/.R. Marx et al.

tonomous surface vessel for urban transportation. J. Field Rob. 40 (8), 1996-2009.
https://doi.org/10.1002/rob.22237
Wang, W., Hagemann, N., Ratti, C., Rus, D., 2021. Adaptive nonlinear model predictive
zontrol for autonomous surface vessels with largely varying payload. In: 2021 IEEE
{nternational Conference on Robotics and Automation (ICRA), pp. 7337-7343. https:
//doi.org/10.1109/1CRA48506.2021.9561331
Wirtensohn, S., Hamburger, O., Homburger, H., Kinjo, L.M., Reuter, J., 2021. Comparison
of advanced control strategies for automated docking. In: IFAC-PapersOnLine. Vol. 54.
pp- 295-300. https://doi.org/10.1016/j.ifacol.2021.10.107
Wächter, A., Biegler, L.T., 2006. On the implementation of an interior-point filter linesearch
 algorithm for large-scale nonlinear programming. Math. Program. (1), 25-57.
nttps://doi.org/10.1007/s10107-004-0559-v

Ocean Engineering 343 (2026) 123388

Zanelli, A., Domahidi, A., Jerez, J., Morari, M., 2017. Forces nlp: an efficient implementation
 of interior-point methods for multistage nonlinear nonconvex programs. Int. J.
Control 93, 1-26. https://doi.org/10.1080/00207179.2017.1316017
are, N., Brandoli, B., Sarvmaili, M., Soares, A., Matwin, S., 2021. Continuous control with
deep reinforcement learning for autonomous vessels. In: ACM Proceedings, Dalhousie
Jniversity.
Zheng, Y., Cheng, L., Zhu, Q., Xue, X., 2014. Trajectory tracking control of autonomous
underwater vehicle based on improved model predictive control. J. Mar. Sci. Appl. 13
(4), 456-462. https: //doi.org/10.1007/s11804-014-1257-7