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Particle Swarm Optimization Based Active Noise Control Algorithm Without Secondary Path Identification

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3 Author(s)
Nirmal Kumar Rout ; School of Electronics Engineering, Kalinga Institute of Industrial Technology University, Bhubaneswar, India ; Debi Prasad Das ; Ganapati Panda

In this paper, particle swarm optimization (PSO) algorithm, which is a nongradient but simple evolutionary computing-type algorithm, is proposed for developing an efficient active noise control (ANC) system. The ANC is conventionally used to control low-frequency acoustic noise by employing a gradient-optimization-based filtered-X least mean square (FXLMS) algorithm. Hence, there is a possibility that the performance of the ANC may be trapped by local minima problem. In addition, the conventional FXLMS algorithm needs prior identification of the secondary path. The proposed PSO-based ANC algorithm does not require the estimation of secondary path transfer function unlike FXLMS algorithm and, hence, is immune to time-varying nature of the secondary path. In this investigation, a small modification is incorporated in the conventional PSO algorithm to develop a conditional reinitialized PSO algorithm to suit to the time-varying plants of the ANC system. Systematic computer simulation studies are carried out to evaluate the performance of the new PSO-based ANC algorithm.

Published in:

IEEE Transactions on Instrumentation and Measurement  (Volume:61 ,  Issue: 2 )