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

<p>An adaptive neural network (NN) event-triggered trajectory tracking control scheme based on finite time convergence is proposed to address the problem of trajectory tracking control of underdriven surface ships. In this scheme, both NNs and minimum learning parameters (MLPS) are applied. The internal and external uncertainties are approximated by NNs. To reduce the computational complexity, MLPs are used in the proposed controller. An event-triggered technique is then incorporated into the control design to synthesise an adaptive NN-based event-triggered controller with finite-time convergence. Lyapunov theory is applied to prove that all signals are bounded in the tracking system of underactuated vessels, and to show that Zeno behavior can be avoided. The validity of this control scheme is determined based on simulation results, and comparisons with some alternative schemes are presented.</p>
</abstract>

An adaptive neural network (NN) event-triggered trajectory tracking control scheme based on finite time convergence is proposed to address the problem of trajectory tracking control of underdriven surface ships. In this scheme, both NNs and minimum learning parameters (MLPS) are applied. The internal and external uncertainties are approximated by NNs. To reduce the computational complexity, MLPs are used in the proposed controller. An event-triggered technique is then incorporated into the control design to synthesise an adaptive NN-based event-triggered controller with finite-time convergence. Lyapunov theory is applied to prove that all signals are bounded in the tracking system of underactuated vessels, and to show that Zeno behavior can be avoided. The validity of this control scheme is determined based on simulation results, and comparisons with some alternative schemes are presented.

Event-Triggered Adaptive Neural Network Trajectory Tracking Control For Underactuated Ships Under Uncertain Disturbance

Polish Maritime Research

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

An adaptive neural network (NN) event-triggered trajectory tracking control scheme based on finite time convergence is proposed to address the problem of...

Event-Triggered Adaptive Neural Network Trajectory Tracking Control For Underactuated Ships Under Uncertain Disturbance

Online veröffentlicht: 10. Okt. 2023

Seitenbereich: 119 - 131

DOI: https://doi.org/10.2478/pomr-2023-0045

Schlüsselwörter
event-triggered control, underactuated marine surface vessels, adaptive neural network, trajectory tracking, finite-time

© 2023 Wenxue Su et al., published by Sciendo

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

Event-Triggered Adaptive Neural Network Trajectory Tracking Control For Underactuated Ships Under Uncertain Disturbance

Online veröffentlicht: 10. Okt. 2023

Seitenbereich: 119 - 131

DOI: https://doi.org/10.2478/pomr-2023-0045

Schlüsselwörterevent-triggered control, underactuated marine surface vessels, adaptive neural network, trajectory tracking, finite-time

© 2023 Wenxue Su et al., published by Sciendo

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

Schlüsselwörter
event-triggered control, underactuated marine surface vessels, adaptive neural network, trajectory tracking, finite-time