1 |
Aalbers, A., Waals, O.J., Tap, R.F., Davison, N.J., 2004. Wave feed forward DP and the effect on shuttle tanker operation. In: Dynamic Positioning Conference.
|
2 |
Du, Jialu, Hu, Xin, Krstic, Miroslav, Sun, Yuqing, 2016. Robust dynamic positioning of ships with disturbances under input saturation. Automatica.
|
3 |
Ziegler, J.G., Nathaniel, N.B., 1942. Optimum Settings for Automatic Controllers. ASME.
|
4 |
Orcina, 2019. Vessel Theory : wave drift and sum frequency loads [WWW Document]. URL. https://www.orcina.com/webhelp/OrcaFlex/Content/html/Vesseltheory,Wavedriftandsumfrequencyloads.htm. accessed 7.12.20.
|
5 |
Cho, K., Van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: ArXiv Preprint ArXiv:1406.1078, pp. 1724-1734. https://doi.org/10.3115/v1/d14-1179.
DOI
|
6 |
De Wit, C., 2009. Optimal Thrust Allocation Methods for Dynamic Positioning of Ships. Delft Univ. Technol. Netherlands.
|
7 |
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M., 2013. Playing atari with deep reinforcement learning. In: ArXiv Preprint ArXiv:1312.5602.
|
8 |
Song, S.S., Kim, S.H., Kim, H.S., Jeon, M.R., 2016. A study on the feedforward control algorithm for dynamic positioning system using ship motion prediction. J. Korean Soc. Mar. Environ. Saf. 22, 129-137. https://doi.org/10.7837/kosomes.2016.22.1.129.
DOI
|
9 |
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., 2014. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929-1958.
|
10 |
Tannuri, E.A., Agostinho, A.C., 2010. Dynamic positioning systems: An experimental analysis of sliding mode control. Contr. Eng. Pract.
|
11 |
Wang, Yu-Long, Han, Q.L., Fei, M.R., Peng, C., 2018. Network-Based TeS Fuzzy Dynamic Positioning Controller Design for Unmanned Marine Vehicles. IEEE Trans. cybern.
|
12 |
Hochreiter, S., Schmidhuber, J., 1997. Long short-term memory. Neural Comput. 9, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735.
DOI
|
13 |
Watkins, C.J.C.H., Dayan, P., 1992. Q-learning. Mach. Learn. 8, 279-292. https://doi.org/10.1007/bf00992698.
DOI
|
14 |
Yeung, R.W., 1983. International Workshop on Ship and Platform Motions.
|
15 |
Fossen, T.I., Perez, T., 2009. Kalman filtering for positioning and heading control of ships and offshore rigs: estimating the effects of waves, wind, and current. IEEE Contr. Syst. Mag. 29, 32-46. https://doi.org/10.1109/MCS.2009.934408.
DOI
|
16 |
Gao, Xiaoyang, Li, Tieshan, Shan, Qihe, Xiao, Yang, Yuan, Liang'en, Liu, Yifan, 2019. Online optimal control for dynamic positioning of vessels via time-based adaptive dynamic programming. Int. J. Ambient Intell. Humanized Comput.
|
17 |
Glorot, X., Bengio, Y., 2010. Understanding the difficulty of training deep feedforward neural networks. J. Mach. Learn. Res. 249-256.
|
18 |
IMCA, 2000. Specification for DP Capability Plots.
|
19 |
Kingma, D.P., Ba, J.L., 2015. Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations. International Conference on Learning Representations. ICLR.
|
20 |
Lee, D., Lee, S.J., Seo, Y.J., 2020. Application of recent developments in deep learning to ANN-based automatic berthing systems. Int. J. Eng. Technol. Innov. 10, 75-90.
DOI
|
21 |
Fossen, T.I., Grovlen, Aslaug, 1998. Nonlinear output feedback control of dynamically positioned ships using vectorial observer backstepping. IEEE Trans. Contr. Syst. Technol.
|
22 |
Lee, S.J., 2008. The Effects of LNG-Sloshing on the Global Responses of LNG-Carriers. Texas A&M University.
|
23 |
Li, Mingyang, Xie, Wenbo, Zhang, Jian, 2020. Anti-windup reconfigurable control for dynamic positioning vessel with thruster faults. Trans. Inst. Meas. Contr.
|