Active vibration control using nonlinear auto-regressive neural network to identify secondary channel
The power unit on board the ship generates periodic low-frequency vibration that affects the normal operation of the equipment on board, and the adaptive feedforward control algorithm can effectively suppress such harmful vibration noise. But the adaptive feedforward control algorithm needs to obtai...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2023-12-01
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| Series: | Journal of Low Frequency Noise, Vibration and Active Control |
| Online Access: | https://doi.org/10.1177/14613484231186704 |
| _version_ | 1797627510976937984 |
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| author | Song Chun-sheng Xiong Xue-chun Yang Qi Jia Bo Chen Bo-yuan Liang Ya-ru Fang Hai-ning |
| author_facet | Song Chun-sheng Xiong Xue-chun Yang Qi Jia Bo Chen Bo-yuan Liang Ya-ru Fang Hai-ning |
| author_sort | Song Chun-sheng |
| collection | DOAJ |
| description | The power unit on board the ship generates periodic low-frequency vibration that affects the normal operation of the equipment on board, and the adaptive feedforward control algorithm can effectively suppress such harmful vibration noise. But the adaptive feedforward control algorithm needs to obtain the identification model of the secondary channels, and the frequency domain least squares method based on the linear Extended auto-regressive model (ARX) is difficult to obtain the identification model with nonlinear characteristics. The nonlinear auto-regressive model (NARX) adds nonlinear mapping layers to the topology of the ARX model to enhance the identification capability of the NARX model for complex systems. In this paper, a block diagram of the Fx-LMS feedforward control algorithm based on the NARX model is proposed, then the initial parameters of the NARX neural network are optimized using the Quantum Particle Swarm Optimization (QPSO) algorithm and the secondary channel is identified, and the identification results show that the accuracy of identifying the secondary channel using the NARX neural network is higher than that of the ARX model. The simulation and experimental results show that the vibration damping effect of the proposed method is better than the traditional Fx-LMS method for both single-line spectrum and multi-line spectrum periodic low-frequency disturbances, which provides a new method for the suppression of periodic low-frequency disturbances. |
| first_indexed | 2024-03-11T10:25:20Z |
| format | Article |
| id | doaj.art-09ec353ba1e6481289f54cf2e2cc0e58 |
| institution | Directory Open Access Journal |
| issn | 1461-3484 2048-4046 |
| language | English |
| last_indexed | 2024-03-11T10:25:20Z |
| publishDate | 2023-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Low Frequency Noise, Vibration and Active Control |
| spelling | doaj.art-09ec353ba1e6481289f54cf2e2cc0e582023-11-15T14:05:04ZengSAGE PublishingJournal of Low Frequency Noise, Vibration and Active Control1461-34842048-40462023-12-014210.1177/14613484231186704Active vibration control using nonlinear auto-regressive neural network to identify secondary channelSong Chun-shengXiong Xue-chunYang QiJia BoChen Bo-yuanLiang Ya-ruFang Hai-ningThe power unit on board the ship generates periodic low-frequency vibration that affects the normal operation of the equipment on board, and the adaptive feedforward control algorithm can effectively suppress such harmful vibration noise. But the adaptive feedforward control algorithm needs to obtain the identification model of the secondary channels, and the frequency domain least squares method based on the linear Extended auto-regressive model (ARX) is difficult to obtain the identification model with nonlinear characteristics. The nonlinear auto-regressive model (NARX) adds nonlinear mapping layers to the topology of the ARX model to enhance the identification capability of the NARX model for complex systems. In this paper, a block diagram of the Fx-LMS feedforward control algorithm based on the NARX model is proposed, then the initial parameters of the NARX neural network are optimized using the Quantum Particle Swarm Optimization (QPSO) algorithm and the secondary channel is identified, and the identification results show that the accuracy of identifying the secondary channel using the NARX neural network is higher than that of the ARX model. The simulation and experimental results show that the vibration damping effect of the proposed method is better than the traditional Fx-LMS method for both single-line spectrum and multi-line spectrum periodic low-frequency disturbances, which provides a new method for the suppression of periodic low-frequency disturbances.https://doi.org/10.1177/14613484231186704 |
| spellingShingle | Song Chun-sheng Xiong Xue-chun Yang Qi Jia Bo Chen Bo-yuan Liang Ya-ru Fang Hai-ning Active vibration control using nonlinear auto-regressive neural network to identify secondary channel Journal of Low Frequency Noise, Vibration and Active Control |
| title | Active vibration control using nonlinear auto-regressive neural network to identify secondary channel |
| title_full | Active vibration control using nonlinear auto-regressive neural network to identify secondary channel |
| title_fullStr | Active vibration control using nonlinear auto-regressive neural network to identify secondary channel |
| title_full_unstemmed | Active vibration control using nonlinear auto-regressive neural network to identify secondary channel |
| title_short | Active vibration control using nonlinear auto-regressive neural network to identify secondary channel |
| title_sort | active vibration control using nonlinear auto regressive neural network to identify secondary channel |
| url | https://doi.org/10.1177/14613484231186704 |
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