Real-time implementation of a robust active control algorithm for narrowband signals suppression

This study presents a practical active noise control (ANC) algorithm with robust stability for reducing the powertrain noise or vibration inside a car. It is important to ensure that a practical ANC system for a car is robustly stable to variations or uncertainties in the actual plant. After investigating the robust stability condition of the ANC algorithm, a robust plant model is designed by considering the multiplicative plant uncertainties within given bounds such as closing or opening door windows. The ANC algorithm was implemented in a dSPACE DS1401 as a control platform, and an error microphone and a subwoofer as a secondary source were positioned at the driver’s left ear and the trunk of the experimental car, respectively. The engine rpm information received from the controller area network of the car was used for the generation of relevant reference signals. The real-time control experiments were carried out against the plant perturbation when the engine was either idling or sweeping in the neutral mode. The results showed that the robust control algorithm can suppress the noise whether the actual plant was nominal or perturbed with the stability over the rpm.

[1]  Philip A. Nelson,et al.  Effect of errors in the plant model on the performance of algorithms for adaptive feedforward control , 1991 .

[2]  Jonathan D. Blotter,et al.  Eigenvalue equalization filtered-x algorithm for the multichannel active noise control of stationary and nonstationary signals. , 2008, The Journal of the Acoustical Society of America.

[3]  Ali H. Sayed,et al.  Robust FxLMS algorithms with improved convergence performance , 1998, IEEE Trans. Speech Audio Process..

[4]  Stephen J. Elliott,et al.  Signal Processing for Active Control , 2000 .

[5]  Arthur P. Berkhoff,et al.  A technique for improved stability of adaptive feedforward controllers without detailed uncertainty measurements , 2011 .

[6]  Sen M. Kuo,et al.  Active noise control: a tutorial review , 1999, Proc. IEEE.

[7]  Evanghelos Zafiriou,et al.  Robust process control , 1987 .

[8]  Sen M. Kuo,et al.  Active Noise Control Systems: Algorithms and DSP Implementations , 1996 .

[9]  Michel Verhaegen,et al.  Robustness of the Filtered-X LMS Algorithm— Part II: Robustness Enhancement by Minimal Regularization for Norm Bounded Uncertainty , 2007, IEEE Transactions on Signal Processing.

[10]  Yunseon Choi,et al.  Length Variation Effect of the Impulse Response Model of a Secondary Path in Embedded Control , 2016, J. Sensors.

[11]  Wei Ren,et al.  Convergence analysis of the multi-variable filtered-X LMS algorithm with application to active noise control , 1999, IEEE Trans. Signal Process..

[12]  Michel Verhaegen,et al.  Robustness of the Filtered-X LMS Algorithm— Part I: Necessary Conditions for Convergence and the Asymptotic Pseudospectrum of Toeplitz Matrices , 2007, IEEE Transactions on Signal Processing.