Adaptive nonlinear ANC system based on time-domain signal reconstruction technology

• Published in: Mechanical systems and signal processing Vol. 162; p. 108056
• Main Authors: Yang, D.P., Song, D.F., Zeng, X.H., Wang, X.L., Zhang, X.M.
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 01.01.2022; Elsevier BV
• Subjects: Active noise control; Adaptive control; Adaptive nonlinear ANC system; Algorithms; Decomposition; Ear; Fault tolerance; Fault-tolerate and robustness; Feature extraction; Multi-network reconstruction model; Noise control; Noise signal decomposition; Passengers; Reference signals; Robustness; Signal reconstruction; VSS-LMS algorithm; Adaptive nonlinear ANC system; Noise signal decomposition; Fault-tolerate and robustness; Multi-network reconstruction model; VSS-LMS algorithm
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2021.108056

•A novel adaptive nonlinear ANC system based on time-domain signal reconstruction technology is proposed.•The noise signal is decomposed to reduce the degree of non-stationarity.•A multi-network reconstruction model is designed for reconstructing the noise signal of passenger ear-sides.•Results demonstrate that proposed ANC system can realise the fault-tolerant and robustness control. When the primary reference signal obtained by the existing Active noise control (ANC) system is not accurate, the control effect of interior noise will be reduced or even fail. Considering the fault-tolerate and robustness of the system, the study proposes an adaptive nonlinear ANC system for interior noise, which contains noise signal decomposition, multi-network reconstruction model and Variable step-size LMS (VSS-LMS) algorithm. The noise signal decomposition method is used to address the non-stationary of the interior noise; Based on the signal components, the multi-network model for the noise signal reconstruction of passenger ear-sides is designed, which is pre-trained by a restricted Boltzmann machine for improved reconstruction accuracy and realize the adaptive extraction of signal features; And then based on the reconstruction signal components, the controller weights of corresponding components are adaptively updated by the VSS-LMS algorithm to control the passenger ear-sides noise. The effectiveness of the proposed adaptive nonlinear ANC system is validated using noise signal sources collected from a vehicle. Compared with the different ANC systems, the proposed system is superior in terms of fault-tolerant and robustness, which can guarantee stable work of the interior noise control.