Self-balancing collaboration between linear and nonlinear adaptive sub-filters for hybrid active noise control

Abstract

Conventional adaptive filters for active noise control (ANC) are troubled by the trade-off between convergence speed and steady state error, especially when nonlinearity plays an important role in system model. In this paper, a hybrid ANC algorithm called FBFLANN is constructed by the collaboration of FIR and bilinear functional link artificial neural network. The characteristics of linear and nonlinear sub-filters are analyzed and utilized to facilitate the adaptability in noise reduction. A convex factor is employed to automatically balance the contribution of two sub-filters, achieving faster convergence and higher accuracy in the presence of nonlinear distortions. The mathematical working and updating rules of the controller are derived to regulate the ANC process theoretically. The effectiveness of the proposed FBFLANN algorithm is demonstrated by numerous simulation studies considering diverse nonlinear acoustic path models as well as acoustic feedback interference. Hardware ANC platform is built up to verify the real-time noise control performance in physical noisy environments. The simulation and experiment results confirm the applicability and prospects for further development of the presented architecture in noise control scenarios.