Precise Docking Control of AUV Based on Neural Network Adaptive Controller

Liu, Zhi; Wang, Xinliang; Guan, Xiawei; Ma, Zhesong; Tang, Pingpeng; Zheng, Chao

doi:10.1109/usys.2018.8778910

Cited by 1 publication

(2 citation statements)

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“…21 Docking control methods include a variety of improved PID controls, 17,22,23 sliding mode control, 24 backstepping control, 20 adaptive robust control law, 25 model-free robust control, 26 and neural network adaptive controller. 27 However, only a few studies have focused on a unified docking approach. This makes the systematic design and integral optimization of AUV docking difficult.…”

Section: Introductionmentioning

confidence: 99%

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Integrated approach to AUV docking based on nonlinear offset-free model predictive control

Shi

Wang

et al. 2022

Measurement and Control

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This study proposes an integrated docking approach for autonomous underwater vehicle (AUV), which directly generate control force and torque from visual guidance features to drive precise AUV docking. Accurate docking control has been constructed as nonlinear offset-free model predictive control (OFNMPC) problem to effectively deal with external disturbances, model mismatch, and various other constraints. For the problem of state and disturbance estimation required by OFNMPC, observers have been presented based on the extended Kalman filter (EKF) and moving horizon estimation (MHE). A visual observation model is proposed so that the observer can directly use visual measurements. OFNMPC was integrated with EKF and MHE, and the proposed OFNMPC-EKF and OFNMPC-MHE docking approaches were applied to an actual AUV model. The simulation results show that the proposed approaches could drive the AUV to complete accurate docking along a reasonable trajectory based on visual measurements. The navigation, guidance, and control functions of docking were effectively integrated. It was observed that in the presence of external disturbances and measurement noise, OFNMPC-EKF has better robustness.

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