Adptive heading control of underactuated unmanned surface vehicle based on improved backpropagation neural network

• Autorzy: Dong Zaopeng , Li Jiakang , Liu Wei , Zhang Haisheng , Qi Shijie , Zhang Zhengqi

Identyfikatory

DOI

10.2478/pomr-2023-0006

• Języki publikacji: EN

Abstrakty

EN

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%.

Słowa kluczowe

EN

underactuated unmanned surface vehicle; backpropagation neural network controller; heading control; hyperbolic tangent function

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2023
• Tom: nr 1
• Strony: 54--64
• Opis fizyczny: Bibliogr. 42 poz., rys., tab.

Twórcy

autor

Dong Zaopeng

• Key Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan University of Technology, Wuhan, China
• Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, China
• School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan, China

• https://orcid.org/0000-0002-8909-8395

autor

Li Jiakang

• Key Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan University of Technology, Wuhan, China
• School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan, China

• https://orcid.org/0000-0002-3568-1382

autor

Liu Wei

• China Institute of Marine Technology&Economy, Beijing, China

autor

Zhang Haisheng

• Key Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan University of Technology, Wuhan, China
• School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan, China

• https://orcid.org/0000-0002-3696-7948

autor

Qi Shijie

• Key Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan University of Technology, Wuhan, China
• School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan, China

• https://orcid.org/0000-0001-8477-043X

autor

Zhang Zhengqi

• Key Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan University of Technology, Wuhan, China
• School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan, China

• https://orcid.org/0000-0002-6756-4244

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).