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- W4387149241 abstract "In order to improve the depth control method of underwater vehicle and reduce the interference and influence of external factors such as currents, this paper analyzes the motion control and dynamics modeling for an independently designed cable patrol autonomous underwater vehicle(CP-AUV), and proposes a control algorithm for CP-AUV that integrates the Actor-Critic algorithm and PID control. The algorithm uses traditional PID control as the controller of the CP-AUV and uses the Actor-Critic algorithm to adjust the PID control parameters to make it better adapted to different disturbances and different adjustment depths. The effectiveness of the method is verified by simulation, and compared with the traditional PID control, the reinforcement learning-based PID control retains the simple and practical advantages of PID, but can also adjust the parameters according to the motion environment, which enhances the robustness of the algorithm and the accuracy of parameter tracking while improving the control accuracy." @default.
- W4387149241 created "2023-09-29" @default.
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- W4387149241 date "2023-08-19" @default.
- W4387149241 modified "2023-09-30" @default.
- W4387149241 title "Depth Control of Cable Patrol Autonomous Underwater Vehicle Based on Reinforcement Learning" @default.
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- W4387149241 doi "https://doi.org/10.1109/wrcsara60131.2023.10261868" @default.
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