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基于改进MPC算法的ROV定深控制策略

杨硕 王泓晖 刘新宇 房鑫 李广浩 刘贵杰

杨硕, 王泓晖, 刘新宇, 等. 基于改进MPC算法的ROV定深控制策略[J]. 水下无人系统学报, 2025, 33(3): 420-432 doi: 10.11993/j.issn.2096-3920.2024-0172
引用本文: 杨硕, 王泓晖, 刘新宇, 等. 基于改进MPC算法的ROV定深控制策略[J]. 水下无人系统学报, 2025, 33(3): 420-432 doi: 10.11993/j.issn.2096-3920.2024-0172
YANG Shuo, WANG Honghui, LIU Xinyu, FANG Xin, LI Guanghao, LIU Guijie. Fixed Depth Control Strategy for Remotely Operated Vehicle Based on Improved Model Predictive Control Algorithm[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 420-432. doi: 10.11993/j.issn.2096-3920.2024-0172
Citation: YANG Shuo, WANG Honghui, LIU Xinyu, FANG Xin, LI Guanghao, LIU Guijie. Fixed Depth Control Strategy for Remotely Operated Vehicle Based on Improved Model Predictive Control Algorithm[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 420-432. doi: 10.11993/j.issn.2096-3920.2024-0172

基于改进MPC算法的ROV定深控制策略

doi: 10.11993/j.issn.2096-3920.2024-0172
基金项目: 青岛市海洋科技协同创新中心项目资助(22-05-CXZX-04-04-22); 山东省高等学校青创科技计划创新团队项目(2022KJ049).
详细信息
    作者简介:

    杨硕:杨 硕(1999-), 男, 在读硕士, 主要研究方向为海洋机电装备技术

  • 中图分类号: TJ630; U674.941

Fixed Depth Control Strategy for Remotely Operated Vehicle Based on Improved Model Predictive Control Algorithm

  • 摘要: 针对有缆遥控水下机器人(ROV)在复杂海洋环境中受到外界干扰影响, 导致深度控制稳定性较差的问题, 提出了一种基于改进模型预测控制(MPC)的复合控制策略。该策略旨在实现高精度定深控制, 同时显著提升ROV在突发外界扰动下的鲁棒性和抗干扰能力。首先, 引入非线性海洋捕食者算法(NMPA)对MPC的关键控制参数进行优化, 以确保ROV在复杂海洋环境中能够实现快速、精确的深度跟踪; 其次, 考虑到传统MPC算法在面对较大外界扰动时的控制效果会受到影响, 该策略引入非线性干扰观测器(NDO)实时补偿外界扰动, 以提升ROV的控制性能与鲁棒性。仿真结果表明: 所提策略使ROV的稳态时间比传统MPC缩短约30%, 超调量降低约10%; 在干扰条件下, 最大超调量降低约27.7%。该策略显著提升了ROV的定深控制性能, 表现出更高的跟踪精度和抗干扰能力。

     

  • 图  1  ROV三维模型

    Figure  1.  Schematic diagram of the 3D model of the ROV

    图  2  推进器推力分布示意图

    Figure  2.  Schematic diagram of thrust distribution for thrusters

    图  3  载体坐标系与惯性坐标系的关系

    Figure  3.  Relationship between the body-fixed coordinate system and the inertial coordinate system

    图  4  NMPA-MPC-NDO算法深度控制器结构框图

    Figure  4.  Block diagram of the depth controller based on the NMPA-MPC-NDO algorithm

    图  5  NMPA优化流程图

    Figure  5.  Flow chart of the NMPA optimization process

    图  6  测试函数寻优结果

    Figure  6.  Optimization results of the test functions

    图  7  NDO干扰力估计曲线

    Figure  7.  Curves of NDO disturbance force estimation

    图  8  NMPA算法迭代寻优

    Figure  8.  Iterative optimization process of the NMPA algorithm

    图  9  静水条件下ROV深度及深度误差变化曲线

    Figure  9.  Curves of depth and depth error of the ROV under static water conditions

