In the presence of uncertain dynamic terms and external disturbances, the problem of trajectory tracking with application to an underactuated underwater vehicle is addressed in this paper. Based on Lyapunov theory and properties of neural networks, a nonlinear neural controller is designed, where multilayer neural networks are adopted to approximate the unmodeled dynamic terms and external disturbances. In order to confine the values of estimated weights within predefined bounds, smooth projection functions are employed. Moreover, measurement noises are considered so as to simulate realistic operation scenario, while filters are designed to get cleaner states. From the stability analysis, it is proven that the tracking errors are globally uniformly ultimately bounded. Numerical examples are provided to demonstrate the robustness of the controller in the presence of unmodeled terms, disturbances and measurement noises.

在存在不确定的动态项和外部干扰的情况下，本文解决了在欠驱动水下航行器中应用轨迹跟踪的问题。基于李雅普诺夫理论和神经网络的性质，设计了一种非线性神经控制器，其中采用多层神经网络来逼近未建模的动态项和外部干扰。为了将估计权重的值限制在预定范围内，采用了平滑投影函数。此外，考虑了测量噪声以模拟现实的操作场景，同时设计了滤波器以获取更清洁的状态。从稳定性分析中可以证明，跟踪误差在全局范围内最终是有界的。