will be stable if the PD controller is.

Journal Article
TL;DR: It is concluded that it may be feasible to employ this fuzzy controller in the management of mean arterial pressure of patients clinically and various applications of the fuzzy controller may exist in industry and in biomedical engineering.
Abstract: This paper deals with real-time fuzzy control of mean arterial pressure (MAP) in pigs by regulating the infusion rate of the vasodilator drug, sodium nitroprusside (SNP). The fuzzy controller was based on the parallel firing mode of a general-purpose Fuzzy LOgic Production System shell, FLOPS [2,3,16,20]. One of the major advantages of this fuzzy control drug delivery system over other existing automatic drug delivery systems is that the fuzzy control system may be designed by using experts' knowledge and experience without any explicit mathematical models involved. Mean arterial pressure in pigs was controlled satisfactorily in real-time by the fuzzy control system. It comes to the conclusion that it may be feasible to employ this fuzzy controller in the management of mean arterial pressure of patients clinically. Also, various applications of the fuzzy controller may exist in industry and in biomedical engineering.

Proceedings ArticleDOI
03 Dec 1990
TL;DR: In this paper, a system identification method is developed, in which measured field data are assumed to be fuzzy data and fuzzy regression analysis is applied to the process of system identification, which can be solved without difficulty by using a linear programming algorithm.
Abstract: A system identification method is developed, in which measured field data are assumed to be fuzzy data and fuzzy regression analysis is applied to the process of system identification. Although the method includes fuzzy coefficients in the formulation, it can be solved without difficulty by using a linear programming algorithm. This fuzzy system identification method has been applied to the construction of a cable-stayed bridge, the Shugahara-Shirokita Bridge in Osaka, Japan. The results confirm that the system identification technique proposed is not only simple to handle but also very practical, compared with previous methods. >