• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.2
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• pp.139-145
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• 2014

Periodic disturbance compensators are widely used in track following servo systems. They are commonly designed and implemented by adaptive feedforward compensators or internal model based compensators. In track following servo systems, the gains of the compensators should be determined considering the change of the sensitivity transfer function and the implementation methods should be selected considering the system environment. This paper proposes a guide for determining gains of the periodic disturbance compensators. Various simulation and experimental results are presented to see the effect of gains. In addition, this paper introduces the various types of implementation methods and compares their merits and demerits.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1813-1814
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• 2008

본 논문에서는 생물학적 운동 메카니즘을 유사하게 구현하기 위해 신경 진동자를 이용한 로봇 팔 제어 시스템을 제안한다. 인간 및 동물의 주기적인 자율 운동을 관장하는 Central Pattern Generator (CPG)를 수학적으로 모델링한 신경 진동자는 그 중요 특성의 하나인 entrainment 효과를 보여준다. 일반적으로 우리는 이 기능을 이용하여 미지의 외부 환경 변화와 같은 외란에 적절히 상호 작용할 수 있는 운동을 생성해 낼 수 있다. 이러한 결과를 보이기 위해, 각 관절에 가상의 신경 진동자 모델을 결합하였고 외부 환경의 변화나 외란의 감지를 위한 F/T센서를 팔의 말단에 부착하여 시스템을 구현하였다. 신경 진동자 모델을 결합한 2축 링크 로봇 팔 시스템(real time)은 주어진 목적운동을 (원 운동) 수행함과 동시에 미지의 외부 환경의 변화(임의의 벽)를 인지하여 적절한 모션을 생성하는 지를 살펴본다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.1
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• pp.33-39
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• 2007

LonWorks over IP (LonWorks/IP) virtual device network (VDN) is an integrated form of LonWorks device network and IP data network. LonWorks/IP VDN can offer ubiquitous access to the information on the factory floor and make it possible for the predictive and preventive maintenance on the factory floor. Timely response is inevitable for predictive and preventive maintenance on the factory floor under the real-time distributed control. The network induced uncertain time delay deteriorates the performance and stability of the real-time distributed control system on LonWorks/IP virtual device network. Therefore, in order to guarantee the stability and to improve the performance of the networked distributed control system the time-varying uncertain time delay needs to be compensated for. In this paper, under the real-time distributed control on LonWorks/IP VDN with uncertain time delay, a control scheme based on disturbance observer and ZPETC(Zero Phase Error Tracking Controller) phase lag compensator is proposed and tested through computer simulation. The result of the proposed control is compared with that of internal model controller (IMC) based on Smith predictor and disturbance observer. It is shown that the proposed control scheme is disturbance and noise tolerant and can significantly improve the stability and the tracking performance of the periodic reference. Therefore, the proposed control scheme is well suited for the distributed servo control for predictive maintenance on LonWorks/IP-based virtual device network with time-varying delay.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1789-1790
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• 2007

이 논문에서 우리는 생체모방 시스템을 구현하기 위해 일반적인 기계 시스템과 인간의 신경 진동자 모델을 결합하였다. 이러한 시스템은 외부환경의 변화에 따른 효과적인 자율 적응 운동 형태를 생성할 수 있다. 인간 및 동물의 주기적 자율 운동을 관장하는 Central Pattern Generator (CPG)는 신경 진동자 네트웍에 의해서 표현가능하고 이는 신경 진동자 모델 내부의 sensory 피드백 신호를 통해, 주기성을 같은 외란에 상호 작용하여 적절한 운동을 생성해 낸다. 따라서 이를 기계 시스템에 결합하면 이러한 시스템은 변화되는 환경이나 잘 알지 못하는 외란에 대하여 자율적으로 적응된 운동을 보일 수 있다. 이를 위해 본 논문은 이러한 신경 진동자 모델과 결합된 realtime 시스템을 구현하고 그 자율 적응 운동의 생성 가능성을 살펴본다.

• Proceedings of the KIPE Conference
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• 2017.11a
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• pp.195-196
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• 2017

This paper proposes a permanent magnet synchronous motor(PMSM) control using a repetitive controller. Repetitive controllers are often used to eliminate periodic disturbances. Based on this, it is possible to alleviate the torque disturbance occurring at a constant cycle to the load of the motor. This paper proposes a method of rejecting periodically generated disturbances by using a repetitive controller and verified by simulation.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.2
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• pp.136-143
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• 2004

This paper presents three algorithms of repetitive controller for compensation of the repeatable runout in hard disk drive. The basic theory of the repetitive controller and the analysis of the disturbance in hard disk drive are introduced. The tracking controller is designed in order to design the "plug-in type" repetitive controller. Design of the repetitive controller is considered as the design of the filter, determination of gain and design of additional compensation for the various types. Specially, trade-off relationship between stability and performance is important for the design. The three kinds of "plug-in type" repetitive controllers are designed, simulated and experimented. Performance and characteristic of them are compared with the analysis of the experimental results.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.1083-1088
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• 2003

This paper presents three algorithms of repetitive controller for compensation of the repeatable uncut in hard disk drive. The basic theory of the repetitive controller and the analysis of the disturbance in hard disk drive are introduced. The tracking controller is designed in order to design the "plug-in type" repetitive controller. Design of the repetitive controller is considered as the design of the filter, determination of Gain and design of additional compensation for the various types. Specially, trade-off relationship between stability and performance is important factor for the design. The thee kinds of "plug-in type" repetitive controllers are designed simulated and experimented. Performance and characteristic of them are compared by the analysis of the experimental results

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1326-1329
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• 1997

In designing the controller by changing the weighting matrix for the pirpose of satisfying constraints, the physical meaning of weighting matrix may disapperar and the system may not yield best performance because operation condition such as periodic disturbance was not considered. In this paper, the weighting matrix is fixed and controller is designed to minimize the new performance index to reduce the effects of periodic-type disturbances. This method is applied to design the satellite controller to verify the effetiveness.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.24-27
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• 1997

A periodic disturbance canceler is proposed to compensate the periodic disturbance due to cutting process in a CNC machining center. For precision cutting, the combination of a disturbance observer and a periodic disturbance canceler is desirable in order to compensate both the frictional force and the periodic disturbance. This method is implemented in a position control system of a CNC machining center in cutting process and the experimental results are described to show its effectiveness.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.115-126
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• 2023

Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.