基于ROV的深海采矿车辆的合作控制,具有学习不确定的非线性动态
Yuheng Chen1, Haicheng Zhang2, Weisheng Zou1
1College of Mechanical and Vehicle Engineering, Hunan University, Changsha, Hunan 410082, China.
ISA transactions
|March 8, 2025
概括
本研究介绍了一种基于远程操作车辆 (ROV) 的新型深海采矿系统,以防止轨道滑动问题. 新系统使用合作控制策略来加强引和沉降控制,提高深海采矿效率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 海洋工程 海洋工程
- 控制系统 控制系统
背景情况:
- 传统的深海采矿车辆面临着轨道滑动的重大挑战,限制了运营效率和可靠性.
- 现有的系统在复杂的水下环境中经常难以精确控制.
研究的目的:
- 提出基于远程操作车辆 (ROV) 的增强深海采矿系统 (RDMV),以克服轨道滑动的限制.
- 制定一个合作的控制战略,以改善RDMV的引和沉降控制.
- 调查基于学习的模型预测控制 (LMPC) 强大的干扰排斥.
主要方法:
- 建立了基于ROV的深海采矿车辆 (RDMV) 的动态模型,使用拉格朗日力学.
- 开发了用于虚拟速度控制规律的分布式模型预测控制 (DMPC).
- 实现了基于学习的模型预测控制 (LMPC) 与Kinky推理 (KI) 预测功能,以实现最佳的控制输入和干扰估计.
主要成果:
- 在个人ROV运动控制中,LMPC控制器展示了可行性和优越性.
- 数字模拟证实了RDMV合作控制战略的有效性.
- 拟议的系统成功地解决了深海采矿中的轨道滑动瓶问题.
结论:
- 这种基于ROV的新型深海采矿系统为传统轨道车提供了优越的替代方案.
- 合作控制策略和LMPC控制器为深海采矿机器人提供了稳健高效的操作.
- 这一进步提高了深海资源提取可靠和高效的潜力.
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