• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.984-989
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• 1991

In this paper, a novel type of a visual servoing method is proposed for eye-in-hand robots by employing a self-organizing fuzzy controller. For this is there defined a new Jacobian riot to be the function of a relative position of the object but to be a function of the only image features. Instead of obtaining an analytic form of the proposed Jacobian, a self-organizing fuzzy controller is then proposed to alleviate difficulties in real-time implementation. To show the validities, the proposed method is applied to a 2-dimensional visual servoing task.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 컴퓨터소사이어티 추계학술대회논문집
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• pp.227-230
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• 2003

We can not distinguish semantism of the feature of the current language “big”, “small”, “beautiful”. But we study artificial linguistic interface work and convert natural language to digital binary linguistic theory, we should define the basical conversion process. When we utilize the sum of product fuzzy theory and the visible numerical value, we can establish reasoning rule of input language. Fuzzy theory should be converted to general resulting rule.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.830-833
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• 1993

In this paper, an optimal tuning Algorithms is presented to automatically improve the performance of fuzzy controller, using the simplified reasoning method and the proposed complex method. The method estimates automatically the optimal values of the parameters of fuzzy controller, according to the change rate and limitation condition of output. The controller is applied to plants with dead time. Then, computer simulations are conducted at step input and the performances are evaluated in the ITAE.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.185-188
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• 1996

In this paper, a fuzzy PNN algorithm is proposed to estimate the structure and parameters of fuzzy model, using the PNN based on GMDH algorithm. New algorithm uses PNN algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the leastsquare method in order to identify the optimal consequence parameters. Both time series data for gas furnace and data for wastewater treatment process are used for the purpose of evaluating the performance of the fuzzy PNN. The results show that the proposed technique can produce the fuzzy model with higher accuracy than other works achieved previously.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1997년도 추계학술대회 논문집
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• pp.70-75
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• 1997

This paper proposes a new approach to the design of fuzzy-neuro control for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• 한국지능시스템학회논문지
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• 제5권3호
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• pp.36-51
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• 1995

An approach to robot hybrid position/force control, which allows force manipulations to be realized without overshoot and overdamping while in the presence of unknown environment, is given in this paper. The manin idea is to used dynamic compensation for known robot parts and fuzzy compensation for unknown environment so as to improve system performance. The fuzzy compensation is implemented by using rule based fuzzy approach to identify the unknown environment. The establishment of proposed control system consists of following two stages. First, similar to the resovled acceleration control method, dynamic compensation and PD control based on known robot dynamics, kinematics and estimated environment stiffness is introduced. To avoid overshoot the whole control system is constructed with overdamping. In the second stage, the unknown environment stiffness is identified by using fuzzy reasoning, where the fuzzy compensation rules are obtained priori as the expression of the relationship betweenenvironment stiffness and system. Based on the simulation result, comparison between cases with or without fuzzy identifications are given, which illustrate the improvement achieced.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1383-1386
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• 1990

Fuzzy theory may be constructed of Fuzzy sets theory, Fuzzy measurement and Appoximate reasoning, and nowadays the reseaches on it are done widely in both theoretical and practical aspects. In this paper, the authors sought for the subject in Hirota's Fuzzy CAI: A decision making simulated fuzzy problem on the speed and accelerator control of a car driven in the high way. Interesting results on these fuzzy solutions of this problem, based on a defuzzification with 9 kinds of the methods, are reported. And the characters of these solutions, linear or nonlinear and their fuzzy control evaluations are stated.

• 제어로봇시스템학회논문지
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• 제7권7호
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• pp.622-631
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• 2001

This paper presents a fuzzy modified PID controller that uses linear fuzzy inference method. In this structure, the proportional and derivative gains vary with the output of the system under control. 2-input PD type fuzzy controller is designed to obtain the varying gains. The proposed fuzzy PID structure maintains the same performance as the same performance as the general-purpose linear PID controller, and enhances the tracking performance over a wide range of input. Numerical simulations and experimental results show the effectiveness of the fuzzy PID controller in comparison with the conventional PID controller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.153-158
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• 1995

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed fuzzy GMDH modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) algorithm and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH algorithm and fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnaceare those for sewage treatment process are used for the purpose of evaluating the performance of the proposed fuzzy GMDH modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2001년도 전력전자학술대회 논문집
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• pp.662-665
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• 2001

As the fuzzy control is applied to servo system the hardware implementation of the fuzzy information systems requires the high speed operations, short real time control and the small size systems. The aims of this study is to develop hardware of the fuzzy information systems to be apply to servo system. In this paper, we propose a calculation method of approximate reasoning for fuzzy control based on $\alpha$-level set decomposition of fuzzy sets by quantize $\alpha$-cuts. This method can be easily implemented with analog hardware. The influence of quantization levels of $\alpha$-cuts on output from fuzzy inference engine is investigated. It is concluded that 4 quantization levels give sufficient result for fuzzy control performance of do servo system. It examined useful with experiment for dc servo system.