• 전자공학회논문지C
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• 제36C권12호
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• pp.69-77
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• 1999

본 논문에서는 간편 간접추론방법을 이용한 퍼지 디지털 PID 제어기의 설계 방법을 제안하였다. 제안된 퍼지 제어기는 선형 디지털 PID 제어기에서 유도하였으며, 간편 간접추론을 이용한 퍼지화부, 제어규칙 베이스 및 퍼지화부의 설계방법을 설명하였다. 제안된 퍼지 제어기는 종래의 디지털 PID 제어기를 기초로 설계하였으므로 구조를 이해하기 쉽고, 퍼지입력에 의한 비선형 특성을 가지므로 선형 및 비선형 플랜트에 적응 능력을 가진다. 또한 각 입력변수 별로 간편 간접추론방법을 사용하여 추론하므로 고속 추론이 가능하고, 퍼지규칙의 수가 증가하여도 쉽게 적용 가능하다. 제안된 제어기의 성능을 D. Misir 등이 사용한 선형 및 비선형 플랜트에 모의 실험하여 효용성을 입증하였다.

• 한국지능시스템학회논문지
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• 제22권3호
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• pp.361-366
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• 2012

본 논문은 퍼지 제어기를 이용하여 전기 이륜차의 속도를 제어한다. 전기 이륜차는 스로틀에 대하여 빠른 응답성과, 정속주행 시 안정성이 요구 된다. 그러나 현재 양산중인 전기 이륜차는 1.5KW(50cc)급으로 탑승자 및 수화물 중량, 도로 상태(비포장 길, 아스팔트 길) 및 바람의 저항 등 부하 변동에 따라 속도 변화가 매우 크므로 정속 주행이 어려우며, 오르막길과 내리막길의 경사도에 따라 급격한 속도 변화가 발생된다. 이러한 문제점을 해결하기 위하여 속도의 오차와 오차의 변화량을 퍼지 제어기의 입력 값으로 사용하고, 모터의 Q축 제어 량을 퍼지 제어기의 출력 값으로 설정 하여 퍼지 제어기를 구성하였다. 모터의 D축 제어 량은 Q축 제어 량에 비례적으로 설정하여 매입형 영구 자석 동기 전동기(Interior Permanent Magnet synchronous Motor, IPMSM)를 장착한 전기 이륜차를 구동하였다. 본 논문에 적용된 제어 대상은 1.5KW급 전기이륜차로서 모터의 속도 제어를 위하여 제안된 알고리즘을 이용한 퍼지 제어기를 사용 하였으며, 매입형 영구 자석 동기전동기의 토크 제어를 위하여 D, Q축 전류 제어기를 사용 하였다. 제안된 알고리즘을 이용한 퍼지 제어기는 설정된 속도를 잘 추종하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 제36회 하계학술대회 논문집 A
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• pp.196-198
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• 2005

In this paper, we design the Thyristor Controlled Series Capacitor(TCSC) supplementary controller for oscillation damping of power system. Here the supplementary controller is designed as a fuzzy logic-based precompensation approach for TCSC. This scheme is easily implemented simply by adding a fuzzy precompensator to an existing TCSC. And we optimize the fuzzy precompensator with a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership function and control rules. Simulation results show that the proposed control technique is superior to a conventional method in dynamic responses over the wide range of operating conditions and is convinced robustness and reliableness in view of structure.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제51권12호
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• pp.691-696
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for induction motor speed control. When induction motor is continuously used long time, its electrical and mechanical Parameters will change, which degrade the Performance of PID controller considerably. This Paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using dSPACE(DS1102) board are performed to verify the effectiveness of the proposed scheme.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.350-353
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• 2003

In this paper, we proposed the method to design fuzzy controller using the experience of the operating expert and experimental numeric data for the robust control about the noise and disturbance instead of the traditional PID controller for the main steam temperature control of the thermal power plant. The temperature of main steam temperature process has to be controlled uniformly for the stable electric power output. The process has the problem of the hunting for the cases of various disturbances. In that case, the manual action of the operator happened to be introduced in some cases. We adopted the TSK (Takagi-Sugeno-Kang) model as the fuzzy controller and designed the fuzzy rules using the informations extracted directly from the real plant and various operating condition to solve the above problems and to apply practically. We implemented the real fuzzy controller as the Function Block module in the DCS(Distributed Control System) and evaluated the feasibility through the experiment81 results of the simulation.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권5호
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• pp.249-256
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• 2000

