REFERENCES
1. Park HC, Chakir S, Kim YB, Lee DH. A Robust payload control system design for offshore cranes: experimental study. Electronics 2021;10:462.
2. Huster A, Bergstrom H, Gosior J, White D. Design and operational performance of a standalone passive heave compensation system for a work class ROV. In: OCEANS 2009. IEEE; 2009. pp. 1–8.
3. Ni J, Liu S, Wang M, Hu X, Dai Y. The simulation research on passive heave compensation system for deep sea mining. In: 2009 International Conference on Mechatronics and Automation. IEEE; 2009. pp. 5111–16.
4. Zhu M, Zhang P, Zhu C, Jia X. Dynamic analysis and optimal control of the landing process of the offshore installation. Adv Mech Eng 2017; doi: 10.1177/1687814017727971.
5. Mackojć A, Chiliński B. Preliminary modelling methodology of a coupled payload-vessel system for offshore lifts of light and heavyweight objects. Bulletin of the Polish Academy of Sciences: Technical Sciences 2022;70: e139003. Available from:.
6. Idres M, Youssef K, Mook D, Nayfeh A. A nonlinear 8-DOF coupled crane-ship dynamic model. In: 44th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference; 2003. p. 1855.
7. Ellermann K, Kreuzer E, Markiewicz M. Nonlinear dynamics of floating cranes. Nonlinear Dynamics 2002;27:107-83.
8. Cha JH, Roh MI, Lee KY. Dynamic response simulation of a heavy cargo suspended by a floating crane based on multibody system dynamics. Ocean Engineering 2010;37:1273-91.
9. Spong MW, Hutchinson S, Vidyasagar M, et al. Robot modeling and control. vol. 3. Wiley New York; 2006.
10. Williams LA. Modelling, Simulation and Control of offshore crane Develop a kinematic and dynamic crane model and study of several control designs [MastersThesis]. Universitetet i Agder; University of Agder. Norway; 2018. Available from: http://hdl.handle.net/11250/2564033.
11. Sutton RS, Barto AG. Reinforcement learning: An introduction. MIT press; 2018. Available from: https://mitpress.mit.edu/9780262039246/.
12. Andersson J, Bodin K, Lindmark D, Servin M, Wallin E. Reinforcement Learning Control of a Forestry Crane Manipulator. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; 2021. pp. 2121–26.
13. Gaudet B, Linares R, Furfaro R. Deep reinforcement learning for six degree-of-freedom planetary landing. Adv Space Res 2020;65:1723-41.
14. Sun X, Xie Z. Reinforcement Learning-Based Backstepping Control for Container Cranes. Mat Pro Eng 2020;2020.
15. Ding M. Reinforcement Learning For Offshore Crane Set-down Operations [MastersThesis]. University of Croningen. Netherlands; 2018. Available from: https://www.ai.rug.nl/~mwiering/Thesis_Mingcheng_Ding.pdf.
16. Vazirizade SM. An intelligent integrated method for reliability estimation of offshore structures wave loading applied in time domain[PhdThesis]. The University of Arizona. USA; 2019. Available from: https://repository.arizona.edu/handle/10150/636592.
17. Küchler S, Mahl T, Neupert J, Schneider K, Sawodny O. Active control for an offshore crane using prediction of the vessel's motion. IEEE/ASME Transactions on Mechatronics 2010;16:297-309.
18. Hasselt H. Double Q-learning. Advances in neural information processing systems 2010;23. Available from: https://proceedings.neurips.cc/paper/2010/hash/091d584fced301b442654dd8c23b3fc9-Abstract.html.
19. Zhang C, Vinyals O, Munos R, Bengio S. A study on overfitting in deep reinforcement learning. arXiv preprint arXiv: 180406893 2018.