• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 D
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• pp.2033-2034
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• 2006

In this paper, we applied genetic algorithm to current control for robust control and stability. In conclusion, we prove that current control of genetic algorithm can be high efficiency.

• 수산해양기술연구
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• 제28권3호
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• pp.262-273
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• 1992

In this study, we selected a DC servo motor as an actuator of the marine diesel engine governor and constructed the position control system of the DC servo motor using the algorithm proposed by authors. Next, we proposed an another method to construct an adaptive control system for marine diesel engine by regarding the controlled system including the DC servo motor as a second order controlled system and verified the validity of this method through the real time control responses. Finally, the results have shown a good response characteristic.

• 한국지능시스템학회논문지
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• 제6권3호
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• pp.40-50
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• 1996

By using the neural networks(NN) as system identifier, the on-line self tuning method for fuzzy controller(FC) is proposed. In theis method, the learning of NN is carried out during control operation of FC and the cinsequent parameters of FC is tuned on-line automatically by means of system output errors backpropagated through NN. The Sugeno fuzzy model with constants as consequent parameters is selected for simplifying computation. In procedures of parameter tuning, the gradient descent method is used and the gradient vectors for adjusting the weight of NN are transferred as controller output errors. To evaluate the performance, the proposed method is applied to the inverted pendulum system.

• 한국지능시스템학회논문지
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• 제12권3호
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• pp.261-268
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• 2002

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.

• 제어로봇시스템학회논문지
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• 제6권1호
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• pp.8-14
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• 2000

In this paper, three adaptive sliding mode control algorithms, which self-tune both the sliding mode gain and the boundary layer thickness, are proposed. The first algorithm uses a gain adaptation rule is combined with the boundary layer thickness adaptatioin rule to satisfy the sliding condition. In the third algorithm, the computation burden of the second algorithm is reduced further, and therefore no extra cost is required for real-time implementation. Due to the mixed sliding mode gain and the boundary layer thickness adaptation scheme, the tracking error and the chattering of the control input can be reduced greatly.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.83-85
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• 1997

In this paper, a self-tunning fuzzy inference technique for stabilization of the inverted pendulum system is proposed. The facility of this self-tunning fuzzy controller which has swing-up control mode and a stabilization one, moves a pendulum in an initial natural stable equilibrium point and a cart in arbitrary position, to an unstable equilibrium point and a center of rail. Specially, the virtual equilibrium point(${\phi}_{VEq}$) which describes functionally considers the interactive dynamics between a position of cart and a angle of inverted pendulum is introduced. And comparing with the convention optimal controller, the proposed self-tunning fuzzy inference structure made substantially the inverted pendulum system robust and stable.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.825-829
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• 2005

This paper presents an approach for designing a fuzzy logic-based adaptive SVC damping In controller for damping low frequency power oscillations. Power systems are often subject to low Frequency electro-mechanical oscillations resulting from electrical disturbances. Generally, power system stabilizers are designed to provide damping against this kind of oscillations. Another means to achieve damping is to design supplementary damping controllers that are equipped with SVC. Various approaches are available for designing such controllers, many of which are based on the concepts of damping torque and others which treat the damping controller design as a generic control problem and apply various control theories on it. In our proposed approach, linear optimal controllers are designed and then a fuzzy logic tuning mechanism is constructed to generate a single control signal. The controller uses the system operating condition and a fuzzy logic signal tuner to blend the control signals generated by two linear controllers, which are designed using an optimal control method. First, we design damping controllers for the two extreme conditions; the control action for intermediate conditions is determined by the fuzzy logic tuner. The more the operating condition belongs to one of the two fuzzy sets, the stronger the contribution of the control signal from that set in the output signal. Simulation studies done on a one-machine infinite-bus and a four-machine two-area test system, show that the proposed fuzzy adaptive damping SVC controller effectively enhances the damping of low frequency oscillations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.128-130
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• 2008

This paper presents an algorithm to design a self-tuning proportional-integral(PI) speed controller for servo systems. The control gains are calculated with estimated system parameters, i.e. inertia and viscous damping which are estimated by initial operation. The simulation and experimental results show the feasibility and performance of the proposed algorithm.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1999년도 추계학술대회논문집
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• pp.363-369
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• 1999

In these days, there are a lot of studies in the port automation, for example, unmanned container trasporter, unmanned gantry crain, and automatic terminal operation systems and so on. In terms of loading and unloading equipments. we can consider container transporter. This paper describes the automatic control for the UCT(unmanned container transporter), especially steering control systems. UCT is now operated on ECT port in Netherland and tested on PSA ports in Singapore. So we present a design on the controller using neural network PID(NNPID) controller to control the steering system and we use the neural network self-tuner to tune the PID parameters. The computer simulations show that our proposed controller has better performances than those of the other.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1655-1658
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• 1991

This paper considers the design and analysis of one-step ahead optimal and adaptive controllers, under the restriction that a known constraint on the input amplitude is imposed. It is assumed that the discrete-time single-input, single-output system to be controlled is linear, except for inequality constraints on the input. The objective function to be minimized is an one-step quadratic function, where polynomial weights on the input and output are included. Both the known parameter and unknown parameter (indirect adaptive controller) cases are examined.