전기학회논문지 (The Transactions of The Korean Institute of Electrical Engineers)

  • 제59권10호
  • /
  • Pages.1900-1907
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  • 2010
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  • 1975-8359(pISSN)
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  • 2287-4364(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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RCMAC 및 PSO 기법을 이용한 능동 소음제어 시스템 성능 개선

Performance Improvement of the Active Noise Control System Using RCMAC and PSO Method

  • 투고 : 2010.07.26
  • 심사 : 2010.09.08
  • 발행 : 2010.10.01
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초록

In this paper, a recurrent cerebellar modulation articulation control with praticle swarm optimization (PSO) method has been investigated for improvement of noise attenuation performance in active noise control system. For narrow band noise, FXLMS and RCMAC has a partial satisfactory noise attenuation. However, noise attenuation performance is poor for broad band noise and nonlinear path since it has linear filter structure. To improve this problem, a RCMAC with PSO is proposed and it is shown that satisfactory noise attenuation performance is obtained by some simulations in duct system using harmonic motor noise and KTX cabin noise as a noise source.

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참고문헌

  1. S.J.Elliot and P.A.Nelson, "Active Noise Control," IEEE Sign. Process Mag., vol. 10, pp. 12-35, 1993. https://doi.org/10.1109/79.248551
  2. S.M. Kuo and D.R.Morgan, "Active Noise Control: a Tutorial Review," IEEE Proc. 87, pp. 973-993, 1999.
  3. X.J.Qiu and C.H.Hansen, "A Modified Filtered-x LMS Algorithm for Active Control of Periodic Noise with On-line Cancellation Path Modeling," J. of Low Frequency Noise, Vib. and Active Contr., vol. 19, pp. 35-46, 2000. https://doi.org/10.1260/0263092001492787
  4. M.Zhang, J.Lan and W.Ser, "A Robust Online Secondary Path Modeling Method with Auxiliary Noise Power Scheduling Strategy and Norm Constraint Manipulation," IEEE Trans. on Speech and Audio Proc., vol. 11, no. 1, pp. 45-53, 2003. https://doi.org/10.1109/TSA.2003.805643
  5. Y,Maeda and T.Yoshida, "An Active Noise Control without Estimation of Secondary Path-ANC Using Simultaneous Perturbation," Active 1999, Florida, USA, pp. 985-994, 1999.
  6. J.M.Souza, C.A.Silva and J.M.G.Sa da Costa, "Fuzzy Active Noise Modeling and Control," Inter. J. of Approximate Reason., vol. 33, pp. 51-70, 2003. https://doi.org/10.1016/S0888-613X(02)00147-0
  7. Z. Qizhi and J.Yongle, "Active Noise Hybrid Feedforward/Feedback Control Using Neural Network Compensation," Trans. ASME, vol. 124, January, pp. 100-104, 2002.
  8. C.C.Ku and K.Y.Lee, "Diagonal Recurrent Neural Networks for Dynamic Systems Control," IEEE Trans. on Neural Networks, vol. 6, No. 1, pp. 144-156, 1995. https://doi.org/10.1109/72.363441
  9. P. Campolucci, P. Uncini, F. Piazza and B.D.Rao, "On-line Learning Algorithm for Locally Recurrent Neural Networks," IEEE Trans. on Neural Networks, vol. 10, no. 2, pp. 340-355, 1999. https://doi.org/10.1109/72.750564
  10. J.S. Albus, "A new approach to manipulator control: The cerebellar model articulation controller (CMAC)," Trans. ASME J. Dyn. Syst. Meas. Contr., vol. 97, no. 3, pp. 220-227, 1975. https://doi.org/10.1115/1.3426922
  11. Y.F. Peng and C.M. Lin, "Intelligent hybrid control for uncertain nonlinear using a recurrent cerebellar model articulation controller," IET Contr. Theory Appl., vol. 151, no. 5, pp. 589-600, 2004. https://doi.org/10.1049/ip-cta:20040903
  12. M. Cler and J. Kennedy, "The particle swarm - explosion, stability, and convergence in a multidimensional complex space," IEEE Evolutionary Comp., vol. 6, no. 1, pp. 58-63, 2002. https://doi.org/10.1109/4235.985692