中文摘要：

为了进一步解决模型存在任意不确定性和外界环境干扰的欠驱动船舶路径跟踪控制问题，在Backstepping方法基础上，引入非线性函数逼近技术对模型中任意不确定因素进行补偿控制。考虑到“计算膨胀”和控制实时性问题，引入动态面控制和最小参数学习方法的设计思想，充分利用欠驱动船舶模型内部结构特征，将用于非线性函数逼近的神经网络权重压缩为4个参数进行在线学习。该算法具有形式简捷、学习参数少、易于工程实现的特点，仿真实例验证了所提出控制策略的有效性。

英文摘要：

In order to supplement the control design for underactuated ships with arbitrary uncertainties and external nonzero time-varying disturbances, a NNs-based concise robust adaptive control scheme is developed based on the popular backstepping method. By virtue of nonlinear function approximation, model uncertainties are approximated and compensated in the control design. In addition, the problems of “explosion of complexity” and the real-time control are solved using the dynamic surface control （DSC） and minimal learning parameter （MLP） techniques. Along with the inherent structural characters of underactuated ships, the neural network weights used for nonlinear function approximation were actually minimized to 4 online learning parameters. Compared with the existing results, the proposed algorithm has the advantages of concise forms, fewer learning parameters and convenience of implementation in practical applications. Numerical simulation results illustrate the effectiveness of the proposed scheme.