The engineering implementation of the multi-channel active noise control (MCANC) system for turboprop aircraft cabin is seriously hampered by its enormous computational complexity. This paper proposes the variable-P-s...
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The engineering implementation of the multi-channel active noise control (MCANC) system for turboprop aircraft cabin is seriously hampered by its enormous computational complexity. This paper proposes the variable-P-sequential-partial-update filtered-x least mean square (VP-SPUfxlms) algorithm, which achieves noise reduction performance comparable to that of the multi-channelfxlms (MCfxlms) algorithm while significantly reducing the computational complexity. Additionally, considering the time-varying nature of the secondary paths in practical applications, the Eriksson online secondary path modeling (OSPM) method is extended from single-channel to multi-channel, the problems that may be faced when the method is applied to MCANC systems are analyzed, and an improved alternative online secondary path modeling (AOSPM) method is proposed to address the above problems, which exhibits great online modeling capabilities without introducing excessive computational load. Simulation and experiment results validate the noise control performance of the proposed method, and the ANC experiment has achieved an average reduction of more than 15 dB in the sound pressure level (SPL) of the four channels, which fully demonstrates its broad engineering application prospects.
A hybrid feedforward/feedback multi-channel system was developed for active road noise control (ARNC) inside a vehicle cabin. First, a centralized feedforward subsystem was present with a multi-channel normalized weig...
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A hybrid feedforward/feedback multi-channel system was developed for active road noise control (ARNC) inside a vehicle cabin. First, a centralized feedforward subsystem was present with a multi-channel normalized weighted error fxlmsalgorithm, including a simplified reference signal normalization method, a reconstructed filter bank for filtering the reference signals, and a newly defined cost function of Aweighting error signals. Second, a distributed feedback subsystem was presented with multiple feedback single-channel simplified normalized fxlmsalgorithms. By combining the two subsystems, a hybrid ARNC system was developed. Furthermore, data related to the ARNC system were collected from the test vehicle. Next, a nondominated sorting genetic algorithm-II was introduced to optimize the system parameter. multi-objective optimization models of the feedforward and hybrid ARNC systems were established respectively, and the optimal Pareto solution sets for their parameters were obtained. Real-time experiments show that, when the test vehicle driving at 60 km/h on the small brick pavement, the developed hybrid ARNC system can achieve an overall noise reduction of 5.87 dBA in the time domain, and a peak noise reduction of 7.43 dBA in the frequency-domain. Compared with the feedforward ARNC system, road noise reduction is much improved. It still has a good noise reduction effect at other speeds and on different roads. (c) 2022 Elsevier Ltd. All rights reserved.
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