• 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.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.530-543
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• 2013

This paper describes a control scheme of speed sensorless fuzzy direct torque control (FDTC) of permanent magnet synchronous motor for electric vehicle (EV). Electric vehicle requires fast torque response and high efficiency of the drive. Speed sensorless FDTC In-wheel PMSM drives without mechanical speed sensors at the motor shaft have the attractions of low cost, quick response and high reliability in electric vehicle application. This paper presents a new approach to estimate the speed of in-wheel electrical vehicles based on Model Reference Adaptive System (MRAS). The direct torque control suffers in low speeds due to the effect of changes in stator resistance on the flux measurements. To improve the system performance at low speeds, a PI-fuzzy resistance estimator is proposed to eliminate the error due to changes in stator resistance. High performance sensorless drive of the in-wheel motor based on MRAS with on line stator resistance tuning is established for four motorized wheels electric vehicle and the whole system is simulated by matalb/simulink. The simulation results show the effectiveness of the new control strategy. This proposed control strategy is extensively used in electric vehicle application.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.353-353
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• 2000

To control a light weight flexible manipulator, a composite fuzzy controller is proposed. The controller is designed based on two time scaled models. A singular perturbation technique is applied for deriving the models. The proposed controller, however, does not use the complex equilibrium manifold equations, which are usually needed in the controller based on the two time scaled models. The controller for a slow sub-model and a fast sub-model are T-S type fuzzy controllers, which use 3 linguistic variables for each sub-model. A step trajectory is used in simulations as a reference trajectory of joint motions. The results of simulations with the proposed controller show excellent damping of flexible motions compared to a controller with derivative control of flexible motions.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2399-2410
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• 2017

In this paper, a linear matrix inequality-based sampled-data fuzzy observer design method is proposed based on the exact discretization approach. In the proposed design technique, a numerically relaxed observer design condition is obtained by using the discrete-time fuzzy Lyapunov function. Unlike the existing studies, the designed observer is robust to the uncertain premise variable because the fuzzy observer is designed under the imperfect premise matching condition, in which the membership functions of the system and observer are mismatched. In addition, we apply the proposed method to the state estimation problem of the attitude and heading reference system (AHRS). To do this, we derive a Takagi-Sugeno fuzzy model for the AHRS system, and validate the proposed method through the hardware experiment.

• 한국지능시스템학회논문지
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• 제6권2호
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• pp.97-105
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• 1996

본 논문에서는 퍼지모델을 이용한 비선형 공정의 적응모델예측제어가 제안된다. 모델예측제어의 저긍구조는 순환 퍼지모델링을 통해 구현된다. 사용된 퍼지모델의 후건부가 입, 출력 변수의 선형식이기 때문에, 전체 공정의 모델을 구하고 이를 이용하여 미래 공정출력을 구한 후 비용함수를 최로로하는 제어법칙은 일반형 예측제어(GPC)와 같은 형태가 된다. 제안된 적응 퍼지모델 예측제어는 퍼지모델이 가지는 본래적인 비선형성으로 인해 비선형공정을 우수한 성능으로 제어한다. 공정제어입력의 변화량을 출력값으로 하는 적응 퍼지모델 예측제어(AFMPC)인 경우, 상수의 기준입력에 대해 정상상태가 없고 매우 우수한 성능을 보인다. 제안된 제어구조의 특성 및 성은 비선형 공정의 모의 실험에 의해 검증한다.

• 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.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권4호
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• pp.154-157
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• 2006

This paper is proposed hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design..of this algorithm based on fuzzy-neural network(FNN) 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 error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.138-138
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• 2000

In this paper, a methodology for designing a controller based on inverse dynamics for speed control of DC motors is presented. The proposed controller consists of a prefilter, the inverse dynamic model of a system and a fuzzy logic controller. The prefilter prevents high frequency effects from the inverse dynamic model. The model of the system is characterized by a nonlinear equation with coulomb friction. The fuzzy logic controller regulates the error between the set-point and the system output which may be caused by disturbances and it simultaneously traces the change o( the reference input. The parameters of the model are estimated by a genetic a]gorithm. An experimental work on a DC motor system is carried out to illustrate the performance of the proposed controller

• 전기학회논문지
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• 제66권1호
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• pp.159-164
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• 2017

In this paper, we propose a method of designing the sampled-data tracking controller for nonlinear systems expressed by the Takagi-Sugeno (T-S) fuzzy model. A sufficient condition that asymptotically stabilizes the state error between the linear reference model and the T-S fuzzy model is derived in terms of linear matrix inequalities. To this end, error dynamics are constructed, and the exact discretization method and the Lyapunov stability theory are employed in this paper. Finally, we validate the proposed method through the simulation example.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.660-662
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• 2004

This paper proposes an Intelligent Speed Estimator in order to realize the speed-sensorless vector control of an induction motor. Intelligent Speed Estimator used Model Reference Adaptive System which has Fuzzy-Neural adaptive mechanism as Speed Estimation method. The Intelligent Speed Estimator estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. The Intelligent Speed Estimator reduces the error of the rotor flux between the voltage flux model and the current flux model using the error and the change of error as input of the Estimator. The computer simulation is executed to verify the propriety and the effectiveness of the proposed speed estimator.