    图  10  扰动条件下ROV深度及深度误差变化曲线

    Figure  10.  Curves of depth and depth error of the ROV under disturbance conditions

    图  11  扰动条件下引入NDO后的ROV深度及深度误差变化曲线

    Figure  11.  Curves of depth and depth error of the ROV with NDO under disturbance conditions

    表  1  标准测试函数

    Table  1.   Standard test functions

    函数名称取值范围最小值
    F1Sphere[−100, 100]0
    F2Schwefel 2.22[−10, 10]0
    F3Schwefel 1.2[−100, 100]0
    F4Schwefel 2.21[−100, 100]0
    F5Quartic Function i.e. Noise[−1.28, 1.28]0
    下载: 导出CSV

    表  2  算法优化对比结果

    Table  2.   Comparative results of algorithm optimization

    函数 算法 最差值 最优值 平均值 标准差
    F1 GWO 4.23×10−33 1.59×10−35 4.80×10−34 1.03×10−33
    SSA 1.89×10−40 0 9.37×10−42 4.19×10−41
    PSO 12.53 2.61 8.81 4.67
    WOA 1.64×10−89 9.64×10−104 7.95×10−91 3.18×10−90
    ABC 1.44 0.20 0.57 0.23
    MPA 1.35×10−27 1.40×10−30 1.31×10−28 2.38×10−28
    NMPA 0 0 0 0
    F2 GWO 1.29×10−19 5.27×10−21 3.37×10−20 3.08×10−20
    SSA 9.32×10−33 0 7.29×10−34 2.33×10−33
    PSO 18.35 6.04 10.88 2.93
    WOA 9.26×10−61 9.93×10−68 6.99×10−62 2.10×10−61
    ABC 0.06 0.02 0.03 0.01
    MPA 7.13×10−16 3.00×10−18 1.85×10−16 1.91×10−16
    NMPA 0 0 0 0
    F3 GWO 6.30×10−7 8.46×10−11 8.17×10−8 1.75×10−7
    SSA 1.70×10−60 0 1.07×10−61 3.89×10−61
    PSO 597.75 147.48 305.40 124.85
    WOA 50 679.00 29 946.81 39 755.14 4 204.85
    ABC 41 812.27 25 556.45 33602.59 4 881.43
    MPA 4.45×10−5 3.10×10−10 6.48×10−6 1.32×10−5
    NMPA 0 0 0 0
    F4 GWO 3.63×10−8 1.42×10−9 1.42×10−8 1.21×10−8
    SSA 7.36×10−29 0 3.68×10−30 1.64×10−29
    PSO 7.77 3.29 5.94 1.38
    WOA 91.31 0.000 1 39.72 32.07
    ABC 57.73 37.03 37.03 5.27
    MPA 1.17×10−10 5.80×10−12 3.86×10−11 2.77×10−11
    NMPA 0 0 0 0
    F5 GWO 2.68×10−3 4.66×10−4 1.37×10−3 6.21×10−4
    SSA 6.63×10−4 9.33×10−5 3.44×10−4 1.84×10−4
    PSO 1.08 0.27 0.60 0.23
    WOA 1.12×10−2 3.99×10−5 2.54×10−3 3.12×10−3
    ABC 0.28 0.13 0.19 3.8×10−2
    MPA 2.99×10−3 1.79×10−4 1.20×10−3 7.35×10−4
    NMPA 2.59×10−4 1.39×10−6 1.11×10−4 6.82×10−5
    下载: 导出CSV
  • [1] MANIMARAN R. Hydrodynamic investigations on the performance of an underwater remote operated vehicle under the wave using Open FOAM[J]. Ships and Offshore Structures, 2022, 17(10): 2186-2202. doi: 10.1080/17445302.2021.1979921
    [2] LIU K, DING M, PAN B, et al. A maneuverable underwater vehicle for near-seabed observation[J]. Nature Communications, 2024, 15(1): 10284. doi: 10.1038/s41467-024-54600-8
    [3] CHO G R, LI J H, PARK D, et al. Robust trajectory tracking of autonomous underwater vehicles using back-stepping control and time delay estimation[J]. Ocean Engineering, 2020, 201: 107131. doi: 10.1016/j.oceaneng.2020.107131
    [4] SUN B, MEI M, ZHU D. A cascaded adaptive UUV tracking control design with ocean current[C]//2015 34th Chinese Control Conference(CCC). Hangzhou, China: IEEE, 2015: 4280-4285.
    [5] CHEN K, WANG Y, FU Y, et al. Depth control of bionic robotic fish based on fuzzy PID algorithm[J]. Journal of Physics: Conference Series, 2024, 2781(1): 012050.
    [6] ZHU D, GAN W, HU Z, et al. A hybrid control strategy of 7000 m-human occupied vehicle tracking control[J]. IEEE Transactions on Intelligent Vehicles, 2019, 5(2): 251-264.
    [7] HASAN M W, ABBAS N H. Disturbance rejection for underwater robotic vehicle based on adaptive fuzzy with nonlinear PID controller[J]. ISA Transactions, 2022, 130: 360-376. doi: 10.1016/j.isatra.2022.03.020
    [8] GAN W, ZHU D, HU Z, et al. Model predictive adaptive constraint tracking control for underwater vehicles[J]. IEEE Transactions on Industrial Electronics, 2019, 67(9): 7829-7840.
    [9] WANG Y, ZHANG Y. Coordinated depth control of multiple autonomous underwater vehicles by using theory of adaptive sliding mode[J]. Complexity, 2018, 2018(1): 4180275. doi: 10.1155/2018/4180275
    [10] ELMOKADEM T, ZRIBI M, YOUCEF-TOUMI K. Trajectory tracking sliding mode control of underactuated AUVs[J]. Nonlinear Dynamics, 2016, 84(2): 1079-1091. doi: 10.1007/s11071-015-2551-x
    [11] WEI Y, AN D, LIU J, et al. Intelligent control method of underwater inspection robot in netcage[J]. Aquaculture Research, 2022, 53(5): 1928-1938. doi: 10.1111/are.15721
    [12] 邓旭, 刘子钰. 基于自适应离散滑模趋近律的UUV深度控制[J]. 船舶工程, 2024, 46(S1): 454-459.
    [13] 闫景昊, 王伟然, 杨冠军, 等. 基于拉盖尔函数的AUV自适应预测轨迹跟踪[J]. 电光与控制, 2023, 30(1): 15-20. doi: 10.3969/j.issn.1671-637X.2023.01.003

