• Journal of the Korean Society for Precision Engineering
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• v.22 no.6 s.171
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• pp.90-97
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• 2005

Magnetic flywheel system utilizes a magnetic bearing, which is able to support the shaft without mechanical contacts, and also it is able to control rotational vibration. Magnetic flywheel system is composed of position sensors, a digital controller, actuating amplifiers, an electromagnet and a flywheel. This work applies the neuro-fuzzy control algorithm to control the vibration of a magnetic flywheel system. It proposes the design skill of an optimal controller when the system has structured uncertainty and unstructured uncertainty, i.e. it has a difficulty in extracting the exact mathematical model. Inhibitory action of vibration was verified at the specified rotating speed. Unbalance response, a serious problem in rotating machinery, is improved by using a magnetic bearing with neuro-fuzzy algorithm.

• Journal of KSNVE
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• v.9 no.2
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• pp.265-272
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• 1999

A LMS algorithms has been widely used for an adaptive filter algorithm in active noise control systems. But this algorithm has poor convergence and it is very difficult to select optimal convergence parameters in this algorithm. In this paper, a fuzzy LMS algorithm where the convergence parameters are computed using a fuzzy logic controller was proposed. A proposed algorithm was applied to active noise control system in HVAC(central Heating Ventilation and Air Conditioning) ducts. The experimental ducts and experimental apparatus were designed and manufactured for experiments, and the modelling of the experimental ducts was also performed for computer simulations. Computer simulations and experiments were performed to show the effectiveness of a proposed algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.194-202
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• 2000

In this paper, an efficient fuzzy rule generation scheme for Adaptive Network-based Fuzzy Inference System(ANFIS) using the conditional fuzzy-means(CFCM) and fuzzy equalization(FE) methods is proposed. Usually, the number of fuzzy rules exponentially increases by applying the gird partitioning of the input space, in conventional ANFIS approaches. Therefore, CFCM method is adopted to render the clusters which represent the given input and output fuzzy and FE method is used to automatically construct the fuzzy membership functions. From this, one can systematically obtain a small size of fuzzy rules which shows satisfying performance for the given problems. Finally, we applied the proposed method to the truck backer-upper control and Box-Jenkins modeling problems and obtained a better performance than previous works.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.1
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• pp.17-32
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• 1992

The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manupulator.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.451-461
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• 2002

Differential evolution(DE) has been preyed to be an efficient method for optimizing real-valued multi-modal objective functions. DE's main assets are its conceptual simplicity and ease of use. However, the convergence properties are deeply dependent on the control parameters of DE. This paper proposes an adaptive differential evolution(ADE) method which combines with a variant of DE and an adaptive mechanism of the control parameters. ADE contributes to the robustness and the easy use of the DE without deteriorating the convergence. 12 optimization problems is considered to test ADE. As an application of ADE the paper presents a supervised clustering method for predicting events, what is called, an evolutionary event clustering(EEC). EEC is tested for 4 cases used widely for the validation of data modeling.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.452-461
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• 2007

Adaptive anti-sway and trajectory tracking control of overhead crane is presented, which utilizes Fuzzy Uncertainty Observer(FUO) and Fuzzy based Variable Structure Control(FVSC). We consider an overhead crane system which can be decoupled into the actuated and unactuated subsystems with its own lumped uncertainty such as parameter uncertainties and external disturbance. First, a new method for anti-sway control using FVSC is proposed to improve the conventional method based on Lyapunov direct method, while a conventional trajectory tracking control law using feedback linearization is directly adopted. Second, FUO is designed to estimate one of the two lumped uncertainties which can compensate both of them, based on the fact that two lumped uncertainties are coupled with each other. Then, an adaptive anti-sway control is proposed by incorporating the proposed FVSC and FUO. Under the condition that the observation error is Uniformly Ultimately Bounded(UUB) within an arbitrarily shrinkable region, the overall closed-loop system is shown to be Globally Uniformly Ultimately Bounded(GUUB). In addition, the Global Asymptotic Stability(GAS) of it is shown under the vanishing disturbance assumption. Finally, the effectiveness of the proposed scheme has been confirmed by numerical simulations.

