2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应控制

沈智鹏 张晓玲

沈智鹏, 张晓玲. 基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应控制. 自动化学报, 2018, 44(10): 1833-1841. doi: 10.16383/j.aas.2017.c170198
引用本文: 沈智鹏, 张晓玲. 基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应控制. 自动化学报, 2018, 44(10): 1833-1841. doi: 10.16383/j.aas.2017.c170198
SHEN Zhi-Peng, ZHANG Xiao-Ling. Recursive Sliding-mode Dynamic Surface Adaptive Control for Ship Trajectory Tracking With Nonlinear Gains. ACTA AUTOMATICA SINICA, 2018, 44(10): 1833-1841. doi: 10.16383/j.aas.2017.c170198
Citation: SHEN Zhi-Peng, ZHANG Xiao-Ling. Recursive Sliding-mode Dynamic Surface Adaptive Control for Ship Trajectory Tracking With Nonlinear Gains. ACTA AUTOMATICA SINICA, 2018, 44(10): 1833-1841. doi: 10.16383/j.aas.2017.c170198

基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应控制

doi: 10.16383/j.aas.2017.c170198
基金项目: 

中央高校基本科研业务费项目 3132017126

辽宁省自然科学基金 201602072

国家自然科学基金 51579024

中央高校基本科研业务费项目 3132016311

详细信息
    作者简介:

    张晓玲  大连海事大学信息科学技术学院硕士研究生.主要研究方向为船舶运动自适应控制.E-mail:eternalzxl@163.com

    通讯作者:

    沈智鹏  大连海事大学信息科学技术学院教授.主要研究方向为自适应控制, 最优控制, 智能控制及其在载运工具中的应用.本文通信作者.E-mail:shenbert@dlmu.edu.cn

Recursive Sliding-mode Dynamic Surface Adaptive Control for Ship Trajectory Tracking With Nonlinear Gains

Funds: 

Fundamental Research Funds for the Central Universities 3132017126

Science Foundation of Liaoning Province of China 201602072

National Natural Science Foundation of China 51579024

Fundamental Research Funds for the Central Universities 3132016311

More Information
    Author Bio:

     Master student at the School of Information Science and Technology, Dalian Maritime University. Her main research interest is ship motion adaptive control

    Corresponding author: SHEN Zhi-Peng  Professor at the School of Information Science and Technology, Dalian Maritime University. His research interest covers adaptive control, optimal control, and intelligent control with the application in vehicle. Corresponding author of this paper
  • 摘要: 针对三自由度全驱动船舶存在模型不确定和未知外部环境扰动的情况,设计出一种基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应神经网络控制方法.该方法综合考虑船舶位置和速度误差之间关系设计递归滑模面,引入神经网络对船舶模型不确定部分进行逼近,设计带σ-修正泄露项的自适应律对神经网络逼近误差与外界环境扰动总和的界进行估计,并应用一种非线性增益函数构造动态面控制律,选取李雅普诺夫函数证明了该控制律能够保证轨迹跟踪闭环系统内所有信号的一致最终有界性.最后,基于一艘供给船进行仿真验证,结果表明,船舶轨迹跟踪响应速度快、精度高,所设计控制器对系统模型参数摄动及外界扰动具有较强的鲁棒性.
    1)  本文责任编委 郭戈
  • 图  1  函数$l(x)$曲线图

    Fig.  1  The curves of function $l(x)$

    图  2  外部环境扰动下船舶的期望轨迹和实际轨迹

    Fig.  2  Desired trajectory and actual trajectory under external environment disturbances

    图  3  外部环境扰动下期望轨迹${\pmb \eta}_d=[x_d, y_d, \psi_d]^{\rm T}$和文算法实际轨迹${\pmb \eta}=[x, y, \psi]^{\rm T}$历时曲线

    Fig.  3  Curves of desired trajectory ${\pmb \eta}_d=[x_d, y_d, \psi_d]^{\rm T}$ and actual trajectory ${\pmb \eta}=[x, y, \psi]^{\rm T}$ with proposed controller versus time under external environment disturbances

    图  4  外部环境扰动下前进速度$u$、横移速度$\nu$和艏摇角速度$r$历时曲线

    Fig.  4  Curves of surge velocity $u$, sway velocity $\nu$ and yaw rate $r$ versus time under external environment disturbances