    YAN J H, WANG W R, YANG G J, et al. Adaptive predictive trajectory tracking of AUV based on Laguerre function[J]. Electronics Optics & Control, 2023, 30(1): 15-20. doi: 10.3969/j.issn.1671-637X.2023.01.003
    [14] 刘甜田, 陆群, 赵伟, 等. 一种基于固定时间稳定理论的移动机器人NMPC方法[J]. 计算机与数字工程, 2022, 50(11): 2423-2427. doi: 10.3969/j.issn.1672-9722.2022.11.014

    LIU T T, LU Q, ZHAO W, et al. A NMPC method for mobile robots based on fixed-time stability theory[J]. Computer and Digital Engineering, 2022, 50(11): 2423-2427. doi: 10.3969/j.issn.1672-9722.2022.11.014
    [15] ZHU D Q, ZHANG H P, LIU C X. Tracking controller based on model prediction control for remotely operated vehicle for thruster fault[J]. Journal of Marine Science and Technology, 2022, 27(2): 840-855. doi: 10.1007/s00773-022-00879-5
    [16] 孙志伟, 李聪. 基于横纵向MPC的智能车换道控制算法[J]. 计算机仿真, 2023, 40(4): 461-468. doi: 10.3969/j.issn.1006-9348.2023.04.089

    SUN Z W, LI C. Lane change control algorithm of intelligent vehicle based on lateral and longitudinal MPC[J]. Computer Simulation, 2023, 40(4): 461-468. doi: 10.3969/j.issn.1006-9348.2023.04.089
    [17] 张硕, 吴雨洋, 汪洋, 等. 基于模型预测控制的无人车编队避障方法[J]. 北京理工大学学报, 2025, 45(1): 34-41.

