Active Noise Control Technique Based On Adaptive Filtering And Modal Analysis

With the economic development and social progress, the noise pollution isbecoming more and more serious problem. Noise harms people’s physical and mentalhealth, causes acoustic fatigue of mechanical equipments. Also, it degrades theoperation and stealth performance of weapon equipments. Consequently, reducing theeffect of noise has important practical significance in civil and military fields. Theactive noise control technique is an effective method for low frequency noise control.According to the mechanism of noise control, the technique can be divided into threeclasses: source control, transmission control and receiver protection. This thesisthoroughly investigates the feedforward active noise control method based on adaptivefiltering, and structural acoustic radiation control method based on modal analysis, torealize the receiver protection and source control. The main content of this thesis can besummarized as follows:1. In the adaptive feedforward active noise control system, the noise attenuation ofnoncausal system is less than that of causal system. In order to ensure the causality andenhance the controlling effectiveness, the pickup method of the reference signal isdiscussed. The reference microphones are demanded to be placed in the directions ofarrival (DOA) of noise sources. The time delay between the reference microphones andthe error microphones are increased by enlarging the distances between them, used tooffset the noncausal components caused by echo and crosstalk in the control paths.Accordingly, it is necessary to estimate the DOA of noise sources in this pickup scheme.Based on Khatri-Rao (KR) product and interpolation focusing matrix, a new algorithmis proposed to estimate the DOA. In this algorithm, the covariance matrix at eachfrequency point is stacked into column vectors through KR product. Then the columnvectors are focused on the center frequency to construct a noise-free data matrix, using aleast square interpolation focusing matrix. Finally, the DOA is estimated by the methodMUSIC in the overdetermined and underdetermined cases. With DOA estimation for theplacement of reference microphones, the experiment results verify the feasibility of theproposed pickup method, by comparing the noise control performance before and afterthe adjustment of location and number for the reference microphones.2. The delayless subband adaptive filtering technique plays a prominent role in thewideband noise active control, which can avoid the signal path delay and accelerate theconverging rate of filter weights. The noise controlling performance of this technique isaffected by the group delay and in-band aliasing of analysis filter banks. Therefore, the design of the analysis filter banks is very crucial. Typically, the analysis filter banks arecomposed of a set of filters created by modulating a prototype FIR filter (PF). Suchfilter banks suffer from a long group delay and a high side-lobe effect, which limits thenoise attenuation. An iterative second-order cone programming (SOCP) method isdeveloped to design the analysis filter banks. The in-band aliasing distortion and groupdelay constraints are expressed as the cost functions of the PF coefficients. In the sequel,the optimization problem of the cost functions can be formulated as an iterative SOCPform. The optimized PF coefficients are uniform-DFT modulated into the desiredanalysis filter banks. The simulation results demonstrate that the well designed filterbanks have a smaller in-band aliasing distortion and lower group delay, compared withthe linear-phase filter banks. Finally, a structure of the delayless subband active noisecontrol in frequency domain is provided, merged with the selective partial updatenormalized LMS algorithm. Simulation results show that the noise controllingperformance is improved with a small residual noise power spectrum, high noiseattenuation level and fast convergence rate.3. In the narrowband active noise control system, the frequency mismatch betweenthe synthesized reference signal and the primary noise will cause that the mean valuedeviation of filter weight coefficients cannot converge to zero vector. As a result, theperformance of narrowband active noise control systems degenerates significantly. Inorder to improve the performance, it is necessary to estimate the frequencies of theprimary noise. An ESPRIT-like algorithm with subspace tracking is proposed toestimate the frequencies of real-valued sinusoidal noise signal. In the proposedalgorithm, the dimension of the signal subspace is equal to the number of thefrequencies present in the observation, and a half of the signal subspace dimension inthe MUSIC method. The simulation results indicate that the proposed algorithmpossesses a high precision to meet the requirement of practical application. Equippedwith the digital signal processing (DSP) platform, the harmonic components ofvibration is controlled for the elastic thin plat through the FXLMS algorithm withoutsecondary path identification. Meanwhile the sound pressure level is reduced for thestructural acoustic radiation noise in the enclosure cavity.4. In the control technique of structural acoustic radiation based on modal analysis, aprecise model is the prerequisite for application of the control method. Recently, thesubspace identification method is popular for establishing state space model. However,it is difficult for practical application because of huge complexity. In the basis of factthat the column space of the extended observability matrix is the same with the signal subspace of autocorrelation matrix of the observation vector, a new recursive subspaceidentification algorithm is proposed for the estimation of state space method. It containstwo recursive processes: one is to compute the observation vectors by using the matrixinversion formula, the other is to estimate the extended observability matrix based onthe multistage power iteration subspace tracking algorithm. Such algorithm canexponentially converge to the principal subspace. With the numerical and practicalmodels, the experiment results show that this method can enhance the identificationprecision. In detail, it manifests a small angle between the estimation and true extendedobservability matrix, and low root mean square error between the identified output andthe actual output. Also we simulate the control environment of the structural acousticradiation by using finite element and acoustic analysis software. The state space modelof the piezoelectric smart structure is identified by the proposed method, and the controlvoltage of the secondary actuator is computed by the linear quadratic optimal controlmethod. Finally, the sound pressure level of the structural acoustic radiation noise isdecreased by controlling the structure modals which have large coupling coefficientswith the acoustic modals.