| 引用本文: | 葛泉波,薛子建,张明川,吴庆涛.基于VMD-WHHO-BLS的无人船位姿预测[J].控制理论与应用,2026,43(2):335~345.[点击复制] |
| GE Quan-bo,XUE Zi-jian,ZHANG Ming-chuan,WU Qing-tao.Prediction of USV pose based on VMD-WHHO-BLS[J].Control Theory & Applications,2026,43(2):335~345.[点击复制] |
|
| 基于VMD-WHHO-BLS的无人船位姿预测 |
| Prediction of USV pose based on VMD-WHHO-BLS |
| 摘要点击 122 全文点击 18 投稿时间：2023-05-04 修订日期：2024-10-17 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2024.30289 |
| 2026,43(2):335-345 |
| 中文关键词 无人船 宽度学习系统 位姿预测 变分模态分解 哈里斯鹰优化 鲸鱼群算法 |
| 英文关键词 unmanned surface vessel broad learning system pose prediction variational mode decomposition Harris Hawks optimization whale optimization algorithm |
| 基金项目 国家自然科学基金项目(62033010), 中国航空科学基金项目(2019460T5001), 江苏高校“青蓝工程”项目(R2023Q07)资助. |
|
| 中文摘要 |
| 随着人工智能技术的发展, 在无人控制系统领域中, 智能传感器的普及使得各式的无人装备运行数据更加
的丰富. 水面无人船作为无人智能装备的重要组成部分, 其关键环节就在于对其安全稳定的自主控制, 因为其结构
复杂, 并且要长时间在未知的环境运作, 难免出现各种异常状态, 会直接影响无人装备的工作能力, 降低其安全性和
经济性, 所以对无人船的位姿状态进行精确的预测十分必要. 本文先利用变分模态分解将时间序列数据分解成若干
分量, 再采用基于宽度学习系统的方法对无人船中的几类数据进行了预测, 同时用基于鲸鱼算法与模拟退火算法改
进的哈里斯鹰优化算法对宽度学习中的伪逆求解回归参数进行优化. 经仿真实验证明, 该方法在预测的准确性和
训练速度方面都有一定优势. |
| 英文摘要 |
| With the development of artificial intelligence technology and the popularity of intelligent sensors in the field
of unmanned control system, the operational data of various types of unmanned equipment is enriched. Unmanned surface
vessel (USV) as an important component of unmanned intelligent equipment, the key link of unmanned aerial vehicle is
to control its safety and stability independently. Because of its complex structure and long time operation in unknown
environment, it is unavoidable that various abnormal conditions will occur, which will directly affect the capability of
unmanned aerial vehicle and reduce its safety and economy. Therefore, it is necessary to accurately predict the unmanned
ship’s posture. Firstly, time series data is decomposed into several components by using variational mode decomposition,
then several types of data in unmanned ship are predicted by adopting a broad-based learning system method. At the
same time, the pseudo-inverse solution regression parameters in broad learning are optimized by applying to a Harris Eagle
optimization algorithm based on whale algorithm and simulated annealing algorithm. Simulation results show that the
proposed method has certain advantages in accuracy and training speed. |
|
|
|
|
|