<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>This paper deals with two topics: roll predictions of marine vessels with machine-learning methods and parameter estimation of unknown ocean disturbances when the amplitude, frequency, offset, and phase are difficult to estimate. This paper aims to prevent the risky roll motions of marine vessels exposed to harsh circumstances. First of all, this study demonstrates complex dynamic phenomena by utilising a bifurcation diagram, Lyapunov exponents, and a Poincare section. Without any observers, an adaptive identification applies these four parameters to the globally exponential convergence using linear second-order filters and parameter estimation errors. Then, a backstepping controller is employed to make an exponential convergence of the state variables to zero. Finally, this work presents the prediction of roll motion using reservoir computing (RC). As a result, the RC process shows good performance for chaotic time series prediction in future states. Thus, the poor predictability of Lyapunov exponents may be overcome to a certain extent, with the help of machine learning. Numerical simulations validate the dynamic behaviour and the efficacy of the proposed scheme.</p>
</abstract>

This paper deals with two topics: roll predictions of marine vessels with machine-learning methods and parameter estimation of unknown ocean disturbances when the amplitude, frequency, offset, and phase are difficult to estimate. This paper aims to prevent the risky roll motions of marine vessels exposed to harsh circumstances. First of all, this study demonstrates complex dynamic phenomena by utilising a bifurcation diagram, Lyapunov exponents, and a Poincare section. Without any observers, an adaptive identification applies these four parameters to the globally exponential convergence using linear second-order filters and parameter estimation errors. Then, a backstepping controller is employed to make an exponential convergence of the state variables to zero. Finally, this work presents the prediction of roll motion using reservoir computing (RC). As a result, the RC process shows good performance for chaotic time series prediction in future states. Thus, the poor predictability of Lyapunov exponents may be overcome to a certain extent, with the help of machine learning. Numerical simulations validate the dynamic behaviour and the efficacy of the proposed scheme.

Roll Prediction and Parameter Identification of Marine Vessels Under Unknown Ocean Disturbances

Division of Navigation & Information System

Department of Engineering Technology, Gadomski School of Engineering

Polish Maritime Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper deals with two topics: roll predictions of marine vessels with machine-learning methods and parameter estimation of unknown ocean disturbances when...

Roll Prediction and Parameter Identification of Marine Vessels Under Unknown Ocean Disturbances

Data publikacji: 29 mar 2024

Zakres stron: 4 - 15

DOI: https://doi.org/10.2478/pomr-2024-0001

Słowa kluczowe
Non-periodic roll motions, dynamic analysis, reservoir-computing, parameter estimation, periodic disturbances, backstepping

© 2024 Sang-Do Lee et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Roll Prediction and Parameter Identification of Marine Vessels Under Unknown Ocean Disturbances

Data publikacji: 29 mar 2024

Zakres stron: 4 - 15

DOI: https://doi.org/10.2478/pomr-2024-0001

Słowa kluczoweNon-periodic roll motions, dynamic analysis, reservoir-computing, parameter estimation, periodic disturbances, backstepping

© 2024 Sang-Do Lee et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Non-periodic roll motions, dynamic analysis, reservoir-computing, parameter estimation, periodic disturbances, backstepping