欠驱动AUV三维路径跟踪RBF神经网络积分滑模控制

霍宇彤; 郭 晨; 于浩淼

doi:10.11993/j.issn.2096-3920.2020.02.003

欠驱动AUV三维路径跟踪RBF神经网络积分滑模控制

doi: 10.11993/j.issn.2096-3920.2020.02.003

大连海事大学船舶电气工程学院, 辽宁大连, 116026

基金项目: 国家自然科学基金(51879027, 51579024, 51809028); 中央高校基本科研业务费专项资金资助项目(3132019109)

详细信息

作者简介:
霍宇彤(1994-), 男, 在读硕士, 主要研究方向为无人水下航行器运动控制.

中图分类号: TJ630.33; TB115
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- 文章访问数: 1623
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- 被引次数: 0
出版历程
- 收稿日期: 2019-04-04
- 修回日期: 2019-04-25
- 刊出日期: 2020-04-30

RBF Neural Network Integral Sliding Mode Control for Three-Dimensional Path Following of Underactuated AUV

College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China

摘要

摘要: 针对欠驱动自主水下航行器(AUV)在未知参数摄动上界约束下三维路径跟踪问题, 为了抑制外界扰动和滑模控制产生的抖振, 提出了一种基于径向基函数(RBF)神经网络积分滑模的空间路径跟踪控制器。通过引入Serret-Frenet局部坐标系和视线法(LOS)制导律, 将路径跟踪的位置误差镇定转换为视线角误差的镇定。在此基础上, 基于Lyapunov直接法设计运动学虚拟控制律, 用以镇定视线角误差。并通过引入RBF神经网络和积分滑模面, 设计动力学路径跟踪控制器。Lyapunov稳定性理论证明了所设计的路径跟踪控制器的稳定性。仿真对比结果表明, 该路径跟踪控制器在不失快速性的前提下能够有效跟踪三维直线路径, 提高了控制精度, 且抑制了抖振, 对外界扰动具有一定的鲁棒性。
- 欠驱动自主水下航行器 /
- 三维路径跟踪 /
- 积分滑模 /
- 径向基函数神经网络
Abstract: For the three-dimensional path following problem of an underactuated autonomous undersea vehicle(AUV) under the upper bound of unknown parameter perturbation, a spatial path-following controller is proposed based on the radial basis function(RBF) neural network integral sliding mode to reduce chattering caused by external disturbance and sliding mode. The position error stabilization of the path following is converted into the stabilization of the line of sight (LOS) angle error by introducing the Serret-Frenet local frame and the LOS guidance law. And the kinematic “virtual control law” is designed for stabilization of the LOS error based on the Lyapunov direct method. Then, the dynamic path-following controller is developed by introducing the RBF neural network and the integral sliding mode surface. The stability of the designed path-following controller is analyzed by using Lyapunov stability theory. Simulation results show that this path-following controller can effectively follow the three-dimensional straight path, improve the control accuracy, reduce the chattering, and display certain robustness to the external disturbance without losing its rapidity.
- underactuated autonomous undersea vehicle(AUV) /
- three-dimensional path following /
- integral sliding mode /
- radial basis function(RBF)neural network

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参考文献(15)

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