This paper deals with the control of the lateral motion of a mobile vehicle. A mobile vehicle using in this experiment is able to adapt many unmanned automatic driving system, for example, like a automated product transporting system. This vehicle is consist of the two servomotors. One is used to accelerate this vehicle and the another is used to change this lateral direction. An adaptive fuzzy logic controller(AFLC) is designed and applied to a experimental mobile vehicle in order to achieve the control of the lateral direction. An adaptive fuzzy logic controller(AFLC) is designed and applied to a experimental mobile vehicle in order to achieve the control of the lateral motion of the vehicle. Therefore, the main aim of this paper is investigate the possibility of applying adaptive fuzzy control algorithms to a microprocessor-based servomotor controller which requires faster and more accurate response compared with many other industrial processes. Fuzzy control rules are derived by modelling an expert's driving actions. Experiments are performed using a mobile vehicle with sensing units, a microprocessor and a host computer.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.265-271
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• 1992

It is the purpose of this paper to present a dialogical designing method for control system using a rough grasp of the unknown process property. We deal with a single-input single-output feedback control system with a fuzzy controller. The process property is roughly estimated by the step response, and the fuzzy controller is interactively modified according to the operator's requests. The modifying rules mainly derived from computer simulation are useful for almost every process, such as an unstable process and a non-minimum phase process. The fuzzy controller is tuned by taking notice of four characteristics of the step response: (1) rising time, (2) overshoot, (3) amplitude and (4) period of vibration. The tuning position of the controller is fourfold: (1) antecedent gain factor GE or GCE, (2) consequent gain factor GDU, (3) arrangement of the antecedent fuzzy labels and (4) arrangement of the control rules. The rules give an instance to the respective items of the controller in an effective order. The modified fuzzy PI controller realizes a good response of a stable process. However, because the GDU tuning becomes difficult for the unstable process, it is necessary to evaluate the stability of the process from the initial step response. The fuzzy PI controller is applied to the process whose initial step response converges with GDU tuning. The fuzzy PI controller with modified sampling time is applied to the process whose step response converges under the repeated application of the GDU tuning. The fuzzy PD controller is applied to the process whose step response never converges by the GDU tuning.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1186-1188
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• 1996

Neural networks and fuzzy systems have attracted the attention of many researehers recently. In general, neural networks are used to obtain information about systems from input/output observation and learning procedure. On the other hand, fuzzy systems use fuzzy rules to identify or control systems. In this paper we present a generalized FCMAC(Fuzzified Cerebellar Model Articulation Controller) networks, by integrating fuzzy systems with the CMAC(Cerebellar Model Articulation Controller) networks. We propose a direct adaptive controller design based on FCMAC(fuzzified CMAC) networks. Simulation results reveal that the proposed adaptive controller is practically feasible in nonlinear plant control.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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• pp.187-192
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• 2004

In this paper, we proposed the method to design fuzzy controller using the experience of the operating expert and experimental numeric data for the robust control about the noise and disturbance instead of the traditional PID controller for the main steam temperature control of the thermal power plant. The temperature of main steam temperature process has to be controlled uniformly for the stable electric power output. The process has the problem of the hunting for the cases of various disturbances. In that case, the manual action of the operator happened to be introduced in some cases. We adopted the TSK (Takagi-Sugeno-Kang) model as the fuzzy controller and designed the fuzzy rules using the informations extracted directly from the real plant and various operating condition to solve the above problems and to apply practically. We implemented the real fuzzy controller as the Function Block module in the DCS(Distributed Control System) and evaluated the feasibility through the experimental results of the simulation.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권8호
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• pp.473-480
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• 2000

A fuzzy PID controller is proposed for the posture control of a two DOF robot vehicle inspecting the defects of the inner wall of sewage pipes. The main difficulty in controlling these kinds of vehicles lies in that the center of two mobile shafts does not coincide with the weight center of the vehicle due to its long and wide shape. In this case the previous controller, based on the assumption that the gap between these centers are small, can not guarantee satisfactory transient response characteristics. In this paper, this gap is included in the mathematical modelling of the robot kinematics, and in order to compensate the unsatisfactory transient response characteristics, the fuzzy PID controller is proposed. This controller tunes the PID control gains with respect to the current state of the errors between the reference and the current postures. A series of simulations has been performed to investigate the tracking performance of the proposed controller for the lane changing path and the robustness to the external disturbance. The simulation results show that the proposed controller has a satisfactory tracking performance in the transient state as compared with that of the backstepping control given in reference.