• 제어로봇시스템학회논문지
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• 제18권6호
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• pp.555-561
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• 2012

This paper describes the design of a fuzzy adaptive sliding mode controller for tracking control of robotic manipulators. The proposed controller incorporates a modified traditional sliding mode controller to drive the system state to a sliding surface and then keep the system state on this surface, and a fuzzy logic controller to accelerate the reaching phase. The stability of the control system is ensured by using Lyapunov theory. To verify the effectiveness of the proposed controller, computer simulation is conducted for a five-bar planar robotic manipulator. The simulation results show that the proposed controller can improve the reaching time and eliminate chattering of the control system at the same time.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2819-2821
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• 2000

We introduce Adaptive Fuzzy Impedance Controller for force control when robot contact with environment. Because robot and environment was always effected by nonlinear conditions. it needs to deal with parameter's uncertainty. As. it induced Fuzzy system in impedance controller. it used fuzzy inference logic that has robustness about uncertainty to tune impedance controller stiffness gain. We applied adaptive fuzzy impedance controller in One-Link Robot system and the method shows a good performance on desired position and force control with intensional contacting environment.

• Journal of Communications and Networks
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• 제7권3호
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• pp.337-352
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• 2005

In this paper, a fuzzy logic implementation of the random early detection (RED) mechanism [1] is presented. The main objective of the proposed fuzzy controller is to reduce the loss probability of the RED mechanism without any change in channel utilization. Based on previous studies, it is clear that the performance of RED algorithm is extremely related to the traffic load as well as to its parameters setting. Using fuzzy logic capabilities, we try to dynamically tune the loss probability of the RED gateway. To achieve this goal, a two-input-single-output fuzzy controller is used. To achieve a low packet loss probability, the proposed fuzzy controller is responsible to control the $max_{p}$ parameter of the RED gateway. The inputs of the proposed fuzzy controller are 1) the difference between average queue size and a target point, and 2) the difference between the estimated value of incoming data rate and the target link capacity. To evaluate the performance of the proposed fuzzy mechanism, several trials with file transfer protocol (FTP) and burst traffic were performed. In this study, the ns-2 simulator [2] has been used to generate the experimental data. All simulation results indicate that the proposed fuzzy mechanism out performs remarkably both the traditional RED and Adaptive RED (ARED) mechanisms [3]-[5].

• 전자공학회논문지SC
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• 제41권3호
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• pp.39-46
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• 2004

본 논문은 IPMSM 드라이브의 고성능 속도 제어를 위하여 퍼지제어와 신경회로망을 혼합 구성한 적응 FNN 제어기를 제시한다. 적응 FNN 제어기는 기준 모델에 기초한 적응 메카니즘을 적용하여 신경회로망의 고도의 적응제어와 퍼지제어기의 강인성 제어의 장점들을 접목한다. 적응 FNN 제어기의 출력은 FNN 제어기의 출력과 적응 퍼지제어의 출력을 합하여 출력을 얻는다. 적응 FNN 제어기는 다양한 동작조건에서 응답특성을 분석하고 평가한다. 제시한 적응 FNN 제어기의 타당성은 IPMSM 드라이브 시스템에 적용하여 성능 결과로 입증한다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2000년도 학술대회논문집
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• pp.193-198
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• 2000

Switched reluctance motor drives have been finding their applications in the variable speed drives due to their relatively low cost, simple and robust structure, controllability and high efficiency. Fuzzy control does not need any model of plant. It is based on plant operator experience and heuristics. Fuzzy control is basically adaptive and gives robust performance for plant parameter variation. This paper deals with the sped control of switched reluctance motor using fuzzy controller with 7-rule based fuzzy logic. The proposed fuzzy controller is superior to the control performance of the conventional PI controller. The fuzzy controller is implemented by 80C196KC, 16 bit one-chip microcontroller.

• 한국지능시스템학회논문지
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• 제15권5호
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• pp.527-532
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• 2005

본 논문에서 불확실한 근사화 오차 유계 추정을 이용한 불확실한 비선형 계통에 대한 적응 퍼지 슬라이딩 모드 제어기를 제안하였다. 계통 출력이 기준 출력을 추종하기 위해 시스템의 불확실성은 결론부 파라미터의 적응 알고리즘에 의해 온라인으로 조정되는 IF-THEN 규칙을 가지는 퍼지 시스템에 의해 근사화하였다. 또한 근사화 오차가 미지의 상수에 의해 유계된다는 가정 하에 리아프노프 합성법으로 근사화 오차 유계 추정 알고리즘을 제안하였다. 전체 제어 시스템은 제어기내의 모든 신호가 균등 유계이고 추종오차가 점근 안정함을 보장한다. 제안한 적응 퍼지 슬라이딩 모드 제어기의 성능을 도립진자 계통에 대한 컴퓨터 모의실험을 통해 입증하였다.

• 전기학회논문지
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• 제56권1호
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• pp.183-187
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• 2007

This paper proposes Fuzzy Speed Estimator using Fuzzy Logic Controller(FLC) as a adaptive law in Model Reference Adaptive System(MRAS) in order to realize the speed-sensorless control of an induction motor. Fuzzy Speed Estimator estimates the speed of an induction motor with a rotor flux of the reference model and the adjustable model in MRAS. Fuzzy logic controller reduces the error of the rotor flux between the reference model and the adjustable model using the error and the change of error of the rotor flux as the input of FLC. The experiment is executed to verify the propriety and the effectiveness of the proposed speed estimator.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권2호
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• pp.145-150
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• 2005

We proposed the indirect adaptive fuzzy model based sliding mode controller to control nonaffine nonlinear systems. Takagi-Sugano fuzzy system is used to represent the nonaffine nonlinear system and then inverted to design the controller at each sampling time. Also sliding mode component is employed to eliminate the effects of disturbances, while a fuzzy model component equipped with an adaptation mechanism reduces modeling uncertainties by approximating model uncertainties. The proposed controller and adaptive laws guarantee that the closed-loop system is stable in the sense of Lyapunov and the output tracks a desired trajectory asymptotically.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2005년도 학술대회 논문집
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• pp.315-320
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• 2005

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of nor measured between the motor speed and output of a reference model. The control performance of the adaptive fuzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권2호
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• pp.76-82
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• 2009

A Heating, Ventilating and Air Conditioning (HVAC) system is a nonlinear multi-input multi-output (MIMO) system. This system is very difficult to control the temperature and the humidity ratio of a thermal space because of complex nonlinear characteristics. This paper proposes an adaptive fuzzy output feedback control based on observer for the nonlinear HVAC system. The nonlinear HVAC system is linearized through dynamic extension. State observers are designed for estimating state variables of the HVAC system. Fuzzy systems are employed to approximate uncertain nonlinear functions of the HVAC system with unavailable state variables. The obtained controller compares with an adaptive feedback controller. Simulation is given to demonstrate the effectiveness of our proposed adaptive fuzzy method.