Stability of active noise control algorithms

Jacobson, C.A.; Johnson, C.R.; McCormick, D. C.; Sethares, William A.

doi:10.1109/97.905944

Cited by 36 publications

(39 citation statements)

References 6 publications

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“…Adaptive feedforward vibration (or noise) compensation is currently used in ANC and AVC when an image of the disturbance is available However, at the end of the nineties it was pointed out that in most of these systems there is a physical "positive" feedback coupling between the compensator system and the measurement of the image of the disturbance (vibration or noise) [6][7][8][9]. The internal inherent physical positive feedback may cause the instability of the AVC or ANC systems.…”

Section: Matec Web Of Conferencesmentioning

confidence: 99%

“…7 The argument (z −1 ) will be omitted in some of the following equations to make them more compact. 8 It is assumed that a reliable model identification is achieved and therefore the estimated model is assumed to be equal to the true model. Selecting a FIR structure for the Q filter associated to the Youla-Kucera parametrization, the controller's polynomials become:…”

Section: Controller Structurementioning

confidence: 99%

“…The characteristic polynomial of the closed loop is still given by (8). We define the output sensitivity function (the transfer function between the disturbance p(t) and the output of the system y(t)) as…”

Section: Controller Structurementioning

confidence: 99%

See 2 more Smart Citations

Adaptive active vibration isolation – A control perspective

Landau

Airimiţoaie

Silva

et al. 2015

MATEC Web of Conferences

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Abstract. In many classes of applications like active vibration control and active noise control, the disturbances can be characterized by their frequencies content and their location in a specific region in the frequency domain. The disturbances can be of narrow band type (simple or multiple) or of broad band type. A model can be associated to these disturbances. The knowledge of the disturbance model as well as of the compensator system is necessary for the design of an appropriate control system in order to attenuate (or to reject) their effect upon the system to be controlled. The attenuation of disturbances by feedback is limited by the Bode Integral and the "water bed" effect upon the output sensitivity function. In such situations, the feedback approach has to be complemented by a "feedforward disturbance compensation" requiring an additional transducer for getting information upon the disturbance. Unfortunately in most of the situations the disturbances are unknown and time-varying and therefore an adaptive approach should be considered. The generic term for adaptive attenuation of unknown and time-varying disturbances is "adaptive regulation" (known plant model, unknown and time-varying disturbance model).The paper will review a number of recent developments for adaptive feedback compensation of multiple unknown and timevarying narrow band disturbances and for adaptive feedforward compensation of broad band disturbances in the presence of the inherent internal positive feedback caused by the coupling between the compensator system and the measurement of the image of the disturbance. Some experimental results obtained on a relevant active vibration control system will illustrate the performance of the various algorithms presented.

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Section: Matec Web Of Conferencesmentioning

confidence: 99%

Section: Controller Structurementioning

confidence: 99%

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Adaptive active vibration isolation – A control perspective

Landau

Airimiţoaie

Silva

et al. 2015

MATEC Web of Conferences

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“…The normalized data transfer equation (5) where d_signal is the current value, minn and maxn is the minimum and maximum value of the desired signal respectively. Finally Backpropagation algorithm is used to train the desired data [5]. We have considered learning rate 0.01 and the mean square error (MSE) 0.001.…”

Section: (4)mentioning

confidence: 99%

On the multi-channel noise cancellation using beamforming algorithm

Alom

Alam

Sobhan³

2008

2008 11th International Conference on Computer and Information Technology

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This paper presents performance analysis of beamforming algorithm for canceling multiple channel noise depending on variation of hidden layer of the multilayer feedforward network and the number of epoch (also known as number of iteration). We consider the learning rate variation of the multi layer perception (MLP) to determine the adaptive learning rate of the network. An MLP has been considered to perform beam-forming, which is an array of sensors connected to the MLP inputs. We use the Backpropagation algorithm as the learning rule for MLP and improving the signal quality. This involves a desired signal whilst removing any noise or interference signals which may come from different sources [1].

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“…The reliability of this measurement is compromised in most of the systems by a "positive" internal feedback coupling between the compensator system and the correlated measurement of the disturbance. The system may become unstable, if the adaptation algorithms do not take into account this positive feedback [2], [4]- [6]. One of the solutions to overcome this problem is to try to compensate the positive feedback [3], [7].…”

Section: Introductionmentioning

confidence: 96%

IIR Youla–Kucera Parameterized Adaptive Feedforward Compensators for Active Vibration Control With Mechanical Coupling

Landau

Airimiţoaie

Alma

2013

IEEE Trans. Contr. Syst. Technol.

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Adaptive feedforward broadband vibration (or noise) compensation requires a reliable correlated measurement with the disturbance (an image of the disturbance). The reliability of this measurement is compromised in most of the systems by a "positive" internal feedback coupling between the compensator system and the correlated measurement of the disturbance. The system may become unstable, if the adaptation algorithms do not take into account this positive feedback. Instead of using classical infinite impulse response (IIR) or finite impulse response (FIR) feedforward compensators, this paper proposes and analyses an IIR Youla-Kucera parametrization of the feedforward compensator. A model-based central IIR stabilizing compensator is used, and its performance is enhanced by the adaptation of the parameters (Q-parameters) of an IIR Youla-Kucera filter.Adaptation algorithms assuring the stability of the system in the presence of the positive internal feedback are provided. Their performances are evaluated experimentally on an active vibration control system. Theoretical and experimental comparisons with FIR Youla-Kucera parameterized feedforward compensators and IIR feedforward compensators are provided.

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Stability of active noise control algorithms

Cited by 36 publications

References 6 publications

Adaptive active vibration isolation – A control perspective

Adaptive active vibration isolation – A control perspective

On the multi-channel noise cancellation using beamforming algorithm

IIR Youla–Kucera Parameterized Adaptive Feedforward Compensators for Active Vibration Control With Mechanical Coupling

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