    图  5  神经网络逼近历时曲线

    Fig.  5  Curves of learning behavior of neural networks

    图  6  控制器输出

    Fig.  6  Curves of controller outputs

    图  7  外部环境扰动和逼近误差的界$\delta_1$, $\delta_2$, $\delta_3$及其估计值$\hat{\delta}_1$, $\hat{\delta}_2$, $\hat{\delta}_3$历时曲线

    Fig.  7  Curves of the bounds of external environment disturbances and approximation errors $\delta_1$, $\delta_2$, $\delta_3$ and their estimations $\hat{\delta}_1$, $\hat{\delta}_2$, $\hat{\delta}_3$ verse time with proposed controller

    图  8  相同海况下本文算法与常规动态面算法跟踪性能比较

    Fig.  8  Comparison of tracking errors under the same sea conditions between the proposed method and the dynamic surface control method

  • [1] Fossen T I, Berge S P. Nonlinear vectorial backstepping design for global exponential tracking of marine vessels in the presence of actuator dynamics. In: Proceedings of the 36th Conference on Decision and Control. San Diego, California, USA: IEEE, 1997, 5: 4237-4242 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=649499
    [2] Yang Y, Du J L, Liu H B, Guo C, Abraham A. A trajectory tracking robust controller of surface vessels with disturbance uncertainties. IEEE Transactions on Control Systems Technology, 2014, 22(4):1511-1518 doi: 10.1109/TCST.2013.2281936
    [3] 付明玉, 焦建芳, 张爱华.基于虚拟领航者的多艘船舶协调路径跟踪控制.华中科技大学学报(自然科学版), 2013, 41(2):102-108 http://d.old.wanfangdata.com.cn/Periodical/hzlgdxxb201302020

    Fu Ming-Yu, Jiao Jian-Fang, Zhang Ai-Hua. Coordinated path following control for multiple surface vessels by using virtual-leader. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2013, 41(2):102-108 http://d.old.wanfangdata.com.cn/Periodical/hzlgdxxb201302020
    [4] Swaroop D, Hedrick J K, Yip P P, Gerdes J C. Dynamic surface control for a class of nonlinear systems. IEEE Transactions on Automatic Control, 2000, 45(10):1893-1899 doi: 10.1109/TAC.2000.880994
    [5] 杜佳璐, 杨杨, 胡鑫, 陈海泉.基于动态面控制的船舶动力定位控制律设计.交通运输工程学报, 2014, 14(5):36-42, 50 doi: 10.3969/j.issn.1671-1637.2014.05.005

    Du Jia-Lu, Yang Yang, Hu Xin, Chen Hai-Quan. Control law design of dynamic positioning for ship based on dynamic surface control. Journal of Traffic and Transportation Engineering, 2014, 14(5):36-42, 50 doi: 10.3969/j.issn.1671-1637.2014.05.005
    [6] Wang D, Huang J. Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Transactions on Neural Networks, 2005, 16(1):195-202 doi: 10.1109/TNN.2004.839354
    [7] Xu B, Zhang Q, Pan Y P. Neural network based dynamic surface control of hypersonic flight dynamics using small-gain theorem. Neurocomputing, 2016, 173:690-699 doi: 10.1016/j.neucom.2015.08.017
    [8] 刘希, 孙秀霞, 刘树光, 徐嵩, 郝震.非脆弱递归滑模动态面自适应神经网络控制.控制理论与应用, 2013, 30(10):1323-1328 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201310015

    Liu Xi, Sun Xiu-Xia, Liu Shu-Guang, Xu Song, Hao Zhen. Non-fragile recursive sliding mode dynamic surface control with adaptive neural network. Control Theory and Applications, 2013, 30(10):1323-1328 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201310015
    [9] 贾鹤鸣, 张利军, 程相勤, 边信黔, 严浙平, 周佳加.基于非线性迭代滑模的欠驱动UUV三维航迹跟踪控制.自动化学报, 2012, 38(2):308-314 http://www.aas.net.cn/CN/abstract/abstract17635.shtml

