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Full text: 11, 1888

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25 8 
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27.33 
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28.0 
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4 
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26 
30 
31 
31 
34 
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32 
31 
30 
29 
27.21 
26.4 
26.50 
25.50 
27.00 
25.25 
25.67 
25.67 
-24.0 
-24.0 
29.0 
27.0 
17 
5 
24 
24 
25 
27 
29 
30 
32 
32 
31 
29 
29 
28 
27 
28 
28.21 
28.6 
28 50 
28.00 
28.00 
27.50 
28.00 
28.00 
28.0 
28.0 
29.0 
28.0 
8 
6 
23 
23 
25 
28 
30 
32 
33 
34 
33 
32 
29 
29 
28 
29 
29.14 
29.2 
29.00 
29.25 
28.83 
28.75 
28.33 
28.67 
28.0 
28.5 
29.5 
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11 
7 
24 
24 
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31 
31 
30 
30 
30 
28 
27 
27 
27.86 
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27.33 
27.5 
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7 
8 
22 
23 
24 
25 
27 
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34 
33 
32 
31 
32 
29 
29 
28.79 
28.4 
28.75 
28.50 
28.67 
27.50 
28.00 
28.67 
27.5 
28.5 
29.0 
28.5 
12 
9 
25 
24 
26 
29 
30 
33 
32 
32 
31 
30 
30 
29 
29 
30 
29.29 
29.2 
29.00 
28.75 
29.33 
29.00 
28.67 
28.67 
28.0 
28.0 
30.0 
29.5 
8 
10 
+27 
26 
+31 
+33 
+39 
+39 
37 
+41 
+41 
+37 
+37 
+36 
+34 
+34 
+35.14 
+36.4 
+35.75 
+34.25 
+35.67 
+33.75 
32.23 
+33.67 
31.5 
+53.0 
+38.0 
+56.0 
15 
11 
24 
24 
25 
26 
28 
32 
32 
31 
31 
30 
29 
29 
30 
29 
28.57 
28.0 
28.00 
27.75 
27.83 
28.00 
28.33 
28.00 
28.0 
27.5 
28.5 
28.0 
8 
12 
25 
26 
25 
26 
27 
30 
31 
32 
32 
33 
30 
30 
29 
29 
28.93 
28.4 
28.75 
29.25 
28.17 
28.75 
28.67 
29.00 
28.5 
29.0 
28.5 
29.5 
8 
13 
25 
24 
26 
29 
30 
32 
+39 
+41 
+39 
+41 
+37 
31 
31 
32 
32.64 
32.4 
33.00 
+33.75 
31.17 
31.50 
31 67 
32.33 
31.5 
32.5 
33.5 
+56.0 
17 
14 
21 
21 
21 
24 
28 
30 
32 
34 
32 
31 
30 
29 
28 
28 
27.79 
28.2 
28.25 
27.50 
27.33 
26.50 
27.00 
27.67 
26.5 
27.5 
29.0 
27.5 
13 
15 
24 
24 
25 
25 
27 
30 
31 
32 
32 
32 
31 
30 
29 
30 
28.71 
28.2 
28.50 
28.25 
28.00 
27.75 
28.33 
28.67 
27.5 
28.0 
29.0 
28.5 
8 
16 
24 
23 
24 
25 
27 
30 
31 
33 
33 
32 
32 
32 
30 
30 
29 00 
28.4 
28.75 
28.25 
28.83 
27.50 
28.00 
28.67 
27.0 
28.0 
29.5 
28.5 
10 
17 
23 
23 
24 
26 
28 
32 
33 
33 
32 
31 
30 
30 
29 
29 
28.79 
28.4 
28.50 
28.25 
28.33 
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28.33 
