Global Adaptive Neural Network Control of Underactuated Autonomous Underwater Vehicles with Parametric Modeling Uncertainty
Global Adaptive Neural Network Control of Underactuated Autonomous Underwater Vehicles with Parametric Modeling Uncertainty
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DOI：
10.1002/asjc.1819
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发表时间：
2018-05
影响因子：
2.4
通讯作者：
Zheping Yan;Man Wang;Jian Xu
中科院分区：
文献类型：
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作者：
Zheping Yan;Man Wang;Jian Xu
研究了一类欠驱动水下机器人在建模参数存在较大不确定性时的全局自适应神经网络控制问题。由于控制输入不能直接作用在横摇和垂荡方向上，因此使用这里定义的两个虚拟速度加上由推进器和方向舵提供的三个实际控制动作来实现系统误差收敛到零附近。受轨迹跟踪的真实的时间特性的启发，所提出的控制器呈现出显著的优势，因为它只包含一个在线更新的自适应参数，而不是神经网络的权重。此外，我们还考虑了实际情况，即当位置跟踪误差初始值突变时，飞行器的速度可能会出现急剧的速度突变，从而导致推力器饱和。引入生物启发模型来平滑虚拟速度命令，使得车辆满足控制输入和速度约束。最后，通过仿真比较验证了该方案的有效性.
This paper focuses on global adaptive neural network control for a class of underactuated autonomous underwater vehicles in the presence of possibly large modeling parametric uncertainty. As the control inputs cannot directly act in the sway and heave directions, two virtual velocities defined here, plus three actual control actions provided by the thrusters and rudders, are used to achieve the convergence of the system errors to around zero. Motivated by real‐time characteristics in the trajectory tracking, the proposed controller presents a significant advantage because it contains only one adaptive parameter to be updated online rather than the neural network weights. In addition, we also consider the practical situation that the velocities of the vehicle may experience sharp speed jumps when the position tracking errors initially change suddenly, which always results in thruster saturation. The biologically inspired model is introduced to smooth the virtual velocity commands such that the vehicle satisfies the control input and velocity constraints. Finally, comparison simulations are given to show the effectiveness of the proposed scheme.