Path following of ship based on sliding mode control with improved RBF neural network and virtual circle
To address the unmeasured velocity, external disturbance and internal model uncertainty for following the path of an under-actuated ship, the paper presents a sliding mode control method based on the radial basis function(RBF) neural network and the velocity observer. To enhance the RBF performance...
| Format: | Article |
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| Language: | zho |
| Published: |
EDP Sciences
2021-02-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2021/01/jnwpu2021391p216/jnwpu2021391p216.html |
| _version_ | 1797426764318769152 |
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| collection | DOAJ |
| description | To address the unmeasured velocity, external disturbance and internal model uncertainty for following the path of an under-actuated ship, the paper presents a sliding mode control method based on the radial basis function(RBF) neural network and the velocity observer. To enhance the RBF performance of approximating the unknown, an arc tangent function was exploited in the RBF neural network to update its weight values. Then, the nonlinear observer was built via the hyperbolic tangent function to deal with the unmeasured velocity of the ship. Furthermore, in order to avoid overshoots when the ship is moving to its way points, the virtual paths of a variable circle based on the turning angle were designed at the joints of the path of the ship to enhance its path following capability. Finally, the simulation results show that the sliding mode controller designed in the paper can force the ship to follow accurately the reference path in case of time-varying disturbances without measured velocity and enhance the path following performance of the ship and the accuracy of the RBF neural network, thus demonstrating its effectiveness. |
| first_indexed | 2024-03-09T08:34:37Z |
| format | Article |
| id | doaj.art-0abf3a2e399c438eaff8a2510a7d4e17 |
| institution | Directory Open Access Journal |
| issn | 1000-2758 2609-7125 |
| language | zho |
| last_indexed | 2024-03-09T08:34:37Z |
| publishDate | 2021-02-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj.art-0abf3a2e399c438eaff8a2510a7d4e172023-12-02T18:33:36ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252021-02-0139121622310.1051/jnwpu/20213910216jnwpu2021391p216Path following of ship based on sliding mode control with improved RBF neural network and virtual circleTo address the unmeasured velocity, external disturbance and internal model uncertainty for following the path of an under-actuated ship, the paper presents a sliding mode control method based on the radial basis function(RBF) neural network and the velocity observer. To enhance the RBF performance of approximating the unknown, an arc tangent function was exploited in the RBF neural network to update its weight values. Then, the nonlinear observer was built via the hyperbolic tangent function to deal with the unmeasured velocity of the ship. Furthermore, in order to avoid overshoots when the ship is moving to its way points, the virtual paths of a variable circle based on the turning angle were designed at the joints of the path of the ship to enhance its path following capability. Finally, the simulation results show that the sliding mode controller designed in the paper can force the ship to follow accurately the reference path in case of time-varying disturbances without measured velocity and enhance the path following performance of the ship and the accuracy of the RBF neural network, thus demonstrating its effectiveness.https://www.jnwpu.org/articles/jnwpu/full_html/2021/01/jnwpu2021391p216/jnwpu2021391p216.htmlpath followingsliding mode controlradial basis function neural networknonlinear observer |
| spellingShingle | Path following of ship based on sliding mode control with improved RBF neural network and virtual circle Xibei Gongye Daxue Xuebao path following sliding mode control radial basis function neural network nonlinear observer |
| title | Path following of ship based on sliding mode control with improved RBF neural network and virtual circle |
| title_full | Path following of ship based on sliding mode control with improved RBF neural network and virtual circle |
| title_fullStr | Path following of ship based on sliding mode control with improved RBF neural network and virtual circle |
| title_full_unstemmed | Path following of ship based on sliding mode control with improved RBF neural network and virtual circle |
| title_short | Path following of ship based on sliding mode control with improved RBF neural network and virtual circle |
| title_sort | path following of ship based on sliding mode control with improved rbf neural network and virtual circle |
| topic | path following sliding mode control radial basis function neural network nonlinear observer |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2021/01/jnwpu2021391p216/jnwpu2021391p216.html |