Real-time implementation of a robust active control algorithm for narrowband signals suppression
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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.
KeywordsRobust control algorithm Active noise suppression Perturbed plant Robust stability Vibration
This work was supported by the Incheon National University Research Grant in 2014.
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Conflict of interest
All Authors declares that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Berkhoff AP (2012) A technique for improved stability of adaptive feedforward controllers without detailed uncertainty measurements. Smart Mater Struct 21(6):11, Article ID 064003Google Scholar
- Boucher CC, Elliott SJ, Nelson PA (1991) Effect of errors in the plant model on the performance of algorithms for adaptive feedforward control. Proc Inst Electr Eng F 138(4):313–319Google Scholar
- Elliott SJ (2001) Signal processing for active control. Academic Press, New YorkGoogle Scholar
- Elliott SJ, Stothers IM, Nelson PA, McDonald AM, Quinn DC, Saunders T (1988) The active control of engine noise inside cars. In: INTER-NOISE and NOISE-CON Congress and Conference Proceedings (InterNoise ’88), vol 1998, no 3, pp 987–990Google Scholar
- Kuo SM, Morgan DR (1996) Active noise control systems: algorithms and DSP implementations. Wiley, New YorkGoogle Scholar
- Lee Y-S, Choi Y, Kim J (2016) Length variation effect of the impulse response model of a secondary path in embedded control. J Sens 2016:7, Article ID 8270121Google Scholar
- Ren W, Kumar PR (1989) Adaptive active noise control: structures, algorithms and convergence analysis. In: Proceedings of the engineering for environmental noise control (INTER-NOISE’89), pp 435–440Google Scholar
- Rupp M, Sayed AH (1998) Robust FxLMS algorithms with improved convergence performance. IEEE Trans Signal Process 6(1):78–85Google Scholar