Optimal parametric design of delayless subband active noise control system based on genetic algorithm optimization

Guo Long, Yawen Wang, Teik C. Lim

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Active noise control systems are generally application-specific, and an appropriate algorithm with an optimal configuration is desirable in the first stage of active noise control system design and deployment. This study presents a design of the subband active noise control system with optimal parameters for a practical broadband active noise control. Although the delayless subband active noise control has gained wide attention for broadband noise cancellation, an optimal design remains a challenge because of the complex interplay between multiple factors such as spectral leakage, delay and weight stacking distortion subject to a number of configurable parameters, and weight stacking method. The configurable parameters can hardly be optimized independently because the active noise control performance depends on the combined configuration. A simple near black box active noise control algorithm optimization model is thus established by incorporating the genetic algorithm optimization into the parametric design of the delayless subband algorithm. The automated process does not require an understanding of the performance characteristics for different parameters. The significance of applying the automated genetic algorithm optimization to the complex multiparameter nonlinear active noise control design is revealed by numerical simulations, particularly for the multichannel low-frequency broadband active noise control system configured with the delayless subband algorithms. This provides a way for the optimal parametric design of subband active noise control before being used in a practical complex scenario.

Original languageEnglish (US)
Pages (from-to)1950-1961
Number of pages12
JournalJVC/Journal of Vibration and Control
Issue number15-16
StatePublished - Aug 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • Automotive Engineering
  • Aerospace Engineering
  • Mechanics of Materials
  • Mechanical Engineering


  • Active noise control
  • genetic algorithm
  • optimal
  • parametric design
  • subband algorithm


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