• Journal of the Korean Institute of Navigation
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• v.5 no.2
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• pp.59-88
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• 1981

Many studies have been done in the field of fuzzy logic theory, but it's application is not so much, and particularly, there isn't any application to the ship's steering system, until now. This paper is to survey the effect of application of fuzzy logic control to the ship's steering system. The controller is made up of a set of Linguistic Control Rules which are conditional linguistic statements connecting the inputs and the output, and take the inputs derived from the errors, that is, deviation angle and it's angular velocity. These two variables together give information about the state of the steering system, and the Linguistic Control Rules are implemented on the digital computer. The characteristics of this system were investigated through the computer simulation and satisfactory results compared with that of the conventional PD controller were obtained.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.619-624
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• 1998

This paper presents a new clustering algorithm called FCM algorithm for the design of fuzzy controller. FCM is an extended version of FCM(Fuzzy c-Means) algorithm and can estimate the number of clusters automatically and give membership grades $u_{ik}$ suitable for making fuzzy control rules. This paper also shows an example of its application to the line pursuit control of a car.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1063-1067
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• 1993

Fuzzy logic is applied to a roll autopilot for missiles. Fuzzy rules are made so that the response duplicates that of the conventional control law for some flight condition. A scaling factor of the fuzzy controller is then scheduled by the missile velocity and altitude information to cope with the variation of the roll dynamics from that flight condition. By computer simulations and calculation of the stability margin, it is shown that the fuzzy control is robuster than the conventional one over the flight envelope even though two control laws work similarly for some flight conditions.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1808-1811
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• 1997

A position control algorithm for a flexible manipulato is stuudied. The proposed algorithm is based on a fuzzy theroy with a Steady State Genetic Algorithm(SSGA). The conventional fuzzy methods need expert's knowledges or human experiences. The SSGA, which is one of the optimization algorithms, tunes automatically the input-output membership parameters and fuzzy rules. The computer simulation is presented ot illustrate the approaches. Finally we applied a fuzzy theory with a SSGA to aposition control of a flexible manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.177-177
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• 2000

In this paper, we propose a two-input two-output fuzzy controller to improve the performance of transient response and to eliminate the steady state error. The outputs of this controller are the control input calculated by position-type fuzzy controller and the accumulated error scaling factor. Here, the accumulated error scaling factor is adjusted on-line by fuzzy rules according to the current trend of the controlled process. To show the usefulness of the proposed controller, it is applied to several systems that are difficult to get satisfactory response by conventional PD controllers or PI controllers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1078-1081
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• 1993

The Objective of this paper is to provide fuzzy control designers with a design tool for stable fuzzy logic controllers. Given multiple sets of data disturbed by vagueness uncertainty, we generate the implicative rules that guarantee stability and robustness of closed-loop fuzzy dynamic systems. We propose the cell-state transition method which utilizes Hsu's cell-to-cell mapping concept [1]. As a result, a generic and implementable design methodology for obtaining a fuzzy feedback gain K, a fuzzy hypercube [2], is provided and illustrated with simple examples.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.218-220
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• 1993

This paper deals with a self-tuning fuzzy controller. The fuzzy controller is constructed with linguistic rules which consist of the fuzzy variables and fuzzy sets. Each of fuzzy sets is characterized by a membership function. The tuning fussy controller has paramemters to effect control output. In this paper we propose tuning method for the scaling factor. Computer simulations carried out on a second-order process will show how the present tuning approach improves the transient and steady-state characteristics of the overall system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.1
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• pp.165-177
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• 2000

A fuzzy controller need membership functions and the control rules depend on heuristic knowledge of expertises entirely. On account of, it is possible that a desired performance of a fuzzy controller can not be guaranteed or easily degraded under some circumstances such as a change of plant parameter which exporters do not considered. Therefore, in this paper we tried to increase the controller's efficiency by adjusting the control rules and the parameters of the membership functions by using a genetic algorithm. We also proposed the Self-Organizing Fuzzy Controller which uses the Fusion Method in order to minimize the number of control rules and to construct the intuitive controller. For validation of the proposed algorithm, we design the Autonomous Guided Vehicle Controller, then apply to variant condition.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.189-192
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• 1996

This paper presents a systematic approach to identify a linguistic fuzzy model for a multi-input and single-output complex system. Such a model is composed of fuzzy rules, and its output is inferred by the simplified reasoning. The structure and membership function parameters for a fuzzy model are automatically and simultaneously identified by GA (Genetic Algorithm). After GA search, optimal parameters for the fuzzy model are finely tuned by a gradient method. A numerical example is provided to evaluate the feasibility of the proposed approach. Comparison shows that the suggested approach can produce the linguistic fuzzy model with higher accuracy and a smaller number of rules than the ones achieved previously in other methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.568-572
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• 1998

This paper defines Fuzzy Logic Units(FLUs) which are piece wise finite elements in multidimension Euclidean space, and redefines triangular membership functions which are different from those defined in traditional literature. By analyzing FLUs, this paper gives a constructive method of fuzzy rules in fuzzy logic systems based on finite element method. The simulation results of single machine to infinite bus system show the effectiveness of the proposed method in this paper.