基于极速学习的欠驱动无人船鲁棒自适应路径跟踪控制

贺新宇; 王宁; 吴浩峻

doi:10.11993/j.issn.2096-3920.2024-0170

基于极速学习的欠驱动无人船鲁棒自适应路径跟踪控制

doi: 10.11993/j.issn.2096-3920.2024-0170

大连海事大学轮机工程学院, 辽宁大连, 116026

基金项目: 国防基础科研计划一般项目(JCKY2022410C013) 省市级.

详细信息

通讯作者:
王　宁, 男, (1983-), 教授, 博士生导师, 主要研究方向为海洋机器人自主控制及绿色智能船舶控制.

中图分类号: TJ630.2; U674.941
计量
- 文章访问数: 12
- HTML全文浏览量: 2
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2024-12-18
- 修回日期: 2025-01-12
- 录用日期: 2025-01-20
- 网络出版日期: 2025-03-27

Extreme Learning-Based Robust Adaptive Path Following Control of An Underactuated Unmanned Surface Vehicle

Marine Engineering College, Dalian Maritime University, Dalian 116026

摘要

摘要: 针对欠驱动无人船 (USV) 存在未建模动力学、参数不确定性和干扰等未知状况, 提出一种基于极速学习的鲁棒自适应路径跟踪控制方案。首先, 采用纵荡视线制导律, 同时制导纵荡速度与航向角, 避免制导过程的奇异现象, 使得USV快速收敛至期望路径; 其次, 将包含系统不确定性和外部干扰的未知动态封装成一个集总未知项, 利用极速学习机的单隐层前馈网络 (SLFN) 随机产生隐层节点在线辨识该未知项, 避免依赖USV先验知识和“维度爆炸”问题; 然后, 通过设计逼近残差自适应补偿器, 同时在线更新SLFN的输出权重和逼近残差, 形成双通道学习机制, 不仅可以增强逼近能力, 而且提高了跟踪精度; 最后,设计自适应路径跟踪控制器, 使得USV的纵荡速度与航向角制导误差可以渐进收敛到原点附近的小邻域。文中仿真研究验证了所提方案的有效性和优越性。
- 欠驱动无人船 /
- 极速学习 /
- 双通道学习机制 /
- 自适应控制 /
- 路径跟踪
Abstract: An extreme learning-based robust adaptive path following control scheme is proposed for underactuated unmanned surface vehicle (USV) with unknown dynamics, parameter uncertainties and disturbances. Firstly, the surge-guided line-of-sight guidance law is used to guide the surge speed and heading angle at the same time, so as to avoid the singularity of the guidance process and make USV converge to the desired path quickly. Secondly, the unknown dynamics including system uncertainties and external disturbances are encapsulated into a lumped unknown term, and hidden layer nodes are randomly generated by the single-hidden layer feedforward network (SLFN) of the extreme learning machine to identify the unknown term, avoid relying on USV prior knowledge and 'dimension explosion' problem. Then, by designing an adaptive compensator for the approximation residual, the output weight and the approximation residual of the SLFN are updated online at the same time to form a dual-channel learning mechanism, which can not only enhance the approximation ability, but also improve the tracking accuracy. Finally, an adaptive path following controller is designed, so that the surge velocity and heading angle guidance errors of USV can gradually converge to a small neighborhood near the origin. Simulation studies verify the effectiveness and superiority of the proposed scheme.
- underactuated unmanned surface vehicle /
- extreme learning /
- dual-channel learning mechanism /
- adaptive control /
- path following

HTML全文

图 1 USV模型

Figure 1. Model of USV

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图 2 USV路径跟踪示意图

Figure 2. Schematic diagram of USV path tracing

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图 3 SLFN结构图

Figure 3. Structure diagram of SLFN

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图 4 USV的EL-RAPFC方案

Figure 4. EL-RAPFC protocol for USV

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图 5 第1组仿真路径跟踪性能

Figure 5. Path following performance of the first set of simulation

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图 6 第1组仿真垂向与沿向跟踪误差

Figure 6. The first set simulates vertical and along-direction tracking errors

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图 7 第2组仿真路径跟踪性能

Figure 7. Path following performance of the second set of simulation

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图 8 第二组仿真垂向与沿向跟踪误差

Figure 8. The second set simulates the tracking error in the vertical and along directions ulation

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图 9 纵荡速度与航向角制导跟踪比较

Figure 9. Comparison of surge speed and heading angle guidance tracking

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图 10 纵荡速度与航向角跟踪误差

Figure 10. Surge speed and heading angle tracking errors

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图 11 复杂未知量及其逼近值

Figure 11. Complex unknown quantities and approximation values

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图 12 自适应逼近误差

Figure 12. Adaptive approximation errors

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图 13 USV的纵向与艏向控制输入

Figure 13. Longitudinal and heading control inputs for USV

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