A data-driven method for optimal control of ship motions for safe crew transfer to offshore wind turbines. (September 2019)
- Record Type:
- Journal Article
- Title:
- A data-driven method for optimal control of ship motions for safe crew transfer to offshore wind turbines. (September 2019)
- Main Title:
- A data-driven method for optimal control of ship motions for safe crew transfer to offshore wind turbines
- Authors:
- Farzanegan, Behzad
Esmailian, Ehsan
Menhaj, Mohammad Bagher - Abstract:
- Highlights: The problem of safely transfer of personnel and equipment from a ship to an offshore wind turbine in order to guarantee safe operation and maintenance (O&M) is solved. A novel neural network (NN) observer-based optimal control (NOPC) scheme is proposed to tackle unknown dynamics, disturbances and non-symmetric control input constraints. Motion control of the ship is done based on analyzing the ship sensor data only. Abstract: Due to uncertainties and random behavior of sea loads, presenting an accurate hydrodynamic analysis for motion control of offshore ships is a big challenge. This paper aims to propose a novel method for motion control of a crew transfer vessel (CTV) in order to ensure safe crew transfer to an offshore wind turbine (OWT). For this purpose, we propose a novel neural network observer-based optimal control (NOPC) scheme to tackle unknown dynamics and disturbances, nonlinear effects, and non-symmetric control input saturation constraints. Accordingly, the neural network (NN) structure addresses the Hamilton-Jacobi-Bellman (HJB) equation and forms an optimal control signal remaining in the saturation bounds. The Lyapunov theory guarantees the Ultimately Uniformly Boundedness (UUB) of all signals of the closed-loop system. The high performance of the presented method is demonstrated in regular waves with high frequency in comparison with the previous studies. It is worth mentioning that there are not any limitations to implement the adoptedHighlights: The problem of safely transfer of personnel and equipment from a ship to an offshore wind turbine in order to guarantee safe operation and maintenance (O&M) is solved. A novel neural network (NN) observer-based optimal control (NOPC) scheme is proposed to tackle unknown dynamics, disturbances and non-symmetric control input constraints. Motion control of the ship is done based on analyzing the ship sensor data only. Abstract: Due to uncertainties and random behavior of sea loads, presenting an accurate hydrodynamic analysis for motion control of offshore ships is a big challenge. This paper aims to propose a novel method for motion control of a crew transfer vessel (CTV) in order to ensure safe crew transfer to an offshore wind turbine (OWT). For this purpose, we propose a novel neural network observer-based optimal control (NOPC) scheme to tackle unknown dynamics and disturbances, nonlinear effects, and non-symmetric control input saturation constraints. Accordingly, the neural network (NN) structure addresses the Hamilton-Jacobi-Bellman (HJB) equation and forms an optimal control signal remaining in the saturation bounds. The Lyapunov theory guarantees the Ultimately Uniformly Boundedness (UUB) of all signals of the closed-loop system. The high performance of the presented method is demonstrated in regular waves with high frequency in comparison with the previous studies. It is worth mentioning that there are not any limitations to implement the adopted strategy for other offshore applications. … (more)
- Is Part Of:
- Applied ocean research. Volume 90(2019)
- Journal:
- Applied ocean research
- Issue:
- Volume 90(2019)
- Issue Display:
- Volume 90, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 90
- Issue:
- 2019
- Issue Sort Value:
- 2019-0090-2019-0000
- Page Start:
- Page End:
- Publication Date:
- 2019-09
- Subjects:
- Data-driven method -- Optimal control system -- Motion control -- Neural networks -- Safe offshore operations -- Unknown dynamics
Ocean engineering -- Periodicals
620.416205 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01411187 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.apor.2019.06.004 ↗
- Languages:
- English
- ISSNs:
- 0141-1187
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1576.240000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 11351.xml