28.33 
28.0 
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29.0 
28.5 
10 
15 
23 
23 
24 
25 
28 
31 
33 
34 
33 
32 
31 
31 
— 
— 
28.8 
29.00 
28.50 
28.50 
27.75 
— 
— 
28.0 
28.5 
29.5 
28.5 
11 
19 
21 
19 
21 
24 
29 
31 
20 
— 
21 
24 
24 
25 
26 
29 
31 
33 
34 
33 
33 
32 
31 
30 
29 
29.57 
29.6 
29.75 
29.25 
29.17 
28.50 
28.67 
29.00 
28.5 
29.0 
30.5 
29.5 
10 
22 
24 
24 
25 
27 
29 
32 
34 
36 
35 
34 
33 
31 
30 
31 
30.36 
30.2 
30.50 
27.75 
29.67 
29.25 
29.67 
30.33 
29.0 
30.0 
31.0 
30.5 
12 
23 
19 
19 
21 
25 
28 
32 
33 
35 
34 
33 
32 
32 
— 
— 
— 
28.4 
28.50 
28.00 
29.00 
27.25 
— 
— 
26.0 
27.0 
30.0 
29.0 
16 
24 
24 
24 
25 
28 
32 
34 
37 
+43 
— 
— 
— 
— 
— 
— 
— 
— 
— 
— 
— 
— 
— 
— 
30.5 
+33.5 
— 
19 
25 
24 
24 
25 
29 
+35 
+37 
36 
34 
34 
34 
33 
34 
32 
— 
— 
32.2 
31.50 
30.25 
32.33 
31.00 
— 
— 
30.0 
29.5 
34.0 
31.5 
13 
26 
26 
+27 
28 
28 
31 
33 
34 
34 
32 
31 
32 
30 
29 
29 
30.29 
31.0 
31.00 
30.00 
29.83 
29.75 
30.00 
30.00 
30.5 
30.5 
31.5 
29.5 
8 
27 
21 
20 
21 
23 
29 
32 
36 
34 
36 
35 
33 
31 
— 
* 
— 
29 0 
29.00 
28.00 
28.33 
27.50 
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— 
280 
27.0 
31.0 
29.0 
16 
28 
26 
26 
27 
29 
30 
33 
35 
37 
35 
35 
33 
32 
31 
30 
31.36 
31.2 
31.50 
31.75 
30.83 
30.75 
30.33 
31.00 
30.5 
31.5 
31.5 
32.0 
11 
29 
25 
25 
26 
28 
30 
34 
+39 
+41 
+39 
34 
33 
32 
32 
31 
32.07 
31.8 
32.25 
32.00 
30.50 
30.25 
31.67 
32.33 
32.0 
+33.0 
31.5 
31.0 
16 
30 
24 
25 
26 
28 
30 
33 
35 
37 
37 
+37 
34 
+35 
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33 
32.07 
31.2 
31.50 
31.75 
31.67 
30.75 
31.00 
31.67 
30.0 
31.0 
32.0 
32.5 
13 
29.43 
29.36 
29.44 
28.90 
29.06 
28.37 
28.61 
29.00 
28.18 
28.77 
30.31 
29.41 
12.14 
29.17 
29.09 
29.20 
28.65 
28.80 
28.34 
28.80 
28.79 
28.54 
28.54 
30.02 
29.13 
- 
22.55 
22.83 
24.14 
26.03 
29.14 
31.79 
33.49 
34.54 
33.59 
32.74 
31.59 
30.78 
29.75 
29.61 
29.47 
29.45 
29.53 
29.04 
29.06 
28.35 
28.64 
28.99 
23.16 
28.69 
30.37 
29.39 
11.99 
23.44 
23.40 
24.48 
26.40 
28.56 
31.33 
32.96 
33.38 
32.83 
32.04 
31.16 
30.40 
29.32 
29.41 
29.22 
29.01 
29.13 
28.81 
28.89 
28.29 
28.59 
28.73 
28.18 
28.39 
29.86 
29.22 
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/.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 line- search 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 implemen- tation 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
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