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基于反步和预测控制方法的欠驱动船舶路径跟踪

Path following of under-actuated ships based on backstepping and predictive control method.

作者信息

Liu Yong, Li Zongxuan, Liu Jun

机构信息

Navigation College, Dalian Maritime University, Dalian, China.

出版信息

Sci Prog. 2023 Jul-Sep;106(3):368504231191407. doi: 10.1177/00368504231191407.

DOI:10.1177/00368504231191407
PMID:37644846
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10469279/
Abstract

To deal with the sideslip angle caused by the current disturbances or transverse motion for path following of under-actuated ships, a nonlinear observer established by an exponential function is introduced in the backstepping approach which converts the path following into heading control. Then, the model predictive control (MPC) method is used as a heading controller, addressing the rudder optimization. A linear extended state observer technology was exploited to estimate yaw rate, external disturbances, and internal uncertainties, which could avoid measuring the high-order state used in the MPC controller and promote the accuracy of the MPC internal model. Moreover, an inverse tangent function is applied to develop a new method for switching the reference heading angle to reduce rudder amplitude when the ship is choosing the next waypoint. Finally, the validity and reliability of the design method were verified through comparative computer simulation experiments.

摘要

为了应对当前干扰或横向运动引起的侧滑角,以实现欠驱动船舶的路径跟踪,在反步方法中引入了由指数函数建立的非线性观测器,该方法将路径跟踪转化为航向控制。然后,采用模型预测控制(MPC)方法作为航向控制器,解决舵优化问题。利用线性扩展状态观测器技术估计偏航率、外部干扰和内部不确定性,这可以避免测量MPC控制器中使用的高阶状态,并提高MPC内部模型的准确性。此外,应用反正切函数开发了一种新的方法来切换参考航向角,以在船舶选择下一个航路点时减小舵角幅度。最后,通过对比计算机仿真实验验证了设计方法的有效性和可靠性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/195266421933/10.1177_00368504231191407-fig12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/1e789da95550/10.1177_00368504231191407-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/f765b566c9f1/10.1177_00368504231191407-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/886aa1458032/10.1177_00368504231191407-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/af3248379f94/10.1177_00368504231191407-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/234126bb9864/10.1177_00368504231191407-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/e3266e4cd262/10.1177_00368504231191407-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/9bd7f46e292a/10.1177_00368504231191407-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/02309f08ecd5/10.1177_00368504231191407-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/c9a57921baff/10.1177_00368504231191407-fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/dc31ea147d14/10.1177_00368504231191407-fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/ae2987721653/10.1177_00368504231191407-fig11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/195266421933/10.1177_00368504231191407-fig12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/1e789da95550/10.1177_00368504231191407-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/f765b566c9f1/10.1177_00368504231191407-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/886aa1458032/10.1177_00368504231191407-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/af3248379f94/10.1177_00368504231191407-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/234126bb9864/10.1177_00368504231191407-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/e3266e4cd262/10.1177_00368504231191407-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/9bd7f46e292a/10.1177_00368504231191407-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/02309f08ecd5/10.1177_00368504231191407-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/c9a57921baff/10.1177_00368504231191407-fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/dc31ea147d14/10.1177_00368504231191407-fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/ae2987721653/10.1177_00368504231191407-fig11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb68/10469279/195266421933/10.1177_00368504231191407-fig12.jpg

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本文引用的文献

1
Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle.基于航路点的自主水下航行器路径跟踪与实时避障控制
Sensors (Basel). 2020 Jan 31;20(3):795. doi: 10.3390/s20030795.
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Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control.自主式无人机在搜救任务中的应用——基于实时协同模型预测控制
Sensors (Basel). 2019 Sep 20;19(19):4067. doi: 10.3390/s19194067.
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Cooperative Path Following Ring-Networked Under-Actuated Autonomous Surface Vehicles: Algorithms and Experimental Results.
协作路径跟踪环形网络欠驱动自主水面航行器:算法与实验结果
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