An extrapolation-based virtual sensing technique of improving the control performance of the FxLMS algorithm in a maritime environment

Mylonas, Dimitrios; Erspamer, Alberto; Yiakopoulos, Christos; Antoniadis, Ioannis

doi:10.1016/j.apacoust.2022.108756

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…In addition, Li introduced the fractional order concept and implemented a multichannel FXLMS approach, using multiple error sensors and loudspeakers to achieve better performance in terms of reducing noise in a large area [13]. These approaches require a number of error sensors to collect noise in a local zone of quiet (ZoQ) around the target noise reduction area as a feedback signal to the approach [14,15]. However, installing many error sensors for active noise reduction in large-space multiunit ART systems is difficult due to vehicle structure, process, and cost constraints.…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Fuzzy Neural Network Active Noise Cancellation Approach without Error Sensors for Autonomous Rail Rapid Transit

Li,

He,

Wang

et al. 2023

Processes

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Autonomous rail rapid transit (ART) is a new type of multiunit, articulated, rubber-wheeled urban transport system. The noise sources of ART have significant time-varying characteristics. It is unsuitable to track the error signal by installing too many error sensors, which poses a significant challenge in the active noise control of ART. Thus, this paper proposes a convolutional fuzzy neural network-based active noise cancellation approach without error sensors to solve this problem. The proposed approach utilizes convolutional neural network (CNN) to extract the noise signal characteristics of ART and trains multiple noise source signals using a CNN to estimate the virtual error signal in the target area. In addition, the proposed approach adopts fuzzy neural network (FNN) for adaptive adjustment of filter weight coefficients to achieve real-time noise tracking control with fast convergence and small error in the convergence process. The experimental results demonstrate that the proposed approach can effectively reduce ART low-frequency noise without error sensors, and the average sound pressure level in the target area is reduced more compared with conventional approaches.

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Section: Introductionmentioning

confidence: 99%

A Convolutional Fuzzy Neural Network Active Noise Cancellation Approach without Error Sensors for Autonomous Rail Rapid Transit

Li,

He,

Wang

et al. 2023

Processes

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“…Kuo and Liu discussed the effectiveness of filtered-x least mean-squares (FxLMS) to cancel both the outside and inside noises of infant incubators in a real NICU environment, and they obtained an average reduction of 13 dB and 17 dB [1,2]. Mylonas developed a simple, robust and computationally efficient virtual sensing FxLMS algorithm and tested it in a cabin mock-up [3]. Zhang proposed a normalized frequency-domain block FxLMS (NFB-FxLMS) to reduce the interior noise of vehicles and showed that the higher the vehicle speed, the more obvious the advantages of the NFB-FxLMS [4].…”

Section: Introductionmentioning

confidence: 99%

A New Proportionate Filtered-x RLS Algorithm for Active Noise Control System

Liang

Yao

Luo

et al. 2022

Sensors

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The filtered-x recursive least square (FxRLS) algorithm is widely used in the active noise control system and has achieved great success in some complex de-noising environments, such as the cabin in vehicles and aircraft. However, its performance is sensitive to some user-defined parameters such as the forgetting factor and initial gain. Once these parameters are not selected properly, the de-noising effect of FxRLS will deteriorate. Moreover, the tracking performance of FxRLS for mutation is still restricted to a certain extent. To solve the above problems, this paper proposes a new proportional FxRLS (PFxRLS) algorithm. The forgetting factor and initial gain sensitivity are successfully reduced without introducing new turning parameters. The de-noising level and tracking performance have also been improved. Moreover, the momentum technique is introduced in PFxRLS to further improve its robustness and de-noising level. To ensure stability, its convergence condition is also discussed in this paper. The effectiveness of the proposed algorithms is illustrated by simulations and experiments with different user-defined parameters and time-varying noise environments.

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A Virtual Sensing Active Noise Control System Based on a Functional Link Neural Network for an Aircraft Seat Headrest

Mylonas

Erspamer

Yiakopoulos

et al. 2023

J. Vib. Eng. Technol.

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Purpose The goal of this work is to propose a local Active Noise Control system for an aircraft seat’s headrest. This system should be able to extend the quiet zone beyond the physical microphones that can be placed behind the passenger’s head due to space constraints. Furthermore, the control algorithm should perform efficiently when non-linear phenomena exist during the sound propagation. Methods A functional link neural network and a computationally efficient Multiple Input Multiple Output approach are used for the system’s implementation. The quiet zone is extended by linearly estimating the acoustic pressure in front of the headrest surface. The main novelty of this paper is the combination of these methods in an attempt to improve acoustic pressure attenuation performance, while keeping computational complexity low. Results The proposed control algorithm has been evaluated through numerical simulations, including Finite Element Method and experimental tests at an aircraft cabin mock-up. The results show that for a real-world acoustic disturbance, a 10 dB reduction in sound pressure level was achieved 10 cm away from the headrest surface. In addition, the attenuation of some harmonics can reach 20 dB and in most cases is bigger than the linear FxLMS algorithm. Conclusions To summarize, it has been demonstrated that a multichannel functional link neural network using a simple virtual sensing technique can efficiently attenuate synthesized and real world acoustic disturbances captured in a tilt-rotor aircraft’s cabin. Finally, it can create an adequate quiet zone for gentle head movement while maintaining its stability.

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An extrapolation-based virtual sensing technique of improving the control performance of the FxLMS algorithm in a maritime environment

Cited by 7 publications

References 18 publications

A Convolutional Fuzzy Neural Network Active Noise Cancellation Approach without Error Sensors for Autonomous Rail Rapid Transit

A Convolutional Fuzzy Neural Network Active Noise Cancellation Approach without Error Sensors for Autonomous Rail Rapid Transit

A New Proportionate Filtered-x RLS Algorithm for Active Noise Control System

A Virtual Sensing Active Noise Control System Based on a Functional Link Neural Network for an Aircraft Seat Headrest

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