    ZHANG S, WU Y Y, WANG Y, et al. Formation obstacle avoidance based on model predictive control for unmanned vehicles[J]. Transactions of Beijing Institute of Technology, 2025, 45(1): 34-41.
    [18] PAN J, ZHANG P, WANG J, et al. Learning for depth control of a robotic penguin: A data-driven model predictive control approach[J]. IEEE Transactions on Industrial Electronics, 2022, 70(11): 11422-11432.
    [19] TRAN H N, PHAM T N N, CHOI S H. Robust depth control of a hybrid autonomous underwater vehicle with propeller torque’s effect and model uncertainty[J]. Ocean Engineering, 2021, 220: 108257. doi: 10.1016/j.oceaneng.2020.108257
    [20] 王红都, 高枫, 黎明, 等. 基于ESO的水下机器人机械臂系统鲁棒模型预测控制[J]. 水下无人系统学报, 2023, 31(6): 827-838. doi: 10.11993/j.issn.2096-3920.2022-0074

    WANG H D, GAO F, LI M, et al. ESO-based robust model predictive control for undersea vehicle manipulator system[J]. Journal of Unmanned Undersea Systems, 2023, 31(6): 827-838. doi: 10.11993/j.issn.2096-3920.2022-0074
    [21] GAN W Y, ZHU D Q, JI D X. QPSO-model predictivecontrol-based approach to dynamic trajectory trackingcontrol for unmanned underwater vehicles[J]. Ocean Engineering, 2018, 158: 208-220. doi: 10.1016/j.oceaneng.2018.03.078
    [22] 唐军, 陈善颖, 谢彬, 等. 基于有限时间干扰观测器的改进模型水下机器人自适应鲁棒容错控制[J]. 科学技术与工程, 2024, 24(11): 4574-4582. doi: 10.12404/j.issn.1671-1815.2302720

    TANG J, CHEN S Y, XIE B, et al. Improved model unmanned underwater vehicle adaptive robust fault-tolerant control based on finite time disturbance observer[J]. Science Technology and Engineering, 2024, 24(11): 4574-4582. doi: 10.12404/j.issn.1671-1815.2302720
    [23] FOSSEN T I. Guidance and control of ocean vehicles[M]. New York: John Wiley & Sons Inc, 1994.
    [24] SADIQ A S, DEHKORDI A A, MIRJALILI S, et al. Nonlinear marine predator algorithm: A cost-effective optimizer for fair power allocation in NOMA-VLC-B5G networks[J]. Expert Systems with Applications, 2022, 203: 117395. doi: 10.1016/j.eswa.2022.117395
    [25] 尹晖, 熊治国, 高翔, 等. 基于PSO的自抗扰飞行控制律参数优化方法[J]. 空军工程大学学报, 2013, 14(3): 19-22, 32.

    YIN H, XIONG Z G, GAO X, et al. Parameters optimization of flight control law using ADRC for super-maneuverable aircraft based on PSO[J]. Journal of Air Force Engineering University, 2013, 14(3): 19-22, 32.
    [26] CHEN W H. Disturbance observer based control for nonlinear systems[J]. IEEE/ASME Transactions on Mechatronics, 2004, 9(4): 706-710. doi: 10.1109/TMECH.2004.839034
    [27] WU C J. 6-DOF modelling and control of a remotely operated vehicle[D]. Adelaide, Australia: Flinders University, 2018.
    [28] RICHARDSON M D, BRIGGS K B. In-situ and laboratory geotechnical measurements of seafloor sediments: Implications for underwater vehicle operations[J]. IEEE Journal of Oceanic Engineering, 1999, 24(4): 404-415.
    [29] BLONDEL P, MURTON B J. Handbook of seafloor sonar imagery[M]. Newyork, USA: Wiley, 1997.
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出版历程
  • 收稿日期:  2024-12-19
  • 修回日期:  2025-01-25
  • 录用日期:  2025-02-08
  • 网络出版日期:  2025-05-27

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