• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.365-370
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• 2007

This paper studies an automation problem of a heating, a ventilating, and an air conditioning (HVAC) for the development of smart space. The HVAC system is described by the fuzzy system for the stability analysis and the controller design. The linear matrix inequalities (LMIs) conditions are derived for the stabilization problem of the closed-loop system under the analog control. Also, it is required to digitally redesign the pre-designed the analog HVAC control system in order to accomplish the remote control via web. It is shown the this digital redesign problem can be converted to the convex optimization problem with the LMI constraints. An example is provided to show the effectiveness of the proposed method.

• 한국지능시스템학회논문지
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• 제10권5호
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• pp.423-431
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• 2000

In this paper, the effect of computing time-delay in the real-time digital fuzzy control systems is investigated and the design methodology of a real-time digital fuzzy controller(DFC) to overcome the problems caused by it is presented. We propose the fuzzy feedback controller whose output is delayed with unit sampling period. The analysis and the design problem considering computing time-delay is very easy because the proposed controller is syncronized with the sampling time. The stabilization problem of the digital fuzzy control system is solved by the linear matrix inequality(LMI) theory. Convex optimization techniques are utilized to find the stable feedback gains and a common positive definite matrix P for the designed fuzzy control system Furthermore, we develop a real-time fuzzy control system for backing up a computer-simulated truck-trailer with the consideration of the computing time-delay. By using the proposed method, we design a DFC which guarantees the stability of the real time digital fuzzy control system in the presence of computing time-delay.

• 한국지능시스템학회논문지
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• 제2권3호
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• pp.29-39
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• 1992

We investigate a systematic design procedure of automatic rule generation of fuzzy logic based controllers for highly nonlinear dynamic systems such as an engine dynamic modle. By "automatic rule generation" we mean autonomous clustering or collection of such meaningful transitional relations from one conditional subspace to another. During the design procedure, we also consider optimaly control strategies such as minimum squared error, near minimum time, minimum energy or combined performance critiera. Fuzzy feedback control systems designed by our method have the properties of closed-loop stability, robustness under parameter variabitions, and a certain degree of optimality. Most of all, the main advantage of the proposed approach is that reliability can be potentially increased even if a large grain of uncertainty is involved within the control system under consideration. A numerical example is shown in which we apply our strategic fuzzy controller dwsign to a highly nonlinear model of engine idling speed control.d control.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1054-1057
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• 1993

A direct method of fuzzy inference and a fuzzy algorithm with learning function are applied to the steam generator level control of nuclear power plant. The fuzzy controller by use of direct inference can control the steam generator in the entire range of power level. There is a little long response time of fuzzy direct inference controller at low power level. The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0%∼30% of full power). Response time of steam generator level control at low power level with this rule base is shown generator level control at low power level with this rule base is shown to be shorter than that of fuzzy controller with direct inference.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1195-1198
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• 1993

We investigate a systematic design procedure of automated rule generation of fuzzy logic based controller for uncertain dynamic systems such as an engine dynamic model.“Automated Tuning”means autonomous clustering or collection of such meaningful transitional relations in the state-space. Optimal control strategies are included in the design procedures, such as minimum squared error, minimum time, minimum energy or combined performance criteria. Fuzzy feedback control systems designed by the cell-state transition method have the properties of closed-loop stability, robustness under parameter variabtions, and a certain degree of optimality. Most of all, the main advantage of the proposed approach is that reliability can be potentially increased even if a large grain of uncertainty is involved within the control system under consideration. A numerical example is shown in which we apply our strategic fuzzy controller design to a highly nonlinear model of engine idle speed contr l.

