<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, we study the control problem of auto-berthing marine surface vessels (MSVs) within a predefined, finite time in the restricted waters of a port, in the face of internal and external uncertain dynamics and actuator faults. We first use radial basis function neural networks to reconstruct the internal uncertainties of the system; then, using the minimum learning parameter method, we transform the weights of the neural networks, the external disturbances of the system, and the bias fault factors into an indirect single-parameter neural learning mode. We also apply a robust depth information adaptation technique to estimate the upper bound on the composite disturbances online. Dynamic surface control technology alleviates the burden of virtual control derivative calculations. Finite-time convergence of the system is guaranteed by a predetermined finite-time function based on a time-based generator (TBG). Based on these methods, we design a finite-time fault-tolerant auto-berthing control scheme based on TBG. The stability of the system is analysed based on Lyapunov stability theory. Finally, we verify the effectiveness of the proposed control scheme through simulation.</p>
</abstract>

In this paper, we study the control problem of auto-berthing marine surface vessels (MSVs) within a predefined, finite time in the restricted waters of a port, in the face of internal and external uncertain dynamics and actuator faults. We first use radial basis function neural networks to reconstruct the internal uncertainties of the system; then, using the minimum learning parameter method, we transform the weights of the neural networks, the external disturbances of the system, and the bias fault factors into an indirect single-parameter neural learning mode. We also apply a robust depth information adaptation technique to estimate the upper bound on the composite disturbances online. Dynamic surface control technology alleviates the burden of virtual control derivative calculations. Finite-time convergence of the system is guaranteed by a predetermined finite-time function based on a time-based generator (TBG). Based on these methods, we design a finite-time fault-tolerant auto-berthing control scheme based on TBG. The stability of the system is analysed based on Lyapunov stability theory. Finally, we verify the effectiveness of the proposed control scheme through simulation.

Auto-berthing Control for MSVs with a Time-based Generator under Actuator Faults: A Concise Neural Single-Parameter Approach

Polish Maritime Research

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

In this paper, we study the control problem of auto-berthing marine surface vessels (MSVs) within a predefined, finite time in the restricted waters of a port,...

Auto-berthing Control for MSVs with a Time-based Generator under Actuator Faults: A Concise Neural Single-Parameter Approach

Publié en ligne: 22 juin 2024

Pages: 92 - 98

DOI: https://doi.org/10.2478/pomr-2024-0025

Mots clés
Auto-berthing control, Time-based generator, Minimum learning parameter, Finite-time control, Concise neural single-parameter, Actuator faults

© 2024 Liping Chen., published by Sciendo

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

Auto-berthing Control for MSVs with a Time-based Generator under Actuator Faults: A Concise Neural Single-Parameter Approach

Publié en ligne: 22 juin 2024

Pages: 92 - 98

DOI: https://doi.org/10.2478/pomr-2024-0025

Mots clésAuto-berthing control, Time-based generator, Minimum learning parameter, Finite-time control, Concise neural single-parameter, Actuator faults

© 2024 Liping Chen., published by Sciendo

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

Mots clés
Auto-berthing control, Time-based generator, Minimum learning parameter, Finite-time control, Concise neural single-parameter, Actuator faults