Model-free iterative learning control for repetitive impulsive noise using FFT

Authors:
Yali Zhou;Yixin Yin;Qizhi Zhang;Woonseng Gan
Affiliations:
School of Automation, University of Science and Technology Beijing, Beijing, China,School of Automation, Beijing Information Science and Technology University, China;School of Automation, University of Science and Technology Beijing, Beijing, China;School of Automation, Beijing Information Science and Technology University, China;School of EEE, Nanyang Technological University, Singapore
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Year:
2012

Citing 3
Cited 0

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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.