• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 춘계종합학술대회
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• pp.713-716
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• 2007

In this paper, we consider one of robust control system, fuzzy PI+fuzzy D controller dealing with noise, load, changed parameters of plant. We apply PI+D controller with a design for output of differential function and, we plan fuzzy controller with input for PID parameter of PI+D controller so We design control system meet with the change of environment with robust in relation to change of parameter. Fuzzy control is possessed of easy 4 rules and membership function and We design fuzzy PI+fuzzy D controller. Plant of this paper make a choice of 3 phase induction motor.

• Journal of Advanced Marine Engineering and Technology
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• 제28권2호
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• pp.228-236
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• 2004

In this paper. we present a neuro-fuzzy controller which unifies both fuzzy logic and multi-layered feed forward neural networks. Fuzzy logic provides a means for converting linguistic control knowledge into control actions. On the other hand. feed forward neural networks provide salient features. such as learning and parallelism. In the proposed neuro-fuzzy controller. the parameters of membership functions in the antecedent part of fuzzy inference rules are identified by using the error back propagation algorithm as a learning rule. while the coefficients of the linear combination of input variables in the consequent part are determined by using the least square estimation method. Finally. the effectiveness of the proposed controller is verified through computer simulation for an inverted pole system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 A
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• pp.140-142
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• 2001

Three single loop fuzzy logic controllers are designed independently for the control of boiler system of fossil-power plant. The control rules and the membership functions of proposed fuzzy logic control system are generated automatically without using plant model. The simulation shows successful results for wide range operation of boiler system of fossil-power plant.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.724-727
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• 1999

In this paper, the direct and indirect neural adaptive controller are combined based on the Lyapunov synthesis approach. The proposed adaptive controller is constructed from RBF neural network and a set of fuzzy IF-THEN rules. And the weighting parameters are adjusted on-line according to some adaptation law for the purpose of controlling the plant to track a given trajectory. In this scheme, fuzzy IF-THEN rules are used to decide the combined weighting factor. It is shown that all the signals in the closed-loop system are uniformly bounded under mild assumptions. The effectiveness of the proposed control scheme is demonstrated through the control of one-link rigid robotics manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.382-386
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• 1992

In this paper, the author proposed FLVSC(Fuzzy Logic Variable Structure Controller), of which control rules are extracted from the concepts of VSC(Variable Structure Control). FLC(Fuzzy Logic Controller) based on linguistic rules has the advantages of not needing of some exact mathematical model for plant to be controlled. The proposed method has the characteristics which are viewed in conventional VSC, e.g. insensitivity to a class of disturbances, parameter variations and uncertainties in sliding mode. In addition, the method has the properties of FLC - noise rejection capability etc. The computer simulations have been carried out for a DC servo motor to show the usefulness of the proposed method and the effects of disturbances and parameter variations are considered.

• Journal of Electrical Engineering and information Science
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• 제3권2호
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• pp.131-138
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• 1998

We derive a back-propagation learning algorithm of fuzzy neural networks using fuzzy operations, which preserves the shapes of fuzzy numbers, in order to utilize fuzzy if-then rules as well as numerical data in the learning of neural networks for classification problems and for fuzzy control problems. By introducing the shape preseving fuzzy operation into a neural network, the proposed network simplifies fuzzy arithmetic operations of fuzzy numbers with exact result in learning the network. And we illustrate our approach by computer simulations on numerical examples.

• Journal of Advanced Marine Engineering and Technology
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• 제34권1호
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• pp.109-115
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• 2010

본 논문은 퍼지 제어를 통한 이동용 로봇에 대한 추적 제어에 대해서 다루고 있다. 이동용 로봇은 Mamdani 형식의 퍼지 제어기로 제어 된다. 퍼지 제어기에 있어서 이동용 로봇과 목표 사이의 각도와 각도의 변화율이 입력으로 사용되고, 출력으로 조향각이 추론되어 제어입력이 된다. 퍼지 규칙은 전문가의 경험적 지식을 참조하여 7개를 사용하였다. 또한 퍼지 제어기 설계에 있어서 하나의 관심 분야인 환산계수(scaling factor) 조정 방법에 대해 제안한다. 본 논문에서는 이를 위해 파라미터 최적화 분야에서 잘 알려진 실수코딩 유전알고리즘을 적용하였다. 시뮬레이션을 통해 다양한 초기 조향각이 주어진 경우, 목표치에 대한 추적 안정성을 보여 퍼지 제어기의 유효성이 확인된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.103-106
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• 1996

This paper presents an iterative fuzzy learning control scheme which is applicable to a broad class of nonlinear systems. The control scheme achieves system stability and boundedness by using the linear feedback plus adaptive fuzzy controller and achieves precise tracking by using the iterative learning rules. The switching mode control unit is added to the adaptive fuzzy controller in order to compensate for the error that has been inevitably introduced from the fuzzy approximation of the nonlinear part. It also obviates any supervisory control action in the adaptive fuzzy controller which normally requires high gain signal. The learning control algorithm obviates any output derivative terms which are vulnerable to noise.

• 한국산업융합학회 논문집
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• 제2권1호
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• pp.61-67
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• 1999

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. And We designed controller by fuzzy learning method, and then compare fuzzy learning method with LQR. The result of simulations shows that the crane is controlled better than LQR for a very large swing angle of 1 radian within nearly one cycle.

• 설비공학논문집
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• 제13권9호
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• pp.810-817
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• 2001

Performance of a water-to-water multi-type heat pump system using R22 which has tow indoor units has been investigated experimentally. The refrigerant flow rate of each indoor unit was regulated by an electronic expansion valve and the total refrigerant flow rate of the system was controlled by a variable speed compressor. In the system, evaporator outlet pressure of refrigerant and outlet temperatures of secondary fluid from indoor units were selected as control variables. Experiments were executed for both cooling and heating modes using PID control method with fuzzy logic, and results of the test are compared with a classical PID method. In the case of PID control with fuzzy logic, the fuzzy control rules corrects PID parameters each time. Results show that PID control with fuzzy logic has the merits of quick response and reduced overshoot.