• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.310-310
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• 1996

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.3B no.1
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• pp.52-58
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• 2003

The application of a sliding mode control for improving the dynamic response of an induction motor based speed control system is presented in this paper and provides attractive features, such as fast response, good transient performance, and insensitivity to variations in plant parameters and external disturbance. However, chattering is a difficult problem for which the sliding mode control is a popular solution. This paper presents a new fuzzy-sliding mode controller for a sensorless vector-controlled induction motor servo system to practically eliminate the chattering problem for traction applications. A DSP based implementation of the speed control system is employed. Experimental results are presented using a propulsion system simulator. The performance of the drive is shown to be practically free from chattering.

• Journal of the Korean Institute of Navigation
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• v.14 no.4
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• pp.17-30
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• 1990

Since L.A. Zadeh introduced the theory of fuzzy sets in 1965, E.H. Mamdani applied the theory to the steam engine control in 1974. Since then, scientists have shown a great deal of interests in its application to practical problems and the possibility of the application of the theory a more complicate system has been increasing greatly. In the fuzzy control, the qualitative knowledge and intuition that the operators of a system has acquired through their experience can be logically described by the Linguistic Control Rule(LCR). The algorithm of th control is made of the LCR, and th control of an object is performed by processing this algorithm implementing a computer. in this thesis, the fuzzy controller of the ship's steering system is devided into two systems, namely FC1 and FC2, according to their control function. FC1 is for the course keeping steering, wheress FC2 is for the altering of s ship's course. The characteristics of the control system were investigated through the digital computer simulation and the results were compared with those of the conventional steering system. It was found that the fuzzy control was more efficient than the conventional auto pilot system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.182-187
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• 2007

A fuzzy modeling method is proposed to build the dynamic model of a walking-beam reheating furnace from the recorded data. In the proposed method, the number of membership function on each variable is increased individually and the modeling accuracy is evaluated iteratively. When the modeling accuracy is satisfied, the membership functions on each variable are fixed and the structure of fuzzy model is determined. Because the training data is limited, in this process, as the number of membership function increase, it is highly possible that some rules are missing, i.e., no data in the training set corresponds to the consequent part of a missing rule. To complete the rulebase, the output of the model constructed at the previous step is used to generate the consequent part of the missing rules. Finally, in the real time application, a rolling update scheme to rulebase is introduced to compensate the change of system dynamics and fine tune the rulebase. The proposed method is verified by the application to the modeling of a reheating furnace.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.5
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• pp.379-383
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• 2003

Parallel type fuzzy controller is designed by using a hybrid connected type fuzzy-PID controller and a model reference fuzzy controller. The first controller, consists of a fuzzy-PI and a fuzzy-PD making a hybrid type fuzzy-PID controller, plays a role as firstly reaching stable responses and secondly overcoming disturbance in plants. The second controller, model reference fuzzy controller, plays a role as reaching faster responses than other controllers. We have confirmed that the controller produces rapid and stable responses and overcomes disturbance by using parallel type fuzzy controller in a DC motor application.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.43-52
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• 1994

A design method of rule-based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of ``IF....,THEN...', using the theories of optimization theory , linguistic fuzzy implication rules and fuzzy c-means clustering. Three kinds of method for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 2), and modified linear inference (type 3). In order to identify premise structure and parameter of fuzzy implication rules, fuzzy c- means clustering and modified complex method are used respectively and the least sequare method is utilized for the identification of optimum consequence parameters. Time series data for gas furance and those for sewage treatment process are used to evaluate the performance of the proposed rule-based fuzzy modeling. Comparison shows that the proposed method can produce the fuzzy model with higher accuracy than previous other studies.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.7-13
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• 1999

This paper propose a fuzzy regression method using fuzzy neural networks when a membership value is attached to each input -output pair. First, an architecture of fuzzy neural networks with fuzzy weights and fuzzy biases is shown. Next, a cost function is defined using the fuzzy output from the fuzzy neural network and the corresponding target output with a membership value. A learning algorithm is derived from the cost function. The derived learning algorithm trains the fuzzy neural network so that the level set of the fuzzy output includes the target output. Last, the proposed method is applied to the quality evaluation problem of injection molding

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.233-236
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• 2001

In this paper, we propose a TSK-type fuzzy classifier using PCA(Principal Component Analysis), FCM(Fuzzy C-Means) clustering and hybrid GA(genetic algorithm). First, input data is transformed to reduce correlation among the data components by PCA. FCM clustering is applied to obtain a initial TSK-type fuzzy classifier. Parameter identification is performed by AGA(Adaptive Genetic Algorithm) and RLSE(Recursive Least Square Estimate). we applied the proposed method to Iris data classification problems and obtained a better performance than previous works.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.384-388
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• 1999

This paper describes an approach for synthesizing a modularized gain scheduled PD type fuzzy logic controller(FLC) of nonlinear magnetic bearing system where the gains of FLC are on-line adapted according to the operating point. Specifically the systematic procedure via root locus technique is carried out for the selection of the gains of FLC. Simulation results demonstrate that the proposed gain scheduled fuzzy logic controller yields not only maximization of stability boundary but also better control performance than a single operating point (without gain scheduling)fuzzy controller.

• Journal of applied mathematics & informatics
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• v.20 no.1_2
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• pp.433-443
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• 2006

In [1], they build two populations' cellular automata model with predation based on the Penna model. In this paper, uncertain aspects and problems of imprecise and vague data are considered in this model. A fuzzy cellular automata model containing movable wolves and sheep has been built. The results show that the fuzzy cellular automata can simulate the classical CA model and can deal with imprecise and vague data.