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- W2289666731 abstract "This paper addresses a neural network-based formation control of underactuated AUVs with limited torque input under environmental disturbances in three-dimensional space. For this purpose, a second-order dynamic model is developed based on a coordinate transformation for underactuated AUVs. Then, a saturated formation controller is proposed by employing saturation functions in order to bound closed-loop error variables. This technique effectively reduces the risk of actuators saturation by decreasing the amplitude of generated control signals. Multi-layer neural networks are combined with an adaptive robust control strategy to deal with the actuator saturation and model uncertainties including unknown vehicle parameters, approximation errors, and constant or time-varying environmental disturbances induced by waves and ocean currents. A Lyapunov synthesis is used to guarantee semi-global uniform ultimate boundedness of tracking errors for all AUVs. Finally, simulation results demonstrate the performance of the proposed formation controller." @default.
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- W2289666731 date "2016-06-01" @default.
- W2289666731 modified "2023-10-14" @default.
- W2289666731 title "Neural network formation control of underactuated autonomous underwater vehicles with saturating actuators" @default.
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- W2289666731 doi "https://doi.org/10.1016/j.neucom.2016.02.041" @default.
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