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

<p>Aiming at the challenges to the accurate and stable heading control of underactuated unmanned surface vehicles arising from the nonlinear interference caused by the overlay and the interaction of multi interference, and also the uncertainties of model parameters, a heading control algorithm for an underactuated unmanned surface vehicle based on an improved backpropagation neural network is proposed. Based on applying optimization theory to realize that the underactuated unmanned surface vehicle tracks the desired yaw angle and maintains it, the improved momentum of weight is combined with an improved tracking differentiator to improve the robustness of the system and the dynamic property of the control. A hyperbolic tangent function is used to establish the nonlinear mappings an approximate method is adopted to summarize the general mathematical expressions, and the gradient descent method is applied to ensure the convergence. The simulation results show that the proposed algorithm has the advantages of strong robustness, strong anti-interference and high control accuracy. Compared with two commonly used heading control algorithms, the accuracy of the heading control in the complex environment of the proposed algorithm is improved by more than 50%.</p>
</abstract>

Aiming at the challenges to the accurate and stable heading control of underactuated unmanned surface vehicles arising from the nonlinear interference caused by the overlay and the interaction of multi interference, and also the uncertainties of model parameters, a heading control algorithm for an underactuated unmanned surface vehicle based on an improved backpropagation neural network is proposed. Based on applying optimization theory to realize that the underactuated unmanned surface vehicle tracks the desired yaw angle and maintains it, the improved momentum of weight is combined with an improved tracking differentiator to improve the robustness of the system and the dynamic property of the control. A hyperbolic tangent function is used to establish the nonlinear mappings an approximate method is adopted to summarize the general mathematical expressions, and the gradient descent method is applied to ensure the convergence. The simulation results show that the proposed algorithm has the advantages of strong robustness, strong anti-interference and high control accuracy. Compared with two commonly used heading control algorithms, the accuracy of the heading control in the complex environment of the proposed algorithm is improved by more than 50%.

Adptive Heading Control of Underactuated Unmanned Surface Vehicle Based on Improved Backpropagation Neural Network

Key Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education

Science and Technology on Underwater Vehicle Laboratory

Polish Maritime Research

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

Aiming at the challenges to the accurate and stable heading control of underactuated unmanned surface vehicles arising from the nonlinear interference caused...

Adptive Heading Control of Underactuated Unmanned Surface Vehicle Based on Improved Backpropagation Neural Network

Data publikacji: 19 kwi 2023

Zakres stron: 54 - 64

DOI: https://doi.org/10.2478/pomr-2023-0006

Słowa kluczowe
underactuated unmanned surface vehicle, backpropagation neural network controller, heading control, hyperbolic tangent function

© 2023 Zaopeng Dong et al., published by Sciendo

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

Adptive Heading Control of Underactuated Unmanned Surface Vehicle Based on Improved Backpropagation Neural Network

Data publikacji: 19 kwi 2023

Zakres stron: 54 - 64

DOI: https://doi.org/10.2478/pomr-2023-0006

Słowa kluczoweunderactuated unmanned surface vehicle, backpropagation neural network controller, heading control, hyperbolic tangent function

© 2023 Zaopeng Dong et al., published by Sciendo

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

Słowa kluczowe
underactuated unmanned surface vehicle, backpropagation neural network controller, heading control, hyperbolic tangent function