Adaptive active noise control systems: algorithms and digital signal processing (DSP) implementations

Kuo, Sen M.

doi:10.1117/12.204209

Cited by 422 publications

(1,032 citation statements)

References 0 publications

Supporting

Mentioning

998

Contrasting

Unclassified

Order By: Relevance

“…a feedback control that has a feedback controller K(s) embedded with a feedforward control loop in which an adaptive FIR filter W(z) is used, as illustrated in Figure 2. (a) Feedforward control-The feedforward loop is an adaptive controller which uses the filtered-x least mean square (FXLMS) algorithm to adjust the filter weights. The FXLMS control algorithm, an extended version of the LMS algorithm typically used for dynamic systems with phase delay secondary paths, is well studied and has been widely applied to many active vibration and noise control applications [14,16,[19][20][23][24]. This controller requires detailed information about the secondary path transfer function.…”

Section: Controller Designmentioning

confidence: 99%

See 1 more Smart Citation

Simulation study on active noise control for a 4-T MRI scanner

Lim

Lee

2008

Magnetic Resonance Imaging

View full text Add to dashboard Cite

The purpose of this work is to study computationally the possibility of the application of a hybrid active noise control technique for MRI acoustic noise reduction.A hybrid control system combined with both feedforward and feedback loops embedded is proposed for potential application on active MRI noise reduction. A set of computational simulation studies were performed. Sets of MRI acoustic noise emissions measured at the patient's left ear location were recorded and used in the simulation study.By comparing three different control systems, namely the feedback, the feedforward and the hybrid control, our results revealed that the hybrid control system is the most effective. The hybrid control system achieved approximately a 20 dB reduction at the principal frequency component.We concluded that the proposed hybrid active control scheme could have a potential application for MRI scanner noise reduction.

show abstract

Section: Controller Designmentioning

confidence: 99%

“…The controller also requires a clean reference signal that is coherent with the primary disturbance. The first issue, concerning the need for a secondary path transfer function, can be handled by applying a system identification method that was discussed in detail in the work of Kuo and Morgan [23].…”

Section: Controller Designmentioning

confidence: 99%

Simulation study on active noise control for a 4-T MRI scanner

Lim

Lee

2008

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…The adaptive feedback controller considered in this chapter can be viewed as a special case of Filtered-U control where the control-to-reference padi and the control-toerror path are identical because die error sensor also serves as the reference sensor [14]. In a purely feedforward control system, a separate reference signal that is correlated with the disturbance is used as the input to the adaptive filter.…”

Section: Classical Disturbance Suppressionmentioning

confidence: 99%

“…Adaptive feedback control attempts to remedy this problem. [8], [14]. This arrangement employs the filtered-X LMS algorithm with an estimate of the actual control-toerror path Gp.…”

Section: E{z) = -Niz)-g^(z)wiz)r(z) Ve(z) = -^^ = -G^(z)r{z) Dw{z)mentioning

confidence: 99%

“…These adaptive filtering methods, such as the LMS algorithm proposed by Widrow and Stearns (1985), have been highly successful in the active noise and vibration control community for narrowband disturbance rejection. The most relevant application of LMS control for noise or vibration control was the socalled Filtered-U algorithm [14] which required feedback compensation for the reference sensor. The ACC implementation is similar in its need for reference sensor compensation because die reference sensor is often identical to the error sensor.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

System Design Methods for Simultaneous Optimal Control of Combustion Instabilities and Efficiency

Baumann¹,

Saunders²,

Vandsburger³

2006

Combustion Processes in Propulsion

View full text Add to dashboard Cite

Fast exact adaptive algorithms for feedforward active noise control

Nelson

Douglas

Bodson

2000

Int. J. Adapt. Control Signal Process.

View full text Add to dashboard Cite

The fast exact least‐mean‐square (LMS) algorithm is a computationally efficient method for computing the outputs and updates for an adaptive LMS finite impulse response (FIR) filter. In this paper, we extend this method to several useful algorithms for feedforward active noise control: the filtered‐X LMS, modified filtered‐X LMS, efficient modified filtered‐X LMS, periodic filtered‐X LMS, and sequential filtered‐X LMS algorithms, respectively. Choosing a block size of two produces overall behaviour for these fast exact versions that are identical to their non‐block counterparts while reducing the numbers of multiplies by up to 25 per cent over those required by the standard algorithms. We then describe Motorola DSP96002 DSP‐ based implementations of the standard and fast exact versions of the filtered‐X LMS algorithm. Our results show that the fast exact implementation can allow a 27.4 per cent increase in the filter lengths over those of the standard implementation on this processor, which is close to the 33.3 per cent increase that would be expected if the number of multiplies were a true indication of an algorithm's complexity. Copyright © 2000 John Wiley & Sons, Ltd.

show abstract

Adaptive active noise control systems: algorithms and digital signal processing (DSP) implementations

Cited by 422 publications

References 0 publications

Simulation study on active noise control for a 4-T MRI scanner

Simulation study on active noise control for a 4-T MRI scanner

System Design Methods for Simultaneous Optimal Control of Combustion Instabilities and Efficiency

Fast exact adaptive algorithms for feedforward active noise control

Contact Info

Product

Resources

About