• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.192-197
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• 2007

This paper presents Hybrid PI controller of IPMSM drive using fuzzy adaptive mechanism(FAM) control. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness, fixed gain PI controller, Hybrid PI controller proposes a new method based self tuning PI controller. Hybrid PI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.1-5
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• 2010

We propose an algorithm which can classify the system should use a PI controller, which have a weak high frequency attenuation characteristics near the critical frequency. To classify the system, we use a time delay element to calculate a gain attenuation rate near the critical frequency. The proposed algorithm also can design PI controller with the given magnitude margin and phase margin specification. The proposed algorithm uses time delay element and saturation function to identify the one point information in frequency domain. We justify the proposed algorithm via the simulation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.5
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• pp.25-31
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• 2007

This paper presents a comparison of different current controller response characteristics of SRM. The most common current controllers of the SRM is hysteresis type. The hysteresis controller is easy to implement and fast current control response, but has the inherent disadvantage of switching frequency variations. The other common type of current controller is PI scheme. The design of a classical PI current controller with fixed parameters for SRM is not an easy task due to the extreme nonlinear characteristics. In this paper, some linearization technique is used for design of PI current controller. Experimental results of 1-hp SRM are presented for the basic reference data which can be used to select the proper current control scheme according to the applications.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.2 s.314
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• pp.24-31
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• 2007

This paper presents self tuning PI(STPI) controller of IPMSM drive using neural network. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, STPI controller proposes a new method based neural network. STPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.488-493
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• 2008

A mathematical model of molten steel level for continuous casting process is presented, where the molten steel level, input and output flow in the mold, the relation between stopper position and input flow etc. are considered. The mathematical model is implemented and simulated by using MATLAB. Comparing the result of molten steel level from the simulator with that of real plant, the performance of the model is shown to be reasonable. By using this simulator, it is shown that PI controller with variable P gain, adjusted by fuzzy logic system, has better control result than conventional PI controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.3
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• pp.57-66
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• 2001

This paper resents a control approach for designing a fuzzy-PI controller for a synchronous generator excitation and SVC system A combination of thyristor-controlled reactors and fixed capacitors (TCR-FC) type SVC is recognized as having the must fiexible control and high speed response, which has been widely utilized in power systems, is considered and designed to improve the response of a synchronous generator, as well as controlling the system voltage A Fuzzy-PI controller for SVC system was proposed in this paper. The PI gain parameters of the proposed Fuzzy-PI controller which is a special type of PI ones are self-tuned by fuzzy inference technique. It is natural that the fuzzy inference technique should be barred on humans intuitions and empirical knowledge. Nonetheless, the conventional ones were not so. Therefore, In this paper, the fuzzy inference technique of PI gains using MMGM(Min Max Gravity Method) which is very similar to humans inference procedures, was presented and allied to the SVC system. The system dynamic responses are examined after applying all small disturbance condition.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.355-367
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• 2007

This paper proposes a design method of the PI(Proportional-Integral)+D(Derivative) controller using self-tuning of the design variables and controller gains. The used fuzzy PI+D controller is the approximated conventional continuos time linear PI+D controller and the used fuzzification method is the fuzzy single tone and the adapted defuzzification method is the simplified tenter of gravity. Fuzzy estimation result would be calculated in the other function elements from the classified fuzzy variables and the result determined by the design variables decides the controller gains. As a result, the proposed method shows the capability of the high speed tuning and can be applied to the case of input variables with many fuzzy partitions and also can bring out the advantage to reduce the reconstruction(digital sampling reconstruction) error. Most simulation results show that this controller makes much bettor efficiency and improvement by using design variables and controller gains.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.268-270
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• 2002

본 논문에서는 댐핑이 고려된 전력계통에서 동요시 안정도를 향상시킬 수 있는 확장된 PI제어를 이용한 조속기 제어기를 제안하였다. 전력계통에서의 동요는 대부분 고장기간 동안에 발전기에 저장된 과잉운동에너지에 의해 발생한다. 계통이 안정하도록 하기 위해서 과잉에너지를 효과적으로 다루려면 보통 직접법을 사용된다. 과잉에너지를 제어하기 위한 직접적인 방법으로는 LFC(Load Frequency Control)의 궤한이 활용된다. 확장된 PI제어 방식이 적용된 제어기는 지역내에서와 지역사이에서의 동요를 감쇄시키기 위한 조속기에 적용될 수 있다 또한 제안된 제어기는 조속기에 스팀밸브 제한 조건이나 발전율 제약조건(Generation Rate Constraint; GRC)과 같은 특이점이 존재할 때에도 우수한 성능을 보여주었다. 시뮬레이션은 두지역의 다기계통에서 부하를 변화시켜가며 실시하였다. 시뮬레이션 결과는 제안된 PI 제어기를 기존의 PI 제어기와 비교하여 매우 향상된 성능을 보여주었다.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2003.05a
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• pp.390-395
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• 2003

PMSM 드라이브의 벡터제어를 위한 속도제어기는 일반적으로 PI 알고리즘을 사용한다.［1］PI 알고리즘에서는 플랜트의 동적 특성을 동정하기 위하여 연속적인 측정이 가능한 플랜트 파라미터의 자동동조 또는 적응 알고리즘을 추가하여 사용하기도 한다. 그러나 PI 제어기는 플랜트의 변동이 발생하여 연속적으로 적응추종을 해야할 경우, 계산이 매우 복잡하고 응답특성이 저하된다.(중략)

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.11 no.2
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• pp.89-93
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• 1997

As industry gets rapidly automatic, D.C servo motor which is controlled by a PI controller needs accurate control. However, when a system has various characters, it is very difficult to guarantee its accuracy. In this paper, rule-based auto-tuning PI controller for motor speed control system is presented as a way of solving this problem. Some rules are based on Ziegler-Nichols step response and expert knowledge. Control parameters are determined by error, slope, steepest slope point, and permiSSIon overshoot. The accuracy of control is demonstrated by a computer s mulation .