    Jia He-Ming, Zhang Li-Jun, Cheng Xiang-Qin, Bian Xin-Qian, Yan Zhe-Ping, Zhou Jia-Jia. Three-dimensional path following control for an underactuated UUV based on nonlinear iterative sliding mode. Acta Automatica Sinica, 2012, 38(2):308-314 http://www.aas.net.cn/CN/abstract/abstract17635.shtml
    [10] 沈智鹏, 姜仲昊, 王国峰, 郭晨.风帆助航船舶运动的模糊自适应迭代滑模控制.哈尔滨工程大学学报, 2016, 37(5):634-639 http://d.old.wanfangdata.com.cn/Periodical/hebgcdxxb201605002

    Shen Zhi-Peng, Jiang Zhong-Hao, Wang Guo-Feng, Guo Chen. Fuzzy-adaptive iterative sliding-mode control for sail-assisted ship motion. Journal of Harbin Engineering University, 2016, 37(5):634-639 http://d.old.wanfangdata.com.cn/Periodical/hebgcdxxb201605002
    [11] Li G Y, Li W, Hildre H P, Zhang H X. Online learning control of surface vessels for fine trajectory tracking. Journal of Marine Science and Technology, 2016, 21(2):251-260 doi: 10.1007/s00773-015-0347-9
    [12] 王昊, 王丹, 彭周华, 孙刚.多自主船协同路径跟踪的自适应动态面控制.控制理论与应用, 2013, 30(5):637-643 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201305015

    Wang Hao, Wang Dan, Peng Zhou-Hua, Sun Gang. Adaptive dynamic surface control for cooperative path following of multiple autonomous surface vessels. Control Theory and Applications, 2013, 30(5):637-643 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201305015
    [13] Wang W, Wang D, Peng Z H, Li T S. Prescribed performance consensus of uncertain nonlinear strict-feedback systems with unknown control directions. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2016, 46(9):1279-1286 doi: 10.1109/TSMC.2015.2486751
    [14] 孙秀霞, 刘希, 徐嵩, 蔡鸣, 高杨军, 唐强.无人机航迹角的非线性增益递归滑模控制.系统工程与电子技术, 2015, 37(2):379-384 http://d.old.wanfangdata.com.cn/Periodical/xtgcydzjs2015020025

    Sun Xiu-Xia, Liu Xi, Xu Song, Cai Ming, Gao Yang-Jun, Tang Qiang. Nonlinear gains recursive sliding mode control for flight-path angle of UAVs. Systems Engineering and Electronics, 2015, 37(2):379-384 http://d.old.wanfangdata.com.cn/Periodical/xtgcydzjs2015020025
    [15] 刘希, 孙秀霞, 刘树光, 徐嵩, 程志浩.非线性增益递归滑模动态面自适应NN控制.自动化学报, 2014, 40(10):2193-2202 http://www.aas.net.cn/CN/abstract/abstract18494.shtml

    Liu Xi, Sun Xiu-Xia, Liu Shu-Guang, Xu Song, Cheng Zhi-Hao. Recursive sliding-mode dynamic surface adaptive NN control with nonlinear gains. Acta Automatica Sinica, 2014, 40(10):2193-2202 http://www.aas.net.cn/CN/abstract/abstract18494.shtml
    [16] Polycarpou M M, Ioannou P A. A robust adaptive nonlinear control design. Automatica, 1996, 32(3):423-427 doi: 10.1016/0005-1098(95)00147-6
    [17] 焦李成, 杨淑媛, 刘芳, 王士刚, 冯志玺.神经网络七十年:回顾与展望.计算机学报, 2016, 39(8):1697-1716 http://d.old.wanfangdata.com.cn/Periodical/jsjxb201608015

    Jiao Li-Cheng, Yang Shu-Yuan, Liu Fang, Wang Shi-Gang, Feng Zhi-Xi. Seventy years beyond neural networks:retrospect and prospect. Chinese Journal of Computers, 2016, 39(8):1697-1716 http://d.old.wanfangdata.com.cn/Periodical/jsjxb201608015
    [18] Fossen T I, Sagatun S I, Sørensen A J. Identification of dynamically positioned ships. Modeling, Identification and Control, 1996, 17(2):153-165 doi: 10.4173/mic.1996.2.7
  • 加载中
图(8)
计量
  • 文章访问数:  2008
  • HTML全文浏览量:  180
  • PDF下载量:  783
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-04-14
  • 录用日期:  2017-08-02
  • 刊出日期:  2018-10-20

目录

    /

    返回文章
    返回