• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 추계학술대회 논문집 학회본부
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• pp.342-344
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• 1994

This paper presents a design technique of SOFLC(Self -Organizing Fuzzy Logic Controller). It is composed of three parts: FLC(Fuzzy Logic Controller) part, RPO (Repeat Parameter Organizing) part, and RTPO (Real Time Parameter Organizing) part. The FLC part is controlled by initial parameters ($a_1$, $a_2$, $a_3$, $b_1$, $b_2$, $b_3$) the RPO part improves parameters by evaluating the performance of control responses controlled by FLC, and the RTPO organizes the parameters for real time in order to have the same value of the control response($y_k$) and the target response($y_k\;^*$).

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1995년도 춘계학술대회논문집; 전남대학교, 19 May 1995
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• pp.142-146
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• 1995

In the paper, Fuzzy Logic Controller(FLC) that determines its optimal coefficients using Genetic Algorithms is considered. It is also applied to the inverted pendulum problem known popularly as a standard plant. Flexibility of the inverted pendulum has been taken into account. In the results, Fuzzy Logic Controller under consideration successfully controls both rigid mode and flexible mode. The rule base of Fuzzy Logic Controller is automatically tuned using not only trial-error method but also Genetic Algorithms.

• 한국정보통신학회논문지
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• 제11권5호
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• pp.1019-1028
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• 2007

본 논문에서는 적응진화알고리즘을 사용한 퍼지 제어기의 설계방법을 제안하였다. 적응진화알고리즘은 전역탐색특성이 우수한 유전알고리즘과 다음세대를 포함하는 해집단에 대해 적응적으로 우수한 국부탐색특성을 가진 진화전략을 사용한다. 재교배 과정에서 유전알고리즘과 진화전략을 위한 해집단의 분배는 적합도에 따라서 적응적으로 결정된다. 적응진화알고리즘은 퍼지제어기의 설계 파라메터인 퍼지변수에 대한 소속함수와 스케일 요소를 결정하는데 사용된다. 제기된 퍼지제어기의 성능을 평가하기 위해서 비선형 특성을 가진 실제 DC 모터 속도제어 시스템을 구성하여 실험하였으며, 실험결과 PD제어기의 경우보다 우수한 속도 제어성능을 가짐을 확인하였다.

• 조명전기설비학회논문지
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• 제20권10호
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• pp.100-106
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• 2006

본 논문에서는 영구자석 동기전동기(PMSM)를 위한 풀 퍼지 로직을 기반으로 한 벡터제어를 제안한다. 퍼지 로직 제어기(FLC)를 기반으로 한 PMSM 드라이버의 성능은 부하, 속도 지령의 스텝 변화와 같은 여러 가지의 조건에서 PI제어기와 비교, 연구되었다. 실험 및 시뮬레이션에서 퍼지 로직 제어기는 속도 제어기 및 전류 제어기에 적용하였다. 고성능 드라이버 시스템에 기존의 PI제어기를 대신할 수 있음을 실험결과에 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.524-527
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• 2003

This paper proposes speed control system using a Fuzzy Logic Controller(FLC) in order to realize the speed control of Permanent Magnet AC Motor with no sensor. FLC based MRAS(Model Reference Adaptive System) estimates the speed of Permanent Magnet AC Motor. Using the estimated speed, speed control is performed. The experiment is executed to verify the propriety and the effectiveness of the proposed system.

• Journal of Biosystems Engineering
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• 제25권1호
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• pp.55-62
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• 2000

Autonomous farm operation needs to be developed for safety, labor shortage problem, health etc. In this research, an autonomous tractor for tillage was investigated using machine vision and a fuzzy logic controller(FLC). Tractor heading and offset were determined by image processing and a geomagnetic sensor. The FLC took the tractor heading and offset as inputs and generated the steering angle for tractor guidance as output. A color CCD camera was used fro the image processing . The heading and offset were obtained using Hough transform of the G-value color images. 15 fuzzy rules were used for inferencing the tractor steering angle. The tractor was tested in the file and it was proved that the tillage operation could be done autonomously within 20 cm deviation with the machine vision and the FLC.

• 제어로봇시스템학회논문지
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• 제17권9호
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• pp.897-901
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• 2011

This paper presents a TS (Takagi-Sugeno) type FLC (Fuzzy Logic Controller) with only 3 rules. The choice of parameters of FLC is very difficult job on design FLC controller. Therefore, the choice of appropriate linguistic variable is an important part of the design of fuzzy controller. However, since fuzzy controller is nonlinear, it is difficult to analyze mathematically the affection of the linguistic variable. So this choice is depend on the expert's experience and trial and error method. In the design of the system, we use a variety of response characteristics like stability, rising time, overshoot, settling time, steady-state error. In particular, it is important for a stable system design to predict the steady-state error because the system's steady-state response of the system is related to the overall quality. In this paper, we propose the method to choose the consequence linear equation's parameter of T-S type FLC in the view of steady-state error. The parameters of consequence linear equations of FLC are tuned according to the system error that is the input of FLC. The full equation of T-S type FLC is presented and using this equation, the relation between output and parameters can represented. As well as the FLC parameters of consequence linear equations affect the stability of the system, it also affects the steady-state error. In this study, The system according to the parameter of consequence linear equations of FLC predict the steady-state error and the method to remove the system's steady-state error is proposed using the prediction error value. The simulation is carried out to determine the usefulness of the proposed method.

• 드라이브 ㆍ 컨트롤
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• 제17권1호
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• pp.44-50
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• 2020

For the requirement of accurate tracking control and the safety of physical human-robot interaction, torque control is basically desirable for humanoid robots. Because of the complexity of humanoid robot dynamics, the TDC (time-delay control) is practical because it does not require a dynamic model. However, there occurs a considerable error due to discontinuous non-linearities. To solve this problem, the TDC-FLC (fuzzy logic compensator) is applied to humanoid robots. The applied controller contains three factors: a TDE (time-delay estimation) factor, a desired error dynamic factor, and FLC to suppress the TDE error. The TDC-FLC is easy to execute because it does not require complicated humanoid dynamic calculations and the heuristic fuzzy control rules are intuitive. TDC-FLC is implemented on the whole body of a humanoid, not on biped legs even though it is performed by a virtual humanoid robot. The simulation results show the validity of the TDC-FLC for humanoid robots.

• 제어로봇시스템학회논문지
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• 제10권2호
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• pp.125-130
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• 2004

This paper describes an approach for synthesizing a Fuzzy Logic Controller(FLC) that combines the benefits of fuzzy logic control and fuzzy logic gain scheduling for the F/A-18 aircraft. Specially, fuzzy rules are utilized on-line to determine the denoralization factor(Κ) of a feedback fuzzy controller based on the dynamic pressure(Q) indicateing the region of the flight envelop the aircraft is operating in. Simulation results demonstrate that the proposed FLC provides excellent compensation for time-varying and/or nonlinear characteristics of the aircraft, and that it also exhibits satisfactory robustness with noisy air data sensors.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.51-56
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• 1998

In this paper, we analyze the effects of scaling factors on the performance of a fuzzy logic controller(FLC). The quantitative relation between input and output variables of FLC is obtained by using a qualsi-linear fuzzy model, and an approximate transfer function of FLC is dervied from the comparison of it with the conventional PID controller. Then we analyze in detail the effects of scaling factor using this approximate transfer function and root locus method. Also we suggest an on-line tuning method for scaling factors which employs an sample performance function and a variable reference for tuning index.