Parameter identification of smart float diving model based on ASNLS algorithm
ObjectivesAiming at the challenge of accurate diving modeling of a smart float, an anti-saturation and noise least squares (ASNLS) algorithm is proposed in this paper to achieve diving multi-parameter identification and depth prediction. MethodsFirstly, the nonlinear motion characteristics of the sm...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Editorial Office of Chinese Journal of Ship Research
2024-02-01
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| Series: | Zhongguo Jianchuan Yanjiu |
| Subjects: | |
| Online Access: | http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03186 |
| _version_ | 1797201215644237824 |
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| author | Yiming ZHONG Caoyang YU Junjun CAO Baoheng YAO Lian LIAN |
| author_facet | Yiming ZHONG Caoyang YU Junjun CAO Baoheng YAO Lian LIAN |
| author_sort | Yiming ZHONG |
| collection | DOAJ |
| description | ObjectivesAiming at the challenge of accurate diving modeling of a smart float, an anti-saturation and noise least squares (ASNLS) algorithm is proposed in this paper to achieve diving multi-parameter identification and depth prediction. MethodsFirstly, the nonlinear motion characteristics of the smart float actuator were included in the gray box-based diving model to better fit the actual model, and the continuous diving motion equation was transformed into a discrete form to match the real-world discrete data sampling. Subsequently, the aforementioned discrete diving model was constructed into a correlation form to attenuate the influence of data noise. Finally, by adjusting the values of the covariance matrix, the designed diving parameter identification algorithm achieved resistance to data saturation. ResultsBased on the data of the South China Sea deep diving experiment of the smart float in 2021, diving model parameter identification and depth prediction are carried out. The results demonstrate that the ASNLS algorithm has faster convergence speed (31.8% higher than the least squares algorithm) and smaller depth prediction error (average absolute percentage errors less than 9% at different depths) than both the traditional least squares algorithm and supports the vector machine algorithm.ConclusionsConsequently, the ASNLS algorithm can provide effective support for the depth control and prediction of the smart float. |
| first_indexed | 2024-04-24T07:44:00Z |
| format | Article |
| id | doaj.art-dcf443c67b06425382869cb0067259a0 |
| institution | Directory Open Access Journal |
| issn | 1673-3185 |
| language | English |
| last_indexed | 2024-04-24T07:44:00Z |
| publishDate | 2024-02-01 |
| publisher | Editorial Office of Chinese Journal of Ship Research |
| record_format | Article |
| series | Zhongguo Jianchuan Yanjiu |
| spelling | doaj.art-dcf443c67b06425382869cb0067259a02024-04-19T05:29:51ZengEditorial Office of Chinese Journal of Ship ResearchZhongguo Jianchuan Yanjiu1673-31852024-02-01192132010.19693/j.issn.1673-3185.03186ZG3186Parameter identification of smart float diving model based on ASNLS algorithmYiming ZHONG0Caoyang YU1Junjun CAO2Baoheng YAO3Lian LIAN4School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, ChinaSchool of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, ChinaSchool of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, ChinaSchool of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, ChinaSchool of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, ChinaObjectivesAiming at the challenge of accurate diving modeling of a smart float, an anti-saturation and noise least squares (ASNLS) algorithm is proposed in this paper to achieve diving multi-parameter identification and depth prediction. MethodsFirstly, the nonlinear motion characteristics of the smart float actuator were included in the gray box-based diving model to better fit the actual model, and the continuous diving motion equation was transformed into a discrete form to match the real-world discrete data sampling. Subsequently, the aforementioned discrete diving model was constructed into a correlation form to attenuate the influence of data noise. Finally, by adjusting the values of the covariance matrix, the designed diving parameter identification algorithm achieved resistance to data saturation. ResultsBased on the data of the South China Sea deep diving experiment of the smart float in 2021, diving model parameter identification and depth prediction are carried out. The results demonstrate that the ASNLS algorithm has faster convergence speed (31.8% higher than the least squares algorithm) and smaller depth prediction error (average absolute percentage errors less than 9% at different depths) than both the traditional least squares algorithm and supports the vector machine algorithm.ConclusionsConsequently, the ASNLS algorithm can provide effective support for the depth control and prediction of the smart float.http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03186smart floatparameter identificationantisaturation and noise least squares (asnls) algorithmmotion predictiondata saturation |
| spellingShingle | Yiming ZHONG Caoyang YU Junjun CAO Baoheng YAO Lian LIAN Parameter identification of smart float diving model based on ASNLS algorithm Zhongguo Jianchuan Yanjiu smart float parameter identification antisaturation and noise least squares (asnls) algorithm motion prediction data saturation |
| title | Parameter identification of smart float diving model based on ASNLS algorithm |
| title_full | Parameter identification of smart float diving model based on ASNLS algorithm |
| title_fullStr | Parameter identification of smart float diving model based on ASNLS algorithm |
| title_full_unstemmed | Parameter identification of smart float diving model based on ASNLS algorithm |
| title_short | Parameter identification of smart float diving model based on ASNLS algorithm |
| title_sort | parameter identification of smart float diving model based on asnls algorithm |
| topic | smart float parameter identification antisaturation and noise least squares (asnls) algorithm motion prediction data saturation |
| url | http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03186 |
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