• 한국생산제조학회지
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• 제22권5호
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• pp.824-830
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• 2013

In ultra-precision machining, the inside temperature should be controlled precisely. The important factors are environmental conditions (outside temperature, humidity) and temperature conditions (cutting heat, spindle heat). Thus, in this study, an environmental control cell for the ultra-precision machine that could control the inside temperature and minimize effects of the surrounding environment to achieve a thermal deformation of less than 400nm of a specimen was designed and verified through C.F.D. Further, a method that could control the temperature precisely by using a blower, heat exchanger and heater was evaluated. As a result, this study established a C.F.D technic for the environmental control cell, and the specimen temperature was controlled to be within $17.465{\pm}0.055^{\circ}C$.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.525-527
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• 1999

In this paper, we propose the target tracking system using fuzzy basis function expansion (FBFN) based on genetic algorithm (GA). In general, the objective of target tracking is to predict the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical method, the parameter uncertainty and the environmental noise may deteriorate the performance of the system. To resolve these problems, we apply artificial intelligent technique to the tracking control of moving targets. The proposed method combines the advantages of both traditional and intelligent technique. The result of numerical simulation shows the effectiveness of the proposed method.

• 한국지능시스템학회논문지
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• 제8권5호
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• pp.5-13
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• 1998

본 논문에서는 DC-DC 컨버터에 적합한 퍼지제어기를 제안하였고, 이는 제어 파라메터의 변화가 심한 경우 기존의 선형 제어로는 만족할 수 없는 성능을 달성하기 위해서이다. 제안된 퍼지제어기는 범용으로 어떠한 DC-DC 컨버터 토폴로지(topologies)에도 적용할 수 있다. 퍼지 제어기 실행은 지존의 오차와 오차의 변화량에 의존하는 방식보다는 주요 상태변수를 이용하는 방식을 사용하였으며, 이는 DC-DC 컨보터의 경우 매우 효과적으로 작용하여 기존의 제어기보다 빠르고 안정적인 소신호응답 및 더욱 향상된 대신호응답을 보장하였다.예로서 부스트(boost) 컨버터에의 적용시 그 시물레이션 결과는 향상된 제어 가능성을 보여주고 있다.

• 한국지능시스템학회논문지
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• 제7권3호
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• pp.72-80
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• 1997

A new design method for Takagi-Sugeno (T-S in short) fuzzy controller which guarantees global asymptotic stability and satisfies a desired performance is proposed in this paper. The method uses LMI(Linear Matrix Inequality) approach to find the common symmetric positive definite matrix P and feedback fains K/sub i/, i= 1, 2,..., r, numerically. The LMIs for stability criterion which treats P and K'/sub i/s as matrix variables is derived from Wang et al.'s stability criterion. Wang et al.'s stability criterion is nonlinear MIs since P and K'/sub i/s are coupled together. The desired performance is represented as $ LMIs which place the closed-loop poles of $ local subsystems within the desired region in s-plane. By solving the stability LMIs and pole placement constraint LMIs simultaneously, the feedback gains K'/sub i/s which gurarntee global asymptotic stability and satisfy the desired performance are determined. The design method is verified by designing a T-S fuzzy controller for an inverted pendulum with a cart using the proposed method.

• 대한기계학회논문집
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• 제18권1호
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• pp.1-11
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• 1994

This paper presents an intelligent robotic control method for chamferless parts mating by integrating fuzzy control and neural network. The successful assembly task requires an extremely high position accuracy and a good knowledge of mating parts. However, conventional assembly method alone makes it difficult to achieve satisfactory assembly performance because of the complexity and the uncertainties of the process and its environments such as not only the limitation of the devices performing the assembly but also imperfect knowledge of the parts being assembled. To cope with these problems, an intelligent robotic assembly method is proposed, which is composed of fuzzy controller and learning mechanism based upon neural net. In this method, fuzzy controller copes with the complexity and the uncertainties of the assembly process, while neural network enhances the assembly scheme so as to learn fuzzy rules from experience and adapt to changes in environment of uncertainty and imprecision. The performance of the proposed assembly scheme is evaluted through a series of experiments using SCARA robot. The results show that the proposed control method can be effectively applied to chamferless precision parts mating.