Adaptive variable step-size neural controller for nonlinear feedback active noise control systems

Adaptive filter techniques and the filtered-x least mean square (FxLMS) algorithm have been used in Active Noise Control (ANC) systems. However, their effectiveness may degrade due to the nonlinearities and modeling errors in the system. In this paper, a new feedback ANC system with an adaptive neural controller and variable step-size learning parameters (VSSP) is proposed to improve the performance. A nonlinear adaptive controller with the FxLMS algorithm is first designed to replace the traditional adaptive FIR filter; then, a variable step-size learning method is developed for online updating the controller parameters. The proposed control is implemented without any offline learning phase, while faster convergence and better noise elimination can be achieved. The main contribution is that we show how to analyze the stability of the proposed closed-loop ANC systems, and prove the convergence of the presented adaptations. Moreover, the computational complexities of different methods are compared. Comparative simulation results demonstrate the validity of the proposed methods for attenuating different noise sources transferred via nonlinear paths, and show the improved performance over classical methods.     

Adaptive active noise and vibration control systems

A review of research concerned with adaptive noise and vibration control systems and performed under the supervision of Corresponding Member of the Russian Academy of Sciences V.A. Zverev at Nizhni Novgorod State University in the 1980s and 1990s is presented. The history of the subject is briefly outlined, and the theoretical foundations of the design of adaptive active control systems for random wave fields are considered. The main experimental studies performed in this area of research at the Department of Bionics and Statistical Radiophysics of Nizhni Novgorod State University are described. Promising lines of research in this area are indicated, and examples of the practical application of adaptive control systems are given.     

A nonlinear active noise control algorithm for virtual microphones controlling chaotic noise

In active noise control (ANC) systems, virtual microphones provide a means of projecting the zone of quiet away from the physical microphone to a remote location. To date, linear ANC algorithms, such as the filtered-x least mean square (FXLMS) algorithm, have been used with virtual sensing techniques. In this paper, a nonlinear ANC algorithm is developed for a virtual microphone by integrating the remote microphone technique with the filtered-s least mean square (FSLMS) algorithm. The proposed algorithm is evaluated experimentally in the cancellation of chaotic noise in a one-dimensional duct. The secondary paths evaluated experimentally exhibit non-minimum phase response and hence poor performance is obtained with the conventional FXLMS algorithm compared to the proposed FSLMS based algorithm. This is because the latter is capable of predicting the chaotic signal found in many physical processes responsible for noise. In addition, the proposed algorithm is shown to outperform the FXLMS based remote microphone technique under the causality constraint (when the propagation delay of the secondary path is greater than the primary path). A number of experimental results are presented in this paper to compare the performance of the FSLMS algorithm based virtual ANC algorithm with the FXLMS based virtual ANC algorithm.     

A robust filtered-s LMS algorithm for nonlinear active noise control

The performance of a nonlinear active noise control (ANC) system based on the recently developed filtered-s least mean square (FsLMS) algorithm deteriorates when strong disturbances in the ANC system are acquired by the microphones. To surmount this shortcoming, a novel robust FsLMS (RFsLMS) algorithm is proposed for a functional link artificial neural network (FLANN) based ANC system. The new ANC system is least sensitive to such disturbances and does not call for any prior information on the noise characteristics. The results obtained from simulation study establish the effectiveness of this new ANC scheme.     

Adaptive Laguerre filters for active noise control

Feedforward controllers are used in many active noise control (ANC) systems to generate destructive interference in noise fields. An ideal feedforward ANC controller should have an infinite impulse response (IIR) transfer function, but most available feedforward ANC controllers have finite impulse responses (FIR) instead. The main reason is related to the adaptation algorithms of ANC systems. In general, adaptive FIR filters converge faster with guaranteed stability. In this study, the adaptive Laguerre filter is proposed and tested in an ANC application with positive experimental effects. The new ANC controller is an IIR filter, but its adaptation is similar to that of a FIR filter with fast convergence and guaranteed stability. Detailed explanations and analysis are presented in the main text.     

A gradient-free adaptation method for nonlinear active noise control

Active Noise Control (ANC) problems are often affected by nonlinear effects, such as saturation and distortion of microphones and loudspeakers. Nonlinear models and specific adaptation algorithms must be employed to properly account for these effects. The nonlinear structure of the problem complicates the application of gradient-based Least Mean Squares (LMS) algorithms, due to the fact that exact gradient calculation requires executing nonlinear recursive filtering operations, which pose computational and stability issues. One favored solution to this problem consists in neglecting recursive terms in the gradient calculation, an approximation which is not always without consequences on the convergence performance. Besides, an efficient application of nonlinear models cannot avoid some form of model structure selection, to avoid the well-known effects of overparametrization and to reduce the computational load on-line. Unfortunately, the standard ANC setting configures an indirect identification problem, due to the presence of the secondary path in the control loop. In the nonlinear case, this destroys the linear regression structure of the problem even if the control filter is linear-in-the-parameters, thereby making it impossible to apply the many existing model selection methods for linear regression problems. A simple and computationally wise low demanding approach is here proposed for parameter estimation and model structure selection that provides an answer to the mentioned issues. The proposed method avoids altogether the use of the error gradient and relies on direct cost function evaluations. A virtualization scheme is used to assess the accuracy improvements when the model is subject to parametric or structural modifications, without directly affecting the control performance. Several simulation examples are discussed to show the effectiveness of the proposed algorithms.     

