Active Noise Control

Kuo, Sen M.; Morgan, D.R.

doi:10.1007/978-3-540-49127-9_49

Cited by 68 publications

(128 citation statements)

References 35 publications

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“…Most of the related studies (Elliott [19], Kuo et al [20]) present a multi-channel architecture that estimates the primary noise at each error sensor. This is a multi-reference algorithm in the sense that, in a K Â K configuration (K secondary sources and K error sensors), K reference signals must be synthesized and the feedback controller is represented by a K Â K matrix-the coefficients of which are the adaptive FIR filters ðW ij ðzÞÞ i;j¼1 ÁÁÁ K .…”

Section: Extension To the Multi-channel Casementioning

confidence: 99%

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A multi-channel feedback algorithm for the development of active liners to reduce noise in flow duct applications

Mazeaud

Galland

2007

Mechanical Systems and Signal Processing

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Section: Extension To the Multi-channel Casementioning

confidence: 99%

“…The robustness of such a feedback algorithm has been extensively discussed in the literature [15][16][17]. It emerges that the SISO structure provides rapid and stable multi-tone convergence in noise signals, without increasing the random noise.…”

Section: Algorithm Performancementioning

confidence: 99%

A multi-channel feedback algorithm for the development of active liners to reduce noise in flow duct applications

Mazeaud

Galland

2007

Mechanical Systems and Signal Processing

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“…The common approach in ANC/AVC is adaptive filtering, where the filter weights are usually updated with the FxLMS algorithm [1]. In most cases, disturbance feedforward is used, although it is possible to use an adaptive filter in feedback control with a technique called "secondary path neutralization" [1], which is equivalent to internal model control [2]. The main disadvantage of this approach is that stability and transient behavior (convergence speed) depend on the system input and therefore, the analysis of the closed loop system is difficult.…”

Section: Introductionmentioning

confidence: 99%

“…Several approaches exist to construct such a controller. The common approach in ANC/AVC is adaptive filtering, where the filter weights are usually updated with the FxLMS algorithm [1]. In most cases, disturbance feedforward is used, although it is possible to use an adaptive filter in feedback control with a technique called "secondary path neutralization" [1], which is equivalent to internal model control [2].…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of an LPV-gain-scheduled observer for rejecting multisine disturbances with time-varying frequencies

Heins

Ballesteros

Bøhn

2012

2012 American Control Conference (ACC)

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A linear time-varying (LTV) discrete-time controller for the rejection of harmonic disturbances with timevarying frequencies on linear time-invariant (LTI) systems is proposed and validated in real-time experiments. The controller is an observer-based state feedback controller with a time-varying observer system matrix and a time-invariant state feedback gain. For the controller design, the combination of plant and disturbance is modeled as a polytopic linear parameter-varying (pLPV) system and linear matrix inequalities (LMIs) derived from quadratic stability theory and LMIs for an upper bound on the H 2 performance are used. The design guarantees closed-loop stability even for arbitrarily fast changes of the varying parameters. The controller is tested on an active noise control headset for a disturbance signal consisting of four harmonics.

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“…Adaptive feedforward control is frequently used to reject harmonic disturbances when the disturbance frequencies are known or can be estimated [4][5][6][7]. Adaptive feedforward algorithms typically rely on least-mean-square (LMS) or recursive least-mean-square (RLMS) algorithms to update parameters.…”

Section: Introductionmentioning

confidence: 99%

Broadband adaptive disturbance rejection for a deployable optical telescope testbed

Hoagg

Lacy²,

Bernstein³

Proceedings of the 2005, American Control Conference, 2005.

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Broadband adaptive disturbance rejection is demonstrated on the artificial white-light source of the Deployable Optical Telescope (DOT) developed by the Air Force Research Laboratory (AFRL). Large deployable optical systems are envisioned as the future of space telescopes. These systems will require active control for disturbance rejection. In this paper, we consider a multi-input, multi-output discretetime adaptive disturbance rejection algorithm. The algorithm requires knowledge of some Markov parameters of the system from the control signal inputs to the performance variables. No information about the disturbance spectrum is required. We demonstrate broadband disturbance rejection numerically on an identified model of DOT's white-light source and experimentally on the system itself.

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Active Noise Control

Cited by 68 publications

References 35 publications

A multi-channel feedback algorithm for the development of active liners to reduce noise in flow duct applications

A multi-channel feedback algorithm for the development of active liners to reduce noise in flow duct applications

Experimental evaluation of an LPV-gain-scheduled observer for rejecting multisine disturbances with time-varying frequencies

Broadband adaptive disturbance rejection for a deployable optical telescope testbed

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