Adaptive frequency domain identification for ANC systems using non-stationary signals

The problem of identification of secondary path in active noise control applications is dealt with fundamentally using time-domain adaptive filters. The use of adaptive frequency domain subband identification as an alternative has some significant advantages which are overlooked in such applications. In this paper two different delayless subband adaptive algorithms for identification of an unknown secondary path in an ANC framework are utilized and compared. Despite of reduced computational complexity and increase convergence rate this approach allows us to use non-stationary audio signals as the excitation input to avoid injection of annoying white noise. For this purpose two non-stationary music and speech signals are used for identification. The performances of the algorithms are measured in terms of minimum mean square error and convergence speed. The results are also compared to a fullband algorithm for the same scenario. The proposed delayless algorithms have a closed loop structure with DFT filterbanks as the analysis filter. To eliminate the delay in the signal path two different weights transformation schemes are compared

Development of Novel Techniques to Study Nonlinear Active Noise Control

Active noise control has been a field of growing interest over the past few decades. The challenges thrown by active noise control have attracted the notice of the scientific community to engage them in intense level of research. Cancellation of acoustic noise electronically in a simple and efficient way is the vital merit of the active noise control system. A detailed study about existing strategies for active noise control has been undertaken in the present work. This study has given an insight regarding various factors influencing performance of modern active noise control systems. The development of new training algorithms and structures for active noise control are active fields of research which are exploiting the benefits of different signal processing and soft- computing techniques. The nonlinearity contributed by environment and various components of active noise control system greatly affects the ultimate performance of an active noise canceller. This fact motivated to pursue the research work in developing novel architectures and algorithms to address the issues of nonlinear active noise control. One of the primary focus of the work is the application of artificial neural network to effectively combat the problem of active noise control. This is because artificial neural networks are inherently nonlinear processors and possesses capabilities of universal approximation and thus are well suited to exhibit high performance when used in nonlinear active noise control. The present work contributed significantly in designing efficient nonlinear active noise canceller based on neural network platform. Novel neural filtered-x least mean square and neural filtered-e least mean square algorithms are proposed for nonlinear active noise control taking into consideration the nonlinear secondary path. Employing Legendre neural network led the development of a set new adaptive algorithms such as Legendre filtered-x least mean square, Legendre vi filtered-e least mean square, Legendre filtered-x recursive least square and fast Legendre filtered-x least mean square algorithms. The proposed algorithms outperformed the existing standard algorithms for nonlinear active noise control in terms of steady state mean square error with reduced computational complexity. Efficient frequency domain implementation of some the proposed algorithms have been undertaken to exploit its benefits. Exhaustive simulation studies carried out have established the efficacy of the proposed architectures and algorithms

Commande robuste et calibrage des systèmes de contrôle actif de vibrations

Dans cette thèse, nous présentons des solutions pour la conception des systèmes de contrôle actif de vibrations. Dans la première partie, des méthodes de contrôle par action anticipatrice (feedforward) sont développées. Celles-ci sont dédiées à la suppression des perturbations bande large en utilisant une image de la perturbation mesurée par un deuxième capteur, en amont de la variable de performance à minimiser. Les algorithmes présentés dans cette mémoire sont conçus pour réaliser de bonnes performances et maintenir la stabilité du système en présence du couplage positif interne qui apparaît entre le signal de commande et l'image de la perturbation. Les principales contributions de cette partie sont l'assouplissement de la condition de Stricte Positivité Réelle (SPR) par l'utilisation des algorithmes d'adaptation Intégrale + Proportionnelle et le développement de compensateurs à action anticipatrice (feedforward) sur la base de la paramétrisation Youla-Kučera. La deuxième partie de la thèse concerne le rejet des perturbations bande étroite par contre-réaction adaptative (feedback). Une méthode d'adaptation indirecte est proposée pour le rejet de plusieurs perturbations bande étroite en utilisant des filtres Stop-bande et la paramétrisation Youla-Kučera. Cette méthode utilise des Filtres Adaptatifs à Encoche en cascade pour estimer les fréquences de perturbations sinusoïdales puis des Filtres Stop-bande pour introduire des atténuations aux fréquences estimées. Les algorithmes sont vérifiés et validés sur un dispositif expérimental disponible au sein du département Automatique du laboratoire GIPSA-Lab de Grenoble.In this thesis, solutions for the design of robust Active Vibration Control (AVC) systems are presented. The thesis report is composed of two parts. In the first one, feedforward adaptive methods are developed. They are dedicated to the suppression of large band disturbances and use a measurement, correlated with the disturbance, obtained upstream from the performance variable by the use of a second transducer. The algorithms presented in this thesis are designed to achieve good performances and to maintain system stability in the presence of the internal feedback coupling which appears between the control signal and the image of the disturbance. The main contributions in this part are the relaxation of the Strictly Positive Real (SPR) condition appearing in the stability analysis of the algorithms by use of Integral + Proportional adaptation algorithms and the development of feedforward compensators for noise or vibration reduction based on the Youla-Kučera parameterization. The second part of this thesis is concerned with the negative feedback rejection of narrow band disturbances. An indirect adaptation method for the rejection of multiple narrow band disturbances using Band-Stop Filters (BSF) and the Youla-Kučera parameterization is presented. This method uses cascaded Adaptive Notch Filters (ANF) to estimate the frequencies of the disturbances' sinusoids and then, Band-stop Filters are used to shape the output sensitivity function independently, reducing the effect of each narrow band signal in the disturbance. The algorithms are verified and validated on an experimental setup available at the Control Systems Department of GIPSA-Lab, Grenoble, France.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Recent Technological Advances in Spatial Active Noise Control Systems