Improving performance of FxRLS algorithm for active noise control of impulsive noise

Active noise control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. New impulsive noise control algorithms based on filtered-x recursive least square (FxRLS) algorithm are presented. The FxRLS algorithm gives better convergence than the filtered-x least mean square (FxLMS) algorithm and its variants but lacks robustness in the presence of high impulsive noise. In order to improve the robustness of FxRLS algorithm for ANC of impulsive noise, two modifications are suggested. First proposed modification clips the reference and error signals while, the second modification incorporates energy of the error signal in the gain of FxRLS (MGFxRLS) algorithm. The results demonstrate improved stability and robustness of proposed modifications in the FxRLS algorithm. However, another limitation associated with the FxRLS algorithm is its computationally complex nature. In order to reduce the computational load, a hybrid algorithm based on proposed MGFxRLS and normalized step size FxLMS (NSS-FXLMS) is also developed in this paper. The proposed hybrid algorithm combines the stability of NSS-FxLMS algorithm with the fast convergence speed of the proposed MGFxRLS algorithm. The results of the proposed hybrid algorithm prove that its convergence speed is faster than that of NSS-FxLMS algorithm with computational complexity lesser than that of FxRLS algorithm.     

Prebifurcational noise rise in nonlinear systems

The phenomenon of prebifurcational noise increase in nonlinear systems in the process of period-doubling bifurcation is investigated. The study is conducted for a discrete system (quadratic mapping); how-ever, many of the laws discovered apply to more general systems. Estimates of the fluctuation variance are obtained both for the linear (away from the bifurcation threshold) and for the nonlinear mode (in the vicinity of the bifurcation threshold). It is shown that the variance of forced fluctuations in the strongly nonlinear mode is proportional to the root-mean-square of the noise intensity rather than to the variance. The possibility of measuring the noise in nonlinear systems on the basis of the prebifurcational noise amplification factor is demonstrated.     

Adaptive noise cancellation in neuromagnetic measurement systems

Summary The noise spectrum of a superconducting second-derivative gradiometer has been investigated in an urban environment. Data have been acquired from the gradiometer with three orthogonal accelerometers and a triaxial fluxgate magnetometer attached to the dewar. These data have been analyzed by using signal processing techniques, primarily an adaptive noise canceller, to reduce noise in the gradiometer data. Results shown here indicate that the environmental noise and/or the dewar motion noise can be reduced as much as 40 dB in noise power. It is also shown, in the case of poor signal-to-noise ratio (S/N∼1), that a 10 Hz sine wave can be extracted. This technique not only shows promise for noise reduction, but also aids in the identification of noise signals which might be misconstrued as part of the evoked response. Analysis of data containing evoked response is now underway. Paper presented at the ?IV International Workshop on Biomagnetism?, held in Rome, September 14–16, 1982.     

Adaptive fuzzy nonlinear inversion-based control for uncertain chaotic systems

<正>This paper presents a robust output feedback control method for uncertain chaotic systems,which comprises a nonlinear inversion-based controller with a fuzzy robust compensator.The proposed controller eliminates the unknown nonlinear function by using a fuzzy system,whose inputs are not the state variables but feedback error signals.The underlying stability analysis as well as parameter update law design are carried out by using the Lyapunov-based technique.The proposed method indicates that the nonlinear inversion-based control approach can also be applied to uncertain chaotic systems.Theoretical results are illustrated through two simulation examples.     

On implementation of adaptive bilinear filters for nonlinear active noise control

In this technical note, the simplified diagonal-structure bilinear filtered-X least mean square (SDBFXLMS) and channel-reduced diagonal-structure bilinear filtered-X least mean square (CRDBFXLMS) algorithms are proposed. Computational complexity for each proposed algorithm is analyzed to show the significant computational reduction in comparison with the diagonal-structure bilinear FXLMS (DBFXLMS) algorithm. For L=15$L=15$ (memory length of the bilinear filter), P=2$P=2$ (the corresponding number of the diagonal channels for the SDBFXLMS algorithm is L+2P=19$L+2P=19$ and the corresponding number of the diagonal channels for the CRDBFXLMS algorithm is 2P=4)$2P=4)$, and M=64$M=64$ (memory length of the secondary path estimate), the SDBFXLMS algorithm achieves 45% and 40% reduction of multiplications and additions, respectively, while the CRDBFXLMS algorithm acquires 78% reduction of multiplications and 76% reduction of additions. Computer simulations validate the satisfied control performances of the proposed algorithms.     