• Journal of Korean Society of Transportation
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• v.14 no.3
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• pp.127-141
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• 1996

This paper presents a unique approach to urban traffic network signal control. This paper begins with an introduction to traffic control in general, and then goes on to describe the approach of fuzzy control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic network condition and adjacent intersection. The signal timing parameters evolve dynamically using only local information to improve traffic signal flow. The signal timing at an intersection is defined by three parameters : cycle time, phase split, off set. Fuzzy decision rules are used to adjust three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. In this paper the effectiveness of this method is showed through simulation of the traffic signal flow in a network of controlled intersection.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.131-134
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• 2002

일반적인 산업현장에서 많이 사용되는 이중탱크 시스템은 동작점 근방에서 선형화하는 고전제어기법을 사용한 것으로서 큰 시간지연과 비선형성으로 인해 정확한 수학적 모델링이 어렵고 모델링을 하더라도 넓은 동작 영역에서 만족스로운 결과를 얻기 어렵다. 따라서, 비교적 모델링에 대한 의존도가 낮은 퍼지, 신경회로망, 유전알고리즘 등의 지능제어 기법들도 제안되고 있다. 그러나 이들 제어기 역시 외란이나 다양한 동작 모드들에 따른 제어기 변수들의 적응성 저하로 인해 안정화 가능 영역이 협소해 지는 것은 물론 시스템의 불안정 현상도 초래한다. 이에 반해, SMC(sliding mode controller)는 변수의 변동, 외란에 둔감한 강점을 갖고 있지만, 시스템의 상태에 따른 슬라이딩 평면 설정의 곤란성과 채터링(chattering)이 존재하는 문제점 이 있다. 따라서 본 논문에서는 이중 탱크 시스템의 정밀한 수위 제어를 위하여, GA과 FLC를 사용하여 최적 변수로 설정 할 수 있게 하고, 채터링 저감을 위해 시스템 동특성 변동과 외란 에 강인한 GA-FSMC(genetic algorithm fuzzy sliding mode controller)를 제안하였다. 시뮬레이션을 통해 종래의 제어기의 제어결과와 비교함으로써 제안하는 GA-FSMC의 우수성을 입증하고자 한다.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.723-732
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• 2001

In this paper, the neurofuzzy controller of optically guided AGV is proposed to improve the path-tracking performance A differential steered AGV has front-side and rear-side optical sensors, which can identify the guiding path. Due to the discontinuity of measured data in optical sensors, optically guided AGVs break away easily from the guiding path and path-tracking performance is being degraded. Whenever the On/Off signals in the optical sensors are generated discontinuously, the motion errors can be measured and updated. After sensing, the variation of motion errors can be estimated continuously by the dead reckoning method according to left/right wheel angular velocity. We define the estimated contour error as the sum of the measured contour in the sensing error and the estimated variation of contour error after sensing. The neurofuzzy system consists of incorporating fuzzy controller and neural network. The center and width of fuzzy membership functions are adaptively adjusted by back-propagation learning to minimize th estimated contour error. The proposed control system can be compared with the traditional fuzzy control and decision system in their network structure and learning ability. The proposed control strategy is experience through simulated model to check the performance.

• Journal of Advanced Marine Engineering and Technology
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• v.19 no.3
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• pp.77-83
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• 1995

Recently, in the field of industrial servo-systems, several methods have been proposed for tracking the reference input fastly and finely without overshoot. These methods, however, are established under hypothesis that structure and parameters of the plant are known accurately and they are time invariant. In practice, it is difficult to obtain the values of plant's parameters accurately and usually plants change with time and operation conditions. In this paper a method to construct the nominal model tracking adaptive control system is proposed. The system is composed of the nomial model which produces a ideal response and the model tracking system with the fuzzy adaptive controller. If the actual plant is equal to the controlled object in the nominal model, the output of the plant is the same as that of the nominal model and the fuzzy adaptive controller becomes idle. However, when the plant changes, the fuzzy adaptive controller of the tracking system operates in order for the output of the plant to track the ideal response. Through the computer simulations under various conditions, it is confirmed that the proposed model tracking system is very effective.