An Alternative Feedback Structure for the Adaptive Active Control of Periodic and Time-Varying Periodic Disturbances

Buchard, M.; Paillard, Bruno

doi:10.1006/jsvi.1997.1297

Cited by 16 publications

(11 citation statements)

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“…An alternative feedback structure was suggested with internal model control. This method achieves a better transient response and faster convergence, but an incorrect secondary path model seems to degrade the total control performance [39]. An LMS algorithm with adaptive step size was investigated to increase the convergence rate [40,41].…”

Section: Limitationsmentioning

confidence: 99%

Enhanced Adaptive Filtering Algorithm Based on Sliding Mode Control for Active Vibration Rejection of Smart Beam Structures

Kim

Yoon

2017

Applied Sciences

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This article investigates vibration rejection for a continuous smart structure using piezoelectric bimorph patches. Such a structure has inherent nonlinearities, such as hysteresis and creep, and the whole system may experience unexpected disturbances, uncertainties, and noise from external sources. Thus, it is very important to design the active control scheme carefully with adaptive filtering systems to deal with these conditions. An advanced adaptive filtering algorithm was developed based on the conventional least mean squares (LMS) method and sliding mode control for the active vibration rejection system. The sliding mode controller is applied to the standard LMS algorithm to overcome problems with misadjustment and excess error in an optimal manner. A numerical analysis and laboratory experiment show that the technique can significantly attenuate the vibration of the smart structure at different levels and broadband frequency spectra. In addition, unidentified impedance is chosen to change the distribution of the mass, and the robustness and the adaptivity of the proposed approach are verified. The experimental results show that the method can isolate impulse-type vibrations of at least 2.8 dB, even with the adjusted mass arrangement.

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Section: Limitationsmentioning

confidence: 99%

Enhanced Adaptive Filtering Algorithm Based on Sliding Mode Control for Active Vibration Rejection of Smart Beam Structures

Kim

Yoon

2017

Applied Sciences

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“…However, the Filtered-x algorithm may become unstable owing to the phase error of the secondary path model [9]. In this paper we also consider a modified-error feedback ANC system [10] that can update the noise control filter using an ordinary adaptive algorithm such as NLMS. The modified-error algorithm utilizes an extra noise control filter, which is updated by a common adaptive algorithm in the background.…”

Section: Introductionmentioning

confidence: 99%

Feedback active noise control system and its application to MRI noise

Kajikawa

2011

2011 8th International Conference on Information, Communications &Amp; Signal Processing

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In this paper, a novel feedback active noise control (ANC) system combining a linear prediction filter and its application to reducing noise generated from magnetic resonance imaging (MRI) devices (MRI noise) are introduced. The proposed ANC system can reduce narrowband noise while suppressing a disturbance with broadband components. Such a disturbance makes a conventional feedback ANC system unstable or divergent because the disturbance corrupts the input signal to the system. In the proposed ANC system, a linear prediction filter is combined with the feedback ANC system to suppress the disturbance. Moreover, a modified-error feedback ANC system that can update the noise control filter using an ordinary adaptive algorithm such as NLMS instead of the Filtered-x algorithm is also incorporated with the linear prediction filter. In the modifiederror feedback ANC system, not only the stability but also the convergence rate and noise reduction ability can be improved. Simulation results demonstrate that the proposed feedback ANC systems are superior to conventional feedback ANC systems in terms of the stability, convergence rate, and noise reduction ability. Furthermore, the proposed ANC system is applied to reducing MRI noise. MRI noise consists of many sinusoidal waves whose amplitude varies with time. We found that the feedback ANC system can effectively reduce MRI noise. The proposed ANC system is implemented in a head-mounted structure to control the noise near the user's ears and to compensate for the low output of compact loudspeakers. Experimental results demonstrate that the proposed ANC system with head-mounted structure can significantly reduce MRI noise by approximately 30 dB in a high field in an actual MRI room even if the imaging mode changes frequently.

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“…Comparing with feedforward ANC, the feedback ANC is not involved of reference microphone and only error microphone is required, it reduces the number of system components and cuts back the cost greatly [5][6][7][8][9]. Because there is no reference microphone, so the problem caused by secondary source (loudspeaker) output getting back to the reference microphone and affecting the performance of ANC system can be avoided.…”

Section: Introductionmentioning

confidence: 99%

“…Another advantage of feedback ANC is that it is unaffected to multiple noise sources. The principle of feedback ANC is adaptive prediction technology [9][10][11]. According to the primary noise, the feedback ANC can be subdivided to narrowband feedback ANC and wideband feedback ANC.…”

Section: Introductionmentioning

confidence: 99%

A novel feedback active noise control for broadband chaotic noise and random noise

2017

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The feedback active noise control (ANC) can be seen as a predictor, the conventional method based on filteredx least mean square (FXLMS) algorithm can only be useful for linear and tonal noise, but for nonlinear and broadband noise, it is useless. The feedback ANC using functional link artificial neural networks (FLANN) based on filtered-s least mean square (FSLMS) algorithm can reduce some nonlinear noise such as chaotic noise, but the noise cancellation performance is not very well, at the same time, it is not useful to random noise. To solve the problem above, a new feedback ANC using wavelet packet FXLMS (WPFXLMS) algorithm is proposed in this paper. By decomposing the broadband noise into several band-limited parts which are predictable and each part is controlled independently, the proposed algorithm can not only suppress the chaotic noise, but also migrate the random noise. Compared with FXLMS and FSLMS algorithms, proposed WPFXLMS algorithm also holds the best performance on noise cancellation. Numerous simulations are conducted to demonstrate the effectiveness of the proposed WPFXLMS algorithm.

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An Alternative Feedback Structure for the Adaptive Active Control of Periodic and Time-Varying Periodic Disturbances

Cited by 16 publications

References 0 publications

Enhanced Adaptive Filtering Algorithm Based on Sliding Mode Control for Active Vibration Rejection of Smart Beam Structures

Enhanced Adaptive Filtering Algorithm Based on Sliding Mode Control for Active Vibration Rejection of Smart Beam Structures

Feedback active noise control system and its application to MRI noise

A novel feedback active noise control for broadband chaotic noise and random noise

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