ROV型深海采矿车动力学建模及路径跟踪控制研究
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国家自然科学基金资助项目(52071138,12272128),广东省基础与应用基础研究资助项目(2024A1515010683),江苏省基础研究计划重点项目(BK20243019)

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    摘要:

    本文针对传统深海采矿系统中履带车操作稳定性差的瓶颈问题,研究了一种新型的遥控水下机器人(ROV)型深海采矿系统,其中ROV型深海采矿车取代了传统的履带式深海采矿车.ROV型深海采矿车由一个ROV和一个采矿机器人(MRT)组成.首先,假设ROV保持固定深度拖曳MRT,通过类比自行车模型,以此建立了ROV型采矿车的动力学模型.其次,提出了一种分层路径跟踪控制策略,基于ROV型采矿车的运动学模型,设计了线性模型预测控制器(LMPC)以计算收敛MRT路径跟踪偏差的虚拟控制律,下层PID控制器计算响应虚拟控制律的ROV控制输入.最后,通过数值模拟初步验证了分层路径跟踪控制策略的可行性.

    Abstract:

    This study proposes a ROV-based deep-sea mining system to overcome the stability issues of tracked vehicles in the traditional deep-sea mining system. The ROV-based deep-sea mining vehicle replaces the traditional tracked deep-sea mining vehicle. The ROV-based deep-sea mining vehicle consists of a remote operated vehicle (ROV) and a mining robot (MRT). First, this paper investigates the dynamic model of the ROV-based mining vehicle, assuming that the ROV maintains a fixed depth while towing the MRT. By analogy with a bicycle model, the dynamic model of the ROV-based mining vehicle is established. Subsequently, a hierarchical path tracking control strategy is introduced. Based on the kinematic model of the ROV-type mining vehicle, a linear model predictive control (LMPC) controller is designed to calculate a virtual control law for converging MRT path tracking deviations. A lower-level PID controller computes the ROV control input in response to the virtual control law. Finally, the feasibility of the hierarchical path tracking control strategy is preliminarily validated through numerical simulations.

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陈昱衡,张海成,邹伟生,李煜,徐道临. ROV型深海采矿车动力学建模及路径跟踪控制研究[J].动力学与控制学报,2025,23(2):30~37; Chen Yuheng, Zhang Haicheng, Zou Weisheng, Li Yu, Xu Daolin. Dynamic Modeling and Path Tracking Control of a ROV-Based Deep-Sea Mining Vehicle[J]. Journal of Dynamics and Control,2025,23(2):30-37.

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  • 收稿日期:2024-07-31
  • 最后修改日期:2024-09-13
  • 在线发布日期: 2025-03-13
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