针对缆控机器人（Remotely Operated Vehicle，ROV）清洗海上风电桩的作业需求，提出一种支架式辅助清洗控制方法。通过支架抵近桩机约束ROV位姿，结合自抗扰控制（Active Disturbance Rejection Control，ADRC）策略实现深度和艏向角双通道控制，同时利用淘金优化器（Gold Rush Optimizer，GRO）对ADRC参数进行优化。首先，进行风电桩清洗方案设计，接着构建ROV数学模型，设计深度控制器和艏向角控制器。结合GRO对ADRC参数进行优化，并在Simulink中仿真验证ADRC控制效果和参数优化效果。最后进行风电桩清洗实验，验证所提清洗控制方法的有效性。仿真结果表明，经参数优化的ADRC抗干扰效果更好，鲁棒性强；海上实验表明最大深度跟踪误差在0.1 m以内，艏向角最大跟踪误差在8°以内。证明支架式清洗控制策略具有可行性。研究结果可为海上风电桩清洗提供参考。
To address the operational requirements of cleaning offshore wind turbine piles using Remotely Operated Vehicle (ROV), this study proposes a bracket-assisted cleaning control method. By using the bracket to constrain the ROV pose near the pile, the method integrates an Active Disturbance Rejection Control (ADRC) strategy to achieve dual-channel control of depth and heading angle. Additionally, the Gold Rush Optimizer (GRO) is employed to optimize the parameters of the ADRC. In terms of methodology, the study first designs a cleaning scheme for wind turbine piles, followed by the establishment of the ROV mathematical model and the design of depth and heading angle controllers. The ADRC parameters are then optimized using the GRO, and the control performance and parameter optimization effects are verified through simulations in Simulink. Finally, offshore experiments are conducted to validate the effectiveness of the proposed cleaning control method. Simulation results demonstrate that ADRC with optimized parameters exhibits enhanced disturbance rejection capabilities and robustness. The offshore experiments reveal that the maximum depth tracking error is within 0.1 m, and the maximum heading angle tracking error is within 8°. These results confirm the feasibility and practicality of the bracket-assisted cleaning control strategy. The research findings provide valuable insights and references for offshore wind turbine pile cleaning operations.
2025,47(23): 98-105 收稿日期：2025-3-11
DOI：10.3404/j.issn.1672-7649.2025.23.015
分类号：U672；TP183；TP242.3
基金项目：国家重点研发计划资助项目（2020YFC1521704）
作者简介：杜康(1999-),男,硕士,研究方向为ROV运动控制
参考文献：
[1] 林玉鑫, 张京业. 海上风电的发展现状与前景展望[J]. 分布式能源, 2023, 8(2): 1-10.
[2] 熊保星, 甘文洋, 陈铭治, 等. 基于模糊线性自抗扰的水下机器人定深控制[J/OL]. 控制工程, 2025, 1-9.
[3] 梁一飞, 李永龙, 王皓冉, 等. 基于降阶扩张状态观测器的水下机器人自抗扰运动控制[J]. 传感器与微系统, 2024, 43(8): 141-144+149.
[4] 宋大雷, 严志鹏, 郭亭亭, 等. 模型补偿的水下桥墩检测机器人抗扰控制研究[J]. 现代电子技术, 2022, 45(5): 129-133.
[5] 郑伦川, 梁新元, 袁乖宁. 机械臂自抗扰控制的自适应-重构粒子群优化[J]. 机械设计与制造, 2025(2): 231-235.
[6] 单正, 王元龙, 白若忱, 等. 基于灰狼优化算法的燃气轮机自抗扰控制参数整定研究[J]. 热能动力工程, 2023, 38(4): 1-6.
[7] 陈建均. 深远海复杂海况下钢管桩清淤工艺的应用研究[J]. 南方能源建设, 2025, 12(1): 109-115.
[8] 蔡洪建, 许玮, 李振杰, 等. 一种支座式辅助冲洗装置及其方法[P]. 中国专利: CN107971267B, 2020-06-09.
[9] 周卫祥. 深海作业型ROV建模方法的研究[D]. 哈尔滨: 哈尔滨工程大学, 2015.
[10] FOSSEN T I. Handbook of marine craft hydrodynamics and motion control[M]. John Wiley & Sons Ltd, 2021.
[11] 季葛盛. 开架式水下机器人水动力及其悬停控制仿真研究[D]. 镇江: 江苏科技大学, 2023.
[12] 王翻, 武建国, 王晓鸣, 等. 改进型自抗扰在ROV位姿控制中的应用[J]. 舰船科学技术, 2024, 46(18): 91-98.
WANG F, WU J G, WANG X M, et al. Application of improved active disturbance rejection in ROV pose control[J]. Ship Science and Technology. 2021, 46(18): 91-98.
[13] ZOLFI K. Gold rush optimizer: A new population-based metaheuristic algorithm[J]. Operations Research and Decisions 2023, 33(1): 113-150.
[14] 张彦. 基于多目标遗传算法和多属性决策的船舶PID控制器参数整定[J]. 舰船科学技术, 2009, 31(3): 42-45.
ZHANG Y. Multi-objective optimization and multi-attribute decision making for PID controller parameters tuning in ship design[J]. Ship Science and Technology, 2009, 31(3): 42-45.
[15] 姚绪梁, 孟令卫, 牛小丽. 基于航行阻力优化的近水面机器人减纵摇控制[J]. 中国舰船研究, 2017, 12(3): 111-119.
YAO X L, MENG L W, NIU X L. LQR pitch control strategy of AUVs based on the optimum of sailing resistance[J]. Chinese Journal of Ship Research, 2017, 12(3): 111-119.
