Adaptive variable step-size neural controller for nonlinear feedback active noise control systems

Nguyen Le Thai, Xing Wu, Jing Na, Yu Guo, N.T. Trung Tin, Phan Xuan Le

Research output: Contribution to journalArticle (Academic Journal)peer-review

16 Citations (Scopus)
363 Downloads (Pure)


Abstract Adaptive filter techniques and the filtered-x least mean square (FxLMS) algorithm have been used in Active Noise Control (ANC) systems. However, their effectiveness may degrade due to the nonlinearities and modeling errors in the system. In this paper, a new feedback ANC system with an adaptive neural controller and variable step-size learning parameters (VSSP) is proposed to improve the performance. A nonlinear adaptive controller with the FxLMS algorithm is first designed to replace the traditional adaptive FIR filter; then, a variable step-size learning method is developed for online updating the controller parameters. The proposed control is implemented without any offline learning phase, while faster convergence and better noise elimination can be achieved. The main contribution is that we show how to analyze the stability of the proposed closed-loop ANC systems, and prove the convergence of the presented adaptations. Moreover, the computational complexities of different methods are compared. Comparative simulation results demonstrate the validity of the proposed methods for attenuating different noise sources transferred via nonlinear paths, and show the improved performance over classical methods.
Original languageEnglish
Pages (from-to)337-347
Number of pages11
JournalApplied Acoustics
Early online date17 Oct 2016
Publication statusPublished - 15 Jan 2017


  • Active noise control
  • Filtered-x least-mean-square (FxLMS) method
  • Variable step-size learning
  • Neural network
  • Nonlinear path


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