Adptive Heading Control of Underactuated Unmanned Surface Vehicle Based on Improved Backpropagation Neural Network-Reference-Cited by-全球学者库

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

Published:2023-03-01 Issue:1 Volume:30 Page:54-64
ISSN:2083-7429
Container-title:Polish Maritime Research
language:en
Short-container-title:

Author:

Dong Zaopeng¹²³^ORCID,Li Jiakang¹³^ORCID,Liu Wei⁴,Zhang Haisheng¹³^ORCID,Qi Shijie¹³^ORCID,Zhang Zhengqi¹³^ORCID

Affiliation:

1. 1 Key Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education , Wuhan University of Technology , Wuhan , China

2. 2 Science and Technology on Underwater Vehicle Laboratory , Harbin Engineering University , Harbin , China

3. 3 School of Naval Architecture, Ocean and Energy Power Engineering , Wuhan University of Technology , Wuhan , China

4. 4 China Institute of Marine Technology&Economy , Beijing , China

Abstract

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%.

Publisher

Walter de Gruyter GmbH

Subject

Mechanical Engineering,Ocean Engineering

Link

https://www.sciendo.com/pdf/10.2478/pomr-2023-0006

Reference42 articles.

1. Y. L. Liao, Y. Li, K. W. Pan, et al., ‘Adaptive Multi-body Yawing Filter of Wave Driven Robot with Dynamic Response Amending,’ Ocean Engineering, vol. 242, pp. 1-11(110167), December 2021. doi: 10.1016/j.oceaneng.2021.110167.

2. G. Shao, Y. Ma, R. Malekian, et al., ‘A novel cooperative platform design for coupled USV-UAV systems,’ IEEE Transactions on Industrial Informatics, vol. 15, no. 9, pp. 4913-4922, April 2019. doi: 10.1109/TII.2019.2912024.

3. G. Q. Zhang, W. Yu, W. D. Zhang, et al., ‘Robust adaptive formation control of underactuated surface vehicles with the desired-heading amendment,’ Journal of Marine Science and Technology, vol. 27, pp. 138-150, June 2021. doi: 10.1007/s00773-021-00820-2.

4. Y. Peng, Y. Yang, J. X. Cui, et al., ‘Development of the USV ‘JingHai-I’ and sea trials in the southern Yellow Sea,’ Ocean Engineering, vol. 131, pp. 186-196, February 2017. doi: 10.1016/j.oceaneng.2016.09.001.

5. Y. L. Liao, Z. H. Jia, W. B. Zhang, et al., ‘Layered berthing method and experiment of unmanned surface vehicle based on multiple constraints analysis,’ Applied Ocean Research, vol. 86, no. 5, pp. 47-60, May 2019. doi: 10.1016/j.apor.2019.02.003.

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO ₂ emission;Energy Sources, Part A: Recovery, Utilization, and Environmental Effects;2023-07-09