Abstract
In this paper, active control of repetitive impulsive noise is studied. A novel model-free iterative learning control (MFILC) algorithm based on FFT is used for an active noise control (ANC) system with an unknown or time-varying secondary path. Unlike the model-based method, the controller design only depends on the measured input and output data without any prior knowledge of the plant model. Computer simulations have been carried out to validate the effectiveness of the presented algorithm. Simulation results show that the proposed scheme can significantly reduce the impulsive noise and is more robust to secondary path changes.
This work is supported by NSFC Grant #11172047 and PHR(IHLB) Grant #PHR201106131.
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Zhou, Y., Yin, Y., Zhang, Q., Gan, W. (2012). Model-Free Iterative Learning Control for Repetitive Impulsive Noise Using FFT. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_51
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DOI: https://doi.org/10.1007/978-3-642-31362-2_51
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