欠驱动水面船舶的自适应神经网络-滑模路径跟随控制

doi:10.16183/j.cnki.jsjtu.2019.122

上海交通大学学报 ›› 2020, Vol. 54 ›› Issue (9): 890-897.doi: 10.16183/j.cnki.jsjtu.2019.122

欠驱动水面船舶的自适应神经网络-滑模路径跟随控制

贺宏伟^a, 邹早建^a^,^b(), 曾智华^a

a.上海交通大学船舶海洋与建筑工程学院,上海200240
b.上海交通大学海洋工程国家重点实验室,上海200240

收稿日期:2019-04-26 出版日期:2020-09-28 发布日期:2020-10-10
通讯作者: 邹早建 E-mail:zjzou@sjtu.edu.cn
作者简介:贺宏伟 (1997-),男,湖南省湘潭市人,硕士生,研究方向为船舶运动控制
基金资助:
国家自然科学基金资助项目(51779140)

Adaptive NN-SM Control for Path Following of Underactuated Surface Vessels

HE Hongwei^a, ZOU Zaojian^a^,^b(), ZENG Zhihua^a

a.School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
b.State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2019-04-26 Online:2020-09-28 Published:2020-10-10
Contact: ZOU Zaojian E-mail:zjzou@sjtu.edu.cn

摘要/Abstract

摘要：

针对欠驱动船舶的路径跟随问题,提出了一种综合神经网络和滑模控制的控制方法.采用视线(LOS)制导方法解决船舶欠驱动问题,并设计了关于漂角的自适应状态观测器,将预测的漂角引入LOS以补偿漂角引起的稳态横向偏差;使用滑模控制方法实现航向控制,并用神经网络处理控制模型的不确定性问题;应用Lyapunov理论证明了控制系统的稳定性,同时通过对比仿真试验结果,验证了本文所提出控制器的有效性.

关键词: 水面船舶, 路径跟随, 视线制导, 自适应观测器, 神经网络, 滑模控制

Abstract:

A control method combining the neural network (NN) and sliding mode (SM) is proposed for path following of underactuated surface vessels. The line-of-sight (LOS) guidance is used to solve the underactuated problem, and an adaptive state observer of drift angle is designed for counteracting the steady state cross-track error caused by the drift angle. The SM control method is applied to heading control while the NN is included to cope with the uncertainties of control model. The stability of the control system is proved by the Lyapunov theory, and the validity of the proposed controller is verified by comparing the simulation results.

Key words: surface vessel, path following, line-of-sight (LOS) guidance, adaptive observer, neural network (NN), sliding mode (SM) control

中图分类号:

TP273

贺宏伟, 邹早建, 曾智华. 欠驱动水面船舶的自适应神经网络-滑模路径跟随控制[J]. 上海交通大学学报, 2020, 54(9): 890-897.

HE Hongwei, ZOU Zaojian, ZENG Zhihua. Adaptive NN-SM Control for Path Following of Underactuated Surface Vessels[J]. Journal of Shanghai Jiaotong University, 2020, 54(9): 890-897.

图/表 5

参考文献 15

[1]	HEALEY A J, LIENARD D. Multivariable sliding mode control for autonomous diving and steering of unmanned underwater vehicles[J]. IEEE Journal of Oceanic Engineering, 1993,18(3):327-339.
[2]	FREDRIKSEN E, PETTERSEN K Y. Global κ-exponential way-point maneuvering of ships: Theory and experiments[J]. Automatica, 2006,42(4):677-687.
[3]	FOSSEN T I, BREIVIK M, SKJETNE R. Line-of-sight path following of underactuated marine craft[J]. IFAC Proceedings Volumes, 2003,36(21):211-216.
[4]	LEKKAS A M, FOSSEN T I. Trajectory tracking and ocean current estimation for marine underactuated vehicles [C]//2014 IEEE Conference on Control Applications (CCA). Juan Les Antibes, France: IEEE, 2014: 905-910.
[5]	BORHAUG E, PAVLOV A, PETTERSEN K Y. Integral LOS control for path following of underactuated marine surface vessels in the presence of constant ocean currents [C]//IEEE Conference on Decision and Control. Cancun, Mexico: IEEE, 2008: 4984-4991.
[6]	KAHVECI N E, IOANNOU P A. Adaptive steering control for uncertain ship dynamics and stability ana-lysis[J]. Automatica, 2013,49(3):685-697.
[7]	FOSSEN T I, LEKKAS A M. Direct and indirect adaptive integral line-of-sight path-following controllers for marine craft exposed to ocean currents[J]. International Journal of Adaptive Control & Signal Processing, 2017,31(4):445-463.
[8]	LIU C, CHEN C P, ZOU Z, et al. Adaptive NN-DSC control design for path following of underactuated surface vessels with input saturation[J]. Neurocomputing, 2017,267:466-474.
[9]	刘洋, 郭晨. 船舶航向保持RBF神经网络自适应非线性控制[J]. 大连海事大学学报, 2013,39(4):1-4.
	LIU Yang, GUO Chen. RBF neural network based adaptive nonlinear control for ship course keeping[J]. Journal of Dalian Maritime University, 2013,39(4):1-4.
[10]	罗伟林, 邹早建, 李铁山. 基于神经网络和L2增益的船舶航向自动舵设计[J]. 中国造船, 2009,50(2):40-45.
	LUO Weilin, ZOU Zaojian, LI Teishan. Neural-network and L2-gain based autopilot design of ship course-keeping[J]. Shipbuilding of China, 2009,50(2):40-45.
[11]	秦梓荷, 林壮, 李平, 等. 基于LOS导航的欠驱动船舶滑模控制[J]. 中南大学学报(自然科学版), 2016,47(10):3605-3611.
	QIN Zihe, LIN Zhuang, LI Ping, et al. Sliding-mode control of underactuated ship based on LOS guidance[J]. Journal of Central South University (Science and Technology), 2016,47(10):3605-3611.
[12]	FOSSEN T I. Handbook of marine craft hydrodynamics and motion control[M]. New York: John Wiley & Sons, 2011: 1-596.
[13]	李铁山, 邹早建, 罗伟林. 基于DSC后推法的非线性系统的鲁棒自适应NN控制[J]. 自动化学报, 2008,34(11):1424-1430.
	LI Tieshan, ZOU Zaojian, LUO Weilin. DSC-backstepping based robust adaptive NN control for nonlinear systems[J]. Acta Automatica Sinica, 2008,34(11):1424-1430.
[14]	刘程. 船舶路径跟踪与减横摇综合控制研究[D]. 上海: 上海交通大学, 2015.
	LIU Cheng. Integrated control of path following and roll motion reduction for marine vessels[D]. Shanghai: Shanghai Jiao Tong University, 2015.
[15]	FOSSEN T I. Guidance and control of ocean vehicles[M]. New York: John Wiley & Sons, 1994: 1-494.

