基于改进模型预测算法的水下机器人定深控制策略

杨硕; 王泓晖; 刘新宇; 房鑫; 李广浩; 刘贵杰

doi:10.11993/j.issn.2096-3920.2024-0172

基于改进模型预测算法的水下机器人定深控制策略

doi: 10.11993/j.issn.2096-3920.2024-0172

中国海洋大学工程学院, 山东青岛, 266100

基金项目: 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, 硕士, 主要研究方向为海洋机电装备技术

中图分类号: TJ630; TP183; TP273
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- 文章访问数: 9
- HTML全文浏览量: 6
- PDF下载量: 1
- 被引次数: 0
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

Depth Control Strategy for Underwater Robots Based on Improved Model Predictive Control

College of Engineering, Ocean University of China, Qingdao 266100, China

摘要

摘要: 针对带缆遥控水下机器人(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策略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。
- 遥控水下机器人 /
- 模型预测控制 /
- 非线性海洋捕食者算法 /
- 非线性干扰观测器 /
- 定深控制
Abstract: To address the issue of poor depth control stability in remotely operated vehicles (ROV) due to external disturbances in complex marine environments, a composite control strategy based on an improved Model Predictive Control (MPC) is proposed. This strategy aims to achieve high-precision depth control while significantly enhancing the robustness and disturbance rejection capability of the ROV under sudden external disturbances. First, a nonlinear marine predator algorithm(NMPA) is introduced to optimize key control parameters of the MPC, ensuring fast and precise depth tracking of the ROV in complex marine environments. Secondly, considering the impact of large external disturbances on traditional MPC performance, the strategy incorporates a nonlinear disturbance observer (NDO) to compensate for external disturbances in real-time, improving the ROV’s control performance and robustness. Simulation results demonstrate that the proposed improved MPC strategy reduces the steady-state time of the ROV by approximately 30% compared to traditional MPC, and decreases the overshoot by about 10%. Under disturbance conditions, the maximum overshoot is reduced by about 27.7%. The proposed strategy significantly enhances the ROV’s depth control performance, exhibiting higher tracking accuracy and better disturbance rejection capability.
- remotely operated vehicle /
- model predictive control /
- nonlinear marine predator algorithm /
- nonlinear disturbance observer /
- depth-holding control

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图 1 ROV三维模型

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

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图 2 推进器推力分布示意图

Figure 2. Schematic diagram of thrust distribution for thrusters

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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

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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

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图 5 NMPA优化流程图

Figure 5. Flow chart of the NMPA optimization process

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图 6 测试函数寻优结果

Figure 6. Optimization results of the test functions

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图 7 NDO干扰力估计曲线

Figure 7. Curves of NDO disturbance force estimation

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图 8 NMPA算法迭代寻优

Figure 8. Iterative optimization process of the NMPA algorithm

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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

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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

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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

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表 1 标准测试函数

Table 1. Standard test functions

函数	名称	取值范围	最小值
F1	Sphere	[−100, 100]	0
F2	Schwefel 2.22	[−10, 10]	0
F3	Schwefel 1.2	[−100, 100]	0
F4	Schwefel 2.21	[−100, 100]	0
F5	Quartic Function i.e. Noise	[−1.28, 1.28]	0

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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

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