带有输入死区的欠驱动水面船有限时间路径跟踪控制

Finite-time path following control for underactuated surface vessels with input dead-zone

中文关键词  欠驱动水面船  路径跟踪  输入死区  有限时间  神经网络  最小学习参数

英文关键词  underactuated surface vessels  path following  input dead-zone  finite-time  neural network  minimum learning parameter algorithm

中文摘要

      针对模型不确定、外界环境干扰和输入死区下的欠驱动水面船路径跟踪控制问题,本文提出一种自适应有限时间路径跟踪控制方法.首先,设计有限时间视线制导律生成期望纵向速度和艏向角指令;继而,采用反步法分别设计有限时间纵向速度和艏向角控制器跟踪生成的期望信号,其中结合径向基函数神经网络和最小学习参数算法逼近模型参数不确定性,以及运用自适应技术补偿环境干扰、神经网络逼近误差和未知死区非线性组成的合成干扰;此外,为了避免复杂计算,运用二阶跟踪微分器得到虚拟艏向控制律的导数,基于李雅普诺夫稳定性理论,证明整个闭环系统是实际有限时间稳定的;最后,仿真结果验证了所提方案的有效性.

英文摘要

      This paper presents an adaptive finite-time path following control scheme for underactuated surface vessels in the presence of input dead-zone, model uncertainties and external environmental disturbances. Firstly, a finite-time surge-heading line-of-sight guidance law is designed to generate desired surge velocity and heading angle. Then, the adaptive finite-time surge and the heading controllers are developed to track reference signals via backstepping technique, where radial basis function neural network and minimum learning parameter algorithm are applied to approximate model uncertainties, and the adaptive technique is introduced to offset the lumped disturbances including unknown external environmental disturbances, approximation errors and input dead-zone. Besides, to reduce the computational burden inherent in backstepping technique, a second-order tracking differentiator is employed to generate derivative of the virtual heading control law. Based on the Lyapunov functions, it is proved that the closed-loop system is practical finite-time stable. Finally, simulation results are given to demonstrate the efficacy of the proposed scheme.