Abstract
Noise in a dynamic system is practically unavoidable. Today, such noise is commonly reduced using an active noise control (ANC) system with the filtered-x least mean square (FXLMS) algorithm. However, the performance of the ANC system with FXLMS algorithm is significantly impaired in nonlinear systems. Therefore, this paper develops an efficient nonlinear adaptive feedback neural controller (NAFNC) to eliminate narrowband noise for both linear and nonlinear ANC systems. The proposed controller is implemented to update its coefficients without prior offline training by neural network. Hence, the proposed method has rapid convergence rate as confirmed by simulation results. The proposed work also analyzes the stability and convergence of the proposed algorithm. Simulation results verify the effectiveness of the proposed method.
Highlights
Noise is everywhere and affects us constantly. e usual approach to reduce noise uses sound-proof or sound-absorbing materials, called passive noise reduction, having the disadvantage of requiring bulky materials and being ineffective in cancelling low-frequency noise. e active noise control (ANC) method, introduced by Lueg in 1936 [1], is built by adding a secondary source to eliminate the undesired noise by the superposition principle
Guo et al improved fuzzy control algorithm based on piezoelectric ceramic materials and proposed the variable stepsize median-LMS algorithm for the vehicle interior noise [5, 6]. ereby, it can be seen that the ANC system has made positive contributions to life
(4) Simulation results verify the effectiveness of the proposed method in both linear and nonlinear ANC systems is paper is organized as follows
Summary
Noise is everywhere and affects us constantly. e usual approach to reduce noise uses sound-proof or sound-absorbing materials, called passive noise reduction, having the disadvantage of requiring bulky materials and being ineffective in cancelling low-frequency noise. e active noise control (ANC) method, introduced by Lueg in 1936 [1], is built by adding a secondary source to eliminate the undesired noise by the superposition principle. Two sound waves from both primary and secondary sources interfere with each other, resulting in both noise cancellation and very efficient low-frequency operation [2]. Gabel et al proposed a multichannel active control system to reduce vehicle interior noise on the road. E proposed algorithm reduced the level of structural vibration and vehicle interior noise under the operating conditions [3]. Wang et al proposed active control using the discrete wavelet transform (DWT) based FXLMS algorithm with a piezoelectric feedback system to cope with the stationary and nonstationary noises in a simplified vehicle cavity model [4]. Guo et al improved fuzzy control algorithm based on piezoelectric ceramic materials and proposed the variable stepsize median-LMS algorithm for the vehicle interior noise [5, 6]. Guo et al improved fuzzy control algorithm based on piezoelectric ceramic materials and proposed the variable stepsize median-LMS algorithm for the vehicle interior noise [5, 6]. ereby, it can be seen that the ANC system has made positive contributions to life
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