• Title/Summary/Keyword: control vibration

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A Study on the Adaptive Active Noise Control Using the Self-tuning feedback controller (자기동조 피이드백 제어기를 이용한 적응 능동소음제어에 관한 연구)

  • Shin, Joon;Lee, Tae-Yeon;Kim, Heung-Seob;Jo, Seong-Oh;Bang, Seung-Hyun;Oh, Jae-Eung
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 1993.04a
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    • pp.140-146
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    • 1993

  • Active noise control uses the intentional superposition of acoustic waves to create a destructive interference pattern such that a reduction of the unwanted sound occurs. In active noise control system the choice of a control structure and design of the controller are the main issues of concern. In real acoustic fields there are a vast number of noise sources with time-varying nature and the characteristics of transducers and the geometric set-up of control system are subject to change. Accordingly the control system should be designed to adapt such circumstances so that required level of performance is maintained. In this paper, the adaptive control algorithm for self-tuning adaptive controller is presented for the application in active noise control system. Self-tuning is a direct integration of identification and controller design algorithm in such a manner that the two processes proceed sequentially. The least mean square algorithm was used for the identification schemes and adaptive weighted minimum variance control algorithm was applied for self-tuning controller. Computer simulation results for self-tuning feedback controller are presented. And simulation results was shown to be useful for the situation in which the periodic noise sources act on the acoustic field.

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An Experimental Study on the Prediction Control Technique for a Magnetic Bearing (자기베어링 예측 제어 기법의 실험적 연구)

  • Kim, Chae Sil;Jung, Hoon Hyung;Shin, Min Jae
    • Journal of the Korean Society for Precision Engineering
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    • v.31 no.2
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    • pp.99-104
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    • 2014

  • Active vibration control methods are required in the high speed rotor systems supported by magnetic bearings. A prediction control technique is one of the control methods. Gain and phase angle are primarily chosen with analyzing the responses for a certain rotor speed. The feasibility of this technique has been reported for only analytical simulations. Therefore this paper constructs the test rig supported by ball bearings with a magnetic bearing type actuator and develops a prediction control system by using LabVIEW and Compact RIO. Finally as rotating speeds are modulated, the gains and phase angles for the speeds are determined with vibration control of the test rig. This leads that the prediction control technique may be applied to the rotor system with the magnetic bearing.

  • https://doi.org/10.7736/KSPE.2014.31.2.99 인용 PDF KSCI

Development of Active Intake Noise Control Algorithm for Improvement Control Performance under Rapid Acceleration and Disturbance (L-Point Running Average Filter를 이용한 급가속 흡기계의 능동소음제어 성능향상을 위한 알고리즘 개발)

  • 전기원;조용구;오재응;이정윤
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2004.05a
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    • pp.780-783
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    • 2004

  • Recently Intake noise has been extensively studied to reduce the engine noise. In order to diminish intake noise several resonators were added to the intake system. However this can cause a reduction of engine output power and an increase of fuel consumption. In this study, active noise control simulation of the Filtered-x LMS algorithm is applied real instrumentation intake noise data under rapid acceleration because intake noise is more excessively increased under the such a harsh condition. But the FXLMS algorithm has poor control performance when the system is disturbed. Thus modified FXLMS algorithm using L-point running average filter is developed to improve the control performance under the rapid acceleration and disturbance. The noise reduction quantity of modified Filtered-x LMS algorithm is more than original one in two cases. In the case of control for real instrumentation intake noise data, maximum residual noise of modified FXLMS algorithm is 2.5 times less than applied the FXLMS and also in the case of disturbed, the modified FXLMS algorithm shows excellent control performance but FXLMS algorithm cat not control.

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