3D trajectory tracking control of an underactuated AUV based on adaptive neural network dynamic surface
3D trajectory tracking control of an underactuated AUV based on adaptive neural network dynamic surface
复制标题
基于自适应神经网络动态面的欠驱动AUV 3D轨迹跟踪控制
DOI：
10.1504/ijvd.2020.115864
复制
发表时间：
2020
影响因子：
0.5
通讯作者：
Xingru Qu
中科院分区：
文献类型：
--
作者：
Xiao Liang;Zhao Zhang;Xingru Qu
本文介绍了在不确定的模型参数和未知的外部干扰下，未知的自动驾驶水下车辆（AUV）的3D轨迹跟踪控制。基于神经网络和自适应技术的动态表面控制方案
This paper addresses the 3D trajectory tracking control of an underactuated autonomous underwater vehicle (AUV) under uncertain model parameters and unknown external disturbances. A dynamic surface control scheme based on neural network and adaptive technique is proposed. In controller design, the first-order integral filters are employed to estimate derivative of virtual control, which avoid repeated derivative of virtual control. To deal with the effect of unknown external disturbances and uncertain model parameters, the neural network and adaptive technique are combined to approximate unknown nonlinear functions. All of the error signals in the closeloop system are uniformly ultimately bounded based on Lyapunov stability theory. Simulation studies and comparisons with adaptive dynamic surface control scheme illustrate the effectiveness and superiority of the proposed control scheme.