引用本文:葛晖,敬忠良,高剑.自主式水下航行器三维路径跟踪的神经网络H∞鲁棒自适应控制方法[J].控制理论与应用,2012,29(3):317~322.[点击复制]
GE Hui,JING Zhong-liang,GAO Jian.Neural network H-infinity robust adaptive control for autonomous underwater vehicle in 3-dimensional path following[J].Control Theory and Technology,2012,29(3):317~322.[点击复制]
自主式水下航行器三维路径跟踪的神经网络H∞鲁棒自适应控制方法
Neural network H-infinity robust adaptive control for autonomous underwater vehicle in 3-dimensional path following
摘要点击 2298  全文点击 1926  投稿时间:2010-06-02  修订日期:2011-11-09
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DOI编号  10.7641/j.issn.1000-8152.2012.3.CCTA100648
  2012,29(3):317-322
中文关键词  自主式水下航行器(AUV)  三维路径跟踪  Serret-Frenet坐标系  质心回路  姿态回路  神经网络
英文关键词  autonomous underwater vehicle  3D path following  Serret-Frenet frame  centroid loop  attitude loop  neural network
基金项目  国家自然科学基金资助项目(60775022); 中国博士后科学基金资助项目(20100470119).
作者单位E-mail
葛晖* 上海交通大学 航空航天学院 gehui-2010@sjtu.edu.cn 
敬忠良 上海交通大学 航空航天学院  
高剑 西北工业大学 航海学院  
中文摘要
      本文研究了存在模型不确定以及外界未知扰动情况下的自主式水下航行器(AUV)的三维路径跟踪控制问题. 针对此问题, 首先利用时标分离原理及正交投影Serret-Frenet坐标系建立了描述AUV质心运动及姿态运动的的仿射非线性数学模型. 其次, 在控制器设计中运用神经网络H∞鲁棒自适应算法克服了模型的不确定性及扰动, 同时在控制器设计中利用了主导输入的思想, 降低了闭环系统的复杂度, 减少了实时计算工作量, 便于工程应用. 基于Lyapunov理论的分析保证了系统的稳定性. 仿真结果表明, 路径跟踪控制律可以保证AUV沿期望路径运动, 并且具有良好的动态性能.
英文摘要
      The 3-dimensional path following control of autonomous underwater vehicle (AUV) with uncertain model and unknown disturbance is investigated. First, the time singular perturbation method and the orthogonal projection reference frame are used to model the position motion, and the attitude dynamic model of the AUV is described by an affine nonlinear system. Next, a neural network robust adaptive control algorithm is employed in the controller design to overcome the uncertainties of the model and the influence of external disturbances. Meanwhile, the dominant input idea is adopted to reduce the complexity of the closed-loop. The stability performance of the system is proved by using Lyaponov stability theory. Simulation indicates that the path following controller keeps the motion of AUV along the prescribed path with desirable performances.