Interior noise control with an active window system

An active window system to reduce the exterior noise sources, such as traffic noise and construction noise which enter rooms through open windows used for natural ventilation is proposed. The proposed system uses a feedforward control method for active noise control so as not to place the sensors and control sources inside the interior space of the building. For global noise reduction throughout the interior room, the control gains for feedforward control are calculated to minimize the total acoustic power of the new source, which is combined with the noise source corresponding to the open window and control sources on the window frame. The performance of the proposed system for directional exterior noise is confirmed with a scale-model experiment. The experimental results show that the proposed system can achieve a noise reduction of up to 10 dB for the entire room of the scale model regardless of the direction of the incident wave.     

Classical and quantum noise in nonlinear optical systems

In quantum optics noise plays an important role, since many of the nonlinear optical systems are quite sensitive to the subtle influences of weak random perturbations, being either classical of quantum mechanical in nature. We discuss the origin of quantum noise emerging from the reversible or the irreversible part of the dynamics and compare it with the properties of purely classical fluctuations. These general features are illustrated by a number of physical examples, such as the laser with loss or gain noise, nonlinear optical devices, and the phenomenon of quantum jumps. These processes have been chosen mainly to illustrate the different aspects of noise, but also because, to a large extent, they can be described in analytical terms.     

Radiation noise of an active interferometer

 An investigation of the behaviour of amplitude and frequency noise of radiation of an active interferometer in the regime of amplified optical bistability is presented. The phenomenon of non-amplification of external signal noise at sharp amplification of the external signal is established. The impact of strong amplitude fluctuations is studied. Our results have shown that the active interferometer allows an effective separation of the valid signal from the amplitude noise. The spectral density of a fluctuation of the field frequency in the active interferometer is shown to fall sharply in comparison with the “quantum limit”. The linewidth of radiation is determined by the spectral density at zero frequency and is equal to the natural linewidth of the external signal. The influence of quantum noise of the amplifying and absorbing media of the active interferometer is discussed. Received: 26 January 1996     

Lattice form adaptive infinite impulse response filtering algorithm for active noise control

In some situations of active noise control, infinite impulse response (IIR) filters are more suitable than finite impulse response (FIR) filters owing to the poles in the transfer function. A number of algorithms have been derived for applying IIR filters in active noise control; however, most of them use the direct form IIR filter structure, which faces the difficulties of checking stability and relatively slow convergence speed for noise composed of narrow-band components with large power disparity. To overcome these difficulties along with using the direct form IIR filters, a new adaptive algorithm is proposed in this paper, which uses and updates the lattice form adaptive IIR filter in an active noise control system. Full mathematical derivations of the proposed algorithm are presented, and the comparison between the proposed algorithm and the commonly used filtered-u LMS and filtered-v LMS algorithms shows the superiority of the proposed algorithm.     

A waveform synthesis algorithm for active control of transformer noise: implementation

For the active control of the transformer noise, a newly developed adaptive algorithm based on waveform synthesis was proposed in [19], where a comparison of the performance of the proposed algorithm with the FXLMS algorithm made on a single channel system showed the feasibility of the algorithm. This paper describes the implementation of the proposed algorithm on a multiple channel adaptive control system, which is used to control the noise radiated by a small transformer in an anechoic chamber. The implementation shows that the proposed algorithm requires less memory and less computation load than a typical implementation of the FXLMS algorithm and that a controller realised with the proposed algorithm can effectively reduce transformer noise and be quite robust.     

Developments in active noise control sound systems for magnetic resonance imaging

Magnetic resonance imaging (MRI) scanners can produce noise measuring over 130 dB SPL. This noise stimulates the auditory nervous system, limiting the dynamic range for stimulus driven activity in functional MRI (fMRI) experiments and can influence other brain functions. Even for structural scans it causes subject anxiety and discomfort in addition to the impediment to communications. Here we describe the realization and validation of a sound system for sound presentation inside an MRI scanner and the modifications to a standard active noise control technique for use in the noisy and compact environment of the scanner. This paper provides a review of the technology available for the presentation of audio stimuli in an MRI environment and the modifications required for the active control of scanner noise. Some of the content has been previously published [Chambers J, Akeroyd MA, Summerfield AQ, Palmer AR. Active control of the volume acquisition noise in functional magnetic resonance imaging: method and psychoacoustical evaluation. J Acoust Soc Am 2001;110(6):3041-54; Levitt H. Transformed up-down methods in psychoacoustics. J Acoust Soc Am 1971;49:467-77], but this paper goes further in describing the stages of development as the system performance was optimised. The performance of the system and both the objective and subjective reduction of the scanner noise are reported. Finally, we discuss recent improvements to the system that are currently being evaluated and describe the theory of opto-acoustical transducers that operate on the principle of light modulation. These are immune from, and do not create, electro-magnetic interference (EMI) and radio-frequency interference (RFI).