Research on Underactuated USV Path Following Algorithm

Ge, Yi; Liu, Zhong; Zhang, Jianqiang; Dong, Jiao

doi:10.1109/itnec48623.2020.9085222

Cited by 9 publications

(2 citation statements)

References 4 publications

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“…Примеры навигационного векторного поля F: а -R 1 = 1, R 2 = 3, координаты препятствия (0,0); б -R 1 = 2, R 2 = 5 , координаты препятствия (1, -1) Навигационное векторное поле F позволяет двигаться по заданной траектории, обходя имеющиеся на пути статические препятствия, с учетом размера препятствия и заданной зоны маневрирования. Однако в связи с особенностями безэкипажного судна (Underactuated Vehicle) [13], на которое оказывает значительное воздействие внешняя среда (ветер, течения), возможен навал БЭС на препятствие. Для уменьшения вероятности подобного последствия навигационное векторное поле F может быть дополнено введением «отталкивающих» векторов в ближней области препятствия.…”

Section: результаты (Results)unclassified

Static Obstacles Avoidance Algorithm for Unmanned Ship

Dyda¹,

Pushkarev²,

Chumakova³

2021

Vestnik GUMRF imeni admirala S. O. Makarova

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Section: результаты (Results)unclassified

Static Obstacles Avoidance Algorithm for Unmanned Ship

Dyda¹,

Pushkarev²,

Chumakova³

2021

Vestnik GUMRF imeni admirala S. O. Makarova

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“…Combined ADRC and radial basis function (RBF) neural networks observed state values using an extended state observer, and the RBF neural networks were used to optimize the parameters in the ADRC control in a RBF-ADRC controller designed by Liu [33]. Yi [34] applied PID feedback control to the USV path following, and a control strategy containing heading control and velocity control is designed based on the line of sight algorithm. Separately from the heading control, the desired velocity is tracked.…”

Section: Introductionmentioning

confidence: 99%

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

Dong

Liu³

et al. 2023

Polish Maritime Research

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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%.

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