Active noise control of a duct using robust control theory

Carmona, J.C.; Alvarado, Victor M.

doi:10.1109/87.880596

Cited by 32 publications

(5 citation statements)

References 23 publications

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“…In practice, in most of the cases the characteristics of these disturbances are unknown or timevarying. While in some particular cases (with a limited range of variations in the frequency of the vibrations) a robust design can be considered (see the example given in Section 11.3 as well as [13,215,44]), in most situations, as a consequence of the high level of attenuation required, an adaptive approach is necessary to obtain a good tuning with respect to the disturbance characteristics (note that the adaptive loop can be added on top of a robust controller-see Section 12.2).…”

Section: Secondary Path Controllermentioning

confidence: 99%

“…• very reliable models for the secondary path and the "positive" feedback path can be identified; • an estimation of the primary path transfer function can be obtained using the measured w(t) as input and e(t) as output (the compensator actuator being at rest); • the quality of the primary path identified model will depend on the frequency characteristics of the signal w(t) coming from the environment; • design of a fixed model based stabilizing feedforward compensator requires the knowledge of the reverse path model only; • knowledge of the disturbance characteristics and of the primary, secondary and reverse paths models is mandatory for the design of an optimal fixed model based feedforward compensator ( [13,215,44]); • adaptation algorithms do not use information neither upon the primary path whose characteristics may be unknown nor upon the disturbance characteristics which may be unknown and time-varying.…”

Section: An Active Distributed Flexible Mechanical Structure Withmentioning

confidence: 99%

“…Linear feedforward designs based on LQG/H 2 methods are proposed in [78,31,64,180,215]. Robust linear feedforward compensator based on H ∞ theory are presented in [78,13,23,44,107,193,198,265,257]. Mixed H 2 /H ∞ techniques are used in [163,209] and minimax LQG solutions in [193,192,191].…”

Section: Notes and Referencesmentioning

confidence: 99%

“…Since these disturbances are located within two relatively small frequency ranges, it is possible to consider a linear control design which will shape the output sensitivity function in such a way that a sufficient attenuation is introduced in these two frequency regions but avoiding significant amplification at other frequencies (both for performance and robustness reason). This problem in the context of active noise control has been considered in [45] and the shaping of the output sensitivity function have been achieved using the convex optimization procedure introduced in [149]. 1 An H ∞ approach can also eventually be used but it will require a quite complicated procedure for defining the appropriate weighting functions.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Introduction to Adaptive and Robust Active Vibration Control

Landau¹,

Airimiţoaie²,

Castellanos-Silva³

et al. 2016

Adaptive and Robust Active Vibration Control

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Section: Secondary Path Controllermentioning

confidence: 99%

Section: An Active Distributed Flexible Mechanical Structure Withmentioning

confidence: 99%

Section: Notes and Referencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Introduction to Adaptive and Robust Active Vibration Control

Landau¹,

Airimiţoaie²,

Castellanos-Silva³

et al. 2016

Adaptive and Robust Active Vibration Control

View full text Add to dashboard Cite

show abstract

“…Black-box identification methods are probably the most commonly used. They are involved in mono- and multipoint noise attenuation applications (see Carmona and Alvarado, 2000; Cheer, 2012; Landau et al, 2019; Rafaely and Elliott, 1999; Schirmacher et al, 2012; Song et al, 2003) and in virtual microphones designs (see Das et al, 2013; Diaz et al, 2006; Moreau et al, 2008; Petersen et al, 2008). They generally involve finite dimensional models that are efficient numerically (acceptable model order) with strong prediction performances.…”

Section: Introductionmentioning

confidence: 99%

A position-dependent acoustic model relevant for some active noise control applications

Boultifat

Loiseau

Yagoubi

et al. 2022

Journal of Vibration and Control

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This paper focuses on acoustic modeling for active noise control applications. The desired model must be finite dimensional and efficient numerically to meet implementation requirements. It must also be parameterized by the position considered inside the cavity. This additional characteristic is desirable for addressing specific active noise control applications such as optimizing microphone placement, estimating acoustic pressure at different positions of the considered cavity, and finally attenuating noise level in a subarea of the cavity. The main contribution of this paper is to propose a finite-dimensional, low-order, and parameterized acoustic model of a cavity, suitable for active noise control applications. The resulting model is defined as a gray box that combines a one-dimensional analytical model of acoustic propagation, which handles the physical parameterization, and a black-box model that copes with actuator and sensor dynamics as well as modeling errors. The parameters of the proposed model are optimized in order to reproduce the frequency behavior of the real system (LS2N active noise control platform) in a frequency range. This model allows one to accurately reproduce the dynamics at any position in the considered cavity. The prediction performance of the proposed model was compared to a classical black-box model (usually used for active noise control applications) and validated experimentally using the LS2N active noise control platform. The analysis highlighted that the proposed gray-box model can predict the acoustic behavior in a great range of positions.

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Filtered gradient active fuzzy neural network noise control in an enclosure backed by a clamped plate

Azadi

Ohadi

2012

Adaptive Control & Signal

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SUMMARYThe performance of conventional linear algorithms in active noise control applications deteriorates facing nonlinearities in the system mainly because of loudspeakers. On the other hand, fuzzy logic and neural networks are good candidates to overcome this drawback. In this paper, the acoustic attenuation of noise in a rectangular enclosure with a flexible panel and five rigid walls is presented both theoretically and experimentally using filtered gradient fuzzy neural network (FGFNN) error back propagation algorithm in which the secondary path effect is implemented in derivation of updating rules. Considering this effect in updating rules leads to faster convergence and stability of the active noise control system. On the other hand, the primary path in the investigated system comprises an identified nonlinear model of loudspeaker inside the aforementioned box, parameters of which vary with the input current. The loudspeaker is identified using series‐parallel neural network model identification method. As a comparison, the performance of filtered‐x least mean squares and FGFNN algorithms are compared. It is observed that FGFNN controller exhibits far better results in the presence of loudspeakers with nonlinear behavior in primary path.Copyright © 2012 John Wiley & Sons, Ltd.

show abstract

Active noise control of a duct using robust control theory

Cited by 32 publications

References 23 publications

Introduction to Adaptive and Robust Active Vibration Control

Introduction to Adaptive and Robust Active Vibration Control

A position-dependent acoustic model relevant for some active noise control applications

Filtered gradient active fuzzy neural network noise control in an enclosure backed by a clamped plate

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