欠驱动水面船舶的自适应神经网络-滑模路径跟随控制

Adaptive NN-SM Control for Path Following of Underactuated Surface Vessels

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 5

参考文献 15

相关文章 15

编辑推荐

Metrics

本文评价

[1]	曾国治, 魏子清, 岳宝, 丁云霄, 郑春元, 翟晓强. 基于CNN-RNN组合模型的办公建筑能耗预测[J]. 上海交通大学学报, 2022, 56(9): 1256-1261.
[2]	吴庶宸, 戚宗锋, 李建勋. 基于深度学习的智能全局灵敏度分析[J]. 上海交通大学学报, 2022, 56(7): 840-849.
[3]	全大英, 陈赟, 唐泽雨, 李世通, 汪晓锋, 金小萍. 基于双通道卷积神经网络的雷达信号识别[J]. 上海交通大学学报, 2022, 56(7): 877-885.
[4]	秦艺超, 黄礼敏, 王骁, 马学文, 段文洋, 郝伟. 基于人工神经网络的自航浮标测波方法可行性[J]. 上海交通大学学报, 2022, 56(4): 498-505.
[5]	赵勇, 苏丹. 基于4种长短时记忆神经网络组合模型的畸形波预报[J]. 上海交通大学学报, 2022, 56(4): 516-522.
[6]	丁明, 孟帅, 王书恒, 夏玺. 六自由度波浪补偿平台的神经网络自适应反馈线性化控制[J]. 上海交通大学学报, 2022, 56(2): 165-172.
[7]	刘秀丽, 徐小力. 基于特征金字塔卷积循环神经网络的故障诊断方法[J]. 上海交通大学学报, 2022, 56(2): 182-190.
[8]	聂瑞, 王红茹. 基于神经网络观测器的无人机编队执行器故障诊断[J]. 空天防御, 2022, 5(2): 32-41.
[9]	王卓鑫, 赵海涛, 谢月涵, 任翰韬, 袁明清, 张博明, 陈吉安. 反向传播神经网络联合遗传算法对复合材料模量的预测[J]. 上海交通大学学报, 2022, 56(10): 1341-1348.
[10]	罗睿乔. 井下节流技术在南海东部高温气田的应用[J]. 海洋工程装备与技术, 2022, 9(1): 58-66.
[11]	陶海红, 闫莹菲. 一种基于GA-CNN的网络化雷达节点遴选算法[J]. 空天防御, 2022, 5(1): 1-5.
[12]	戴少怀, 杨革文, 郁文, 吴向上. 基于RBF神经网络的雷达有源压制干扰识别[J]. 空天防御, 2022, 5(1): 102-107.
[13]	郑德重, 杨媛媛, 黄浩哲, 谢哲, 李文涛. 基于距离置信度分数的多模态融合分类网络[J]. 上海交通大学学报, 2022, 56(1): 89-100.
[14]	许凌, 王兴志, 肖林朋. 基于语义特征抓取电网调度事件的检测技术[J]. 上海交通大学学报, 2021, 55(S2): 86-91.
[15]	王岩, 陈耀然, 韩兆龙, 周岱, 包艳. 基于互信息理论与递归神经网络的短期风速预测模型[J]. 上海交通大学学报, 2021, 55(9): 1080-1086.