This article provides a broad overview of the recent advances in the field of active noise control techniques to reduce unwanted noise over a certain spatial region of interest. Thanks to commercial and technological advances in local active noise control systems extending the size of the quiet zone seems to be a crucial step to developing the next generation of active control systems for a more personalized and quieter audio product. In this review article, the advances over the past decade the in design and development of spatial active noise control techniques to enlarge the controlled sound zone is reviewed. The focus is specifically on the adaptive control techniques and the methods proposed in the frequency domain to control the sound field. The study has paid specific attention to the most important performance measures in designing a spatial active noise control system such as convergence rate, stability and robustness of the algorithm, the size of the quiet zone and how it can be enlarged by configuring the loudspeaker and microphone array geometries. Finally, the authors will discuss the current and future challenges that should be overcome to improve the effectiveness of the recently proposed methods to expand the silence zone

Adaptive Algorithms Design for Active Noise Control Systems with Disturbance at Reference and Error Microphones

Active noise control (ANC) is a popular choice for mitigating the acoustic noise in the surrounding environment resulting from industrial and medical equipment, appliances, and consumer electronics. ANC cancels the low frequency acoustic noise by generating a cancelling sound from speakers. The speakers are triggered by noise control filters and produce sound waves with the same amplitude and inverted phase to the original sound. Noise control filters are updated by adaptive algorithms. Successful applications of this technology are available in headsets, earplugs, propeller aircraft, cars and mobile phones. Since multiple applications are running simultaneously, efficiency of the adaptive control algorithms in terms of implementation, computations and performance is critical to the performance of the ANC systems. The focus of the present project is on the development of efficient adaptive algorithms that perform optimally in different configurations of ANC systems suitable for real world applications.Thesis (Ph.D.) -- University of Adelaide, School of Electrical & Electronic Engineering, 202

Retrospective Cost Adaptive Control for Feedback and Feedforward Noise Control

This dissertation concerns the development of retrospective cost adaptive control (RCAC) and the application of RCAC to the active noise control (ANC) problem. We further the development of RCAC by presenting an alternative interpretation the retrospective performance variable. The retrospective performance decomposition is derived which separates the retrospective performance into the sum of a pseudo-performance term and a model-matching error term. We demonstrate an experimental application of RCAC by applying it to the broadband feedback road noise suppression problem in a vehicle. We show that RCAC is able to suppress the primary modes of the road noise at the performance microphone location. However, qualitative evaluation of the noise at the location of the driver was poor. This leads to the question, if you suppress the noise at the performance microphone, what is effect at the actual ear of the driver where you may not be able to place a sensor. The concept of spatial spillover is explored, where we develop an operator that relates relative suppression at the performance microphone to relative suppression at the evaluation microphone, which we denote as the spatial spillover function. The properties of the spatial spillover function are then validated numerically and experimentally. Finally, the framework of RCAC is extended to the feedforward control problem. Comparisons of RCAC feedforward control are made to linear-quadratic-Gaussian (LQG) control. It is shown that under certain conditions, RCAC is able to match the performance of LQG. Furthermore, we compare RCAC to the filtered-x/filtered-u least-mean-square (Fx/FuLMS) and the filtered-x/filtered-u recursive-least-square (Fx/FuRLS) algorithms and demonstrate numerically that RCAC is able to achieve better asymptotic performance that FuRLS. The RCAC feedforward control algorithm is demonstrated in an acoustic experiment. We demonstrate experimentally that if the ideal feedforward controller is implementable, the RCAC controller is able to recover the frequency response of the ideal controller.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/140812/1/antai_1.pd

The investigation and design of a piezoelectric active vibration control system for vertical machining centres

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Applications of fuzzy counterpropagation neural networks to non-linear function approximation and background noise elimination

An adaptive filter which can operate in an unknown environment by performing a learning mechanism that is suitable for the speech enhancement process. This research develops a novel ANN model which incorporates the fuzzy set approach and which can perform a non-linear function approximation. The model is used as the basic structure of an adaptive filter. The learning capability of ANN is expected to be able to reduce the development time and cost of the designing adaptive filters based on fuzzy set approach. A combination of both techniques may result in a learnable system that can tackle the vagueness problem of a changing environment where the adaptive filter operates. This proposed model is called Fuzzy Counterpropagation Network (Fuzzy CPN). It has fast learning capability and self-growing structure. This model is applied to non-linear function approximation, chaotic time series prediction and background noise elimination