Sliding mode adaptive control for ship path following with sideslip angle observer. (1st May 2022)
- Record Type:
- Journal Article
- Title:
- Sliding mode adaptive control for ship path following with sideslip angle observer. (1st May 2022)
- Main Title:
- Sliding mode adaptive control for ship path following with sideslip angle observer
- Authors:
- Zhang, Hugan
Zhang, Xianku
Bu, Renxiang - Abstract:
- Abstract: An adaptive sliding mode control algorithm based on radial basis function neural networks(RBF-NNs) is proposed to solve the problems of external disturbances, internal model uncertainty and sideslip angle unknown in the process of underactuated ship path following. Firstly, the three dimensional path following control is transformed into one dimensional heading control by backstepping algorithm. Because the sideslip angle is unknown, the RBF-NNs are used to estimate the sideslip angle. Taking the ship's heading error and the yaw rate error as the input of neural network, 10 hidden layers are used to classify the input linearly, and an adaptive law is designed to update the weight online to solve the problem of unknown sideslip angle. The linear extended state observer (LESO) is introduced to estimate the total disturbances in the path following process, so as to solve the problem of external disturbances and internal model uncertainty. Finally, the mathematical model group(MMG) model is used for simulation. The simulation results show that the designed controller can follow the preset waypoints well, and the RBF-NNs can estimate the sideslip angle well, LESO can well observe the total disturbances. It is demonstrate that this scheme can provide a potential reference for the path following of underactuated ships with unknown sideslip and disturbances. Highlights: The three degree of freedom ship path following control is converted to heading control by backsteppingAbstract: An adaptive sliding mode control algorithm based on radial basis function neural networks(RBF-NNs) is proposed to solve the problems of external disturbances, internal model uncertainty and sideslip angle unknown in the process of underactuated ship path following. Firstly, the three dimensional path following control is transformed into one dimensional heading control by backstepping algorithm. Because the sideslip angle is unknown, the RBF-NNs are used to estimate the sideslip angle. Taking the ship's heading error and the yaw rate error as the input of neural network, 10 hidden layers are used to classify the input linearly, and an adaptive law is designed to update the weight online to solve the problem of unknown sideslip angle. The linear extended state observer (LESO) is introduced to estimate the total disturbances in the path following process, so as to solve the problem of external disturbances and internal model uncertainty. Finally, the mathematical model group(MMG) model is used for simulation. The simulation results show that the designed controller can follow the preset waypoints well, and the RBF-NNs can estimate the sideslip angle well, LESO can well observe the total disturbances. It is demonstrate that this scheme can provide a potential reference for the path following of underactuated ships with unknown sideslip and disturbances. Highlights: The three degree of freedom ship path following control is converted to heading control by backstepping algorithm. The sideslip angle observer is designed by RBF-NNs to compensate virtual heading. The LESO is introduced to observe the external disturbance of the ship. … (more)
- Is Part Of:
- Ocean engineering. Volume 251(2022)
- Journal:
- Ocean engineering
- Issue:
- Volume 251(2022)
- Issue Display:
- Volume 251, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 251
- Issue:
- 2022
- Issue Sort Value:
- 2022-0251-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-05-01
- Subjects:
- Ship motion control -- Sliding mode control -- Disturbances compensation -- RBF-NNs -- Sideslip angle observer
Ocean engineering -- Periodicals
Ocean engineering
Periodicals
620.4162 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00298018 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.oceaneng.2022.111106 ↗
- Languages:
- English
- ISSNs:
- 0029-8018
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6231.280000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 21246.xml