• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.994-997
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• 2003

This Paper presents a study of the performance of a DC servo motor with a model reference adaptive fuzzy speed controller (MRAFSC) in the presences of load disturbances. MRAFSC comprised inner feedback loop consisting of the fuzzy logic controller (FLC) and plant, and outer loop consisting of an adaptation mechanism which is designed for tuning a control rule of the FLC. Experimental results show the good performance in the DC servo motor system with the proposed adaptive fuzzy controller.

• 한국기계기술학회지
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• 제20권6호
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• pp.809-817
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• 2018

The model predictive controller performance of the mobile robot is set to an arbitrary value because it is difficult to select an accurate value with respect to the controller parameter. The general model predictive control uses a quadratic cost function to minimize the difference between the reference tracking error and the predicted trajectory error of the actual robot. In this study, we construct a predictive controller by transforming it into a quadratic programming problem considering velocity and acceleration constraints. The control parameters of the predictive controller, which determines the control performance of the mobile robot, are used a simple weighting matrix Q, R without the reference model matrix $A_r$ by applying a quadratic cost function from which the reference tracking error vector is removed. Therefore, we designed the predictive controller 1 and 2 of the mobile robot considering the constraints, and optimized the controller parameters of the predictive controller using a genetic algorithm with excellent optimization capability.

• 제어로봇시스템학회논문지
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• 제4권2호
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• pp.203-208
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• 1998

This paper presents an adaptive fuzzy controller using an neural network and adaptation algorithm. Reference-model following neuro-fuzzy controller(RMFNFC) is invesgated in order to overcome the difficulty of rule selecting and defects of the membership function in the general fuzzy logic controller(FLC). RMFNFC is developed to tune various parameter of the fuzzy controller which is used for the discrete nonlinear system control. RMFNFC is trained with the identification information and control closed loop error. A closed loop error is used for design criteria of a fuzzy controller which characterizes and quantize the control performance required in the overall control system. A control system is trained up the controller with the variation of the system obtained from the identifier and closed loop error. Numerical examples are presented to control of the discrete nonlinear system. Simulation results show the effectiveness of the proposed controller.

• 전력전자학회논문지
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• 제7권5호
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• pp.413-418
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• 2002

본 연구는 관성의 변화에 견실한 피드포워드 적응 위치제어기를 제안한다. 제안된 피드포워드 적응 위치제어기는 PI 위치제어기에 피드포워드와 기준모델 적응제어기를 추가한 구조를 가진다. 제어기의 특징은 과도상태에서 적응제어기가 위치오차와 기준모델의 속도 정보를 이용하여 피드포워드 이득을 조정한다. 이것은 과도응답 특성을 향상시키고, 정상상태의 수렴 시간을 개선시킨다. 위치 오차에 속도의 정보를 받아들여 기존의 Pl 위치제어 및 기준모델 적응제어에 비하여 속응성이 우수하다. 그리고, 적응제어기에 규준화 함수를 사용하여 입력크기에 대해 출력신호가 일정한 응답특성을 가지도록 하였다. 시뮬레이션을 통하여 제안된 피드포워드 적응 위치제어기 동작의 타당성을 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.471-476
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• 1994

In this paper a new controller is proposed which gives the resultant system the appointed input-output properties, low sensitivity and robust stability. The proposed controller consists of a reference model and a robust compensator. The reference model determines the input-output properties of the total system and is constructed by using the nominal model of the plant. We can design the reference model by applying design techniques which pay attention to steady robustness and no attention to sensitivity and robust stability, and need all state variables of the plant. The robust compensator is obtained as a solution of the mixed sensitivity problem in H infinity control theory. Therefore, low sensitivity and robust stability are guaranteed in the resultant system. The simulation experiments show that the proposed controller is effective and useful.

• 전력전자학회논문지
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• 제7권3호
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• pp.215-223
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• 2002

본 논문은 벡터로 제어되는 유도전동기 드라이브를 위하여 퍼지논리에 기초한 속도 및 자속제어기의 적응제어를 제시한다. 적응 메카니즘에서 제시된 모델기준 적응방법은 전동기의 속도와 기준모델의 출력 사이에서 측정한 오차와 오차의 변화에 의하여 퍼지논리를 수행한다. MIRAC(Model Reference Adaptive Control) 퍼지제어기는 다양한 동작조건을 위하여 시뮬레이션에 의해 평가한다. 제시한 MIRAC 퍼지제어기의 타당성은 유도전동기 드라이브 시스템에 적용하여 성능 결과로 입증한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.698-701
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• 1996

A novel neural network control scheme is proposed to identify the inverse dynamic model of robot manipulator and to compensate for uncertainties in robot dynamics. The proposed controller is called reference compensation technique(RCT) by compensating at reference input trajectory. The proposed RCT scheme has many benefits due to the differences in compensating position and learning algorithm. Since the compensation is done outside the plant it can be applied to many control systems without modifying the inside controller. It performs well with low controller gain because the operating range of input values is small and the output of the neural network controller is amplified through the controller gain. The back-propagation algorithm is used to train and simulations of three link robot manipulator are carried out to prove the proposed controller's performances.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 정기총회 및 추계학술대회 논문집 학회본부
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• pp.329-331
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• 1993

A new RMFSOC(Reference Model Following Self-Organizing Controller) is proposed. It is composed by adding the reference model and decision rule to the Mamdani's SOC. The reference model is introduced to explicitly specify the control performance. The self-organizing level of the RMFSOC organizes the control rule which makes the process output follow the reference output generated by the reference model. In order to avoid unnecessary control rule modification, a decision rule is also introduced to determine whether the control rule modification is needed or not.

• Journal of Advanced Marine Engineering and Technology
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• 제27권5호
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• pp.609-616
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• 2003

The purpose of this study is to design the adaptive speed control system of a marine diesel engine by combining the Model Matching Method and the Nominal Model Tracking Method. The authors proposed already a new method to determine efficiently the PID control Parameters by the Model Matching Method. typically taking a marine diesel engine as a non-oscillatory second-order system. But. actually it is very difficult to find out the exact model of a diesel engine. Therefore, when diesel engine model and actual diesel engine are unmatched as an another approach to promote the speed control characteristics of a marine diesel engine, this paper Proposes a Model Reference Adaptive Speed Control system of a diesel engine, in which PID control system for the model of a diesel engine is adopted as the nominal model and Fuzzy controller and derivative operator are adopted as the adaptive controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2064-2066
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• 2002

This paper proposes Model Reference Adaptive Fuzzy System(MRAFS) using Fuzzy Logic Controller(FLC) as a adaptive laws in Model Reference Adaptive System(MRAS) in order to realize the speed-sensorless control of an induction motor. MRAFS estimates the speed of an induction motor with a rotor flux of a reference model and 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 as the input of FLC. The computer simulation is executed to verify the propriety and the effectiveness of the proposed system.