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

• Published in: IEEE transactions on instrumentation and measurement Vol. 61; no. 2; pp. 554 - 563
• Main Authors: Rout, N. K., Das, D. P., Panda, G.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic noise; Active noise control; Active noise control (ANC); adaptive filtering; Algorithms; conditional reinitialized PSO (CRPSO); Convergence; Equations; Evolutionary; Instrumentation; Least mean squares; Loudspeakers; Mathematical model; Microphones; Noise; Noise control; Optimization; particle swarm optimization (PSO); Studies
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2011.2169180

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.