小型双发尾推无人艇约束模型预测路径跟踪控制与实验验证

梁建建; 何绍溟

doi:10.12382/bgxb.2026.0474

您当前的位置：

首页 >

文章列表页 >

小型双发尾推无人艇约束模型预测路径跟踪控制与实验验证

更新时间：2026-08-11

- 小型双发尾推无人艇约束模型预测路径跟踪控制与实验验证
- Constrained Model Predictive Path Following Control and Experimental Verification for a Small Twin-Thruster Aft-Propelled Unmanned Surface Vehicle
- 兵工学报 2026年
- 作者机构：
  
  北京理工大学空天科学与技术学院，北京海淀 100081
- 作者简介：
  
  邮箱：shaoming.he@bit.edu.cn
- 基金信息：
  
  国家自然科学基金项目(52302449)
- DOI：10.12382/bgxb.2026.0474
  中图分类号： TP242
- 收稿：2026-05-24，
  
  修回：2026-07-29，
  
  录用：2026-08-06，
- 稿件说明：
移动端阅览
梁建建, 何绍溟. 小型双发尾推无人艇约束模型预测路径跟踪控制与实验验证[J/OL]. 兵工学报, 2026.

LIANG Jianjian, HE Shaoming. Constrained Model Predictive Path Following Control and Experimental Verification for a Small Twin-Thruster Aft-Propelled Unmanned Surface Vehicle[J/OL]. Acta Armamentarii, 2026.
梁建建, 何绍溟. 小型双发尾推无人艇约束模型预测路径跟踪控制与实验验证[J/OL]. 兵工学报, 2026. DOI： 10.12382/bgxb.2026.0474.

LIANG Jianjian, HE Shaoming. Constrained Model Predictive Path Following Control and Experimental Verification for a Small Twin-Thruster Aft-Propelled Unmanned Surface Vehicle[J/OL]. Acta Armamentarii, 2026. DOI： 10.12382/bgxb.2026.0474.

摘要

小型无人艇具有平台尺寸小、惯性和水动力特性明显、执行机构受限等特点，在曲线路径跟踪中容易出现侧滑、超调和推进器控制信号频繁波动等问题。传统视线制导与反馈控制方法结构简单、易于实现，但通常难以在控制求解过程中显式考虑系统约束和未来运动趋势，限制了其在复杂曲线路径跟踪任务中的控制精度和运行平稳性。针对上述问题，面向一种小型双发尾推无人艇，提出一种基于约束模型预测控制的路径跟踪方法，并完成平台构建、动力学建模、参数辨识、控制器设计和外场实验验证。采用玻璃纤维和不饱和树脂复合材料搭建小型无人艇实验平台，设计由上层任务计算机和底层嵌入式控制器组成的两级自动驾驶系统；结合双推进器差速驱动特性，建立无人艇平面三自由度动力学模型，并通过静态推力测试和遥控航行数据完成系统参数辨识；在预测时域内生成参考航向和速度序列，将状态约束、推力约束和推力增量约束纳入模型预测控制优化问题，实现左右推进器推力的在线滚动优化。数值仿真与外场实验结果表明，所提方法能够有效改善小型无人艇曲线路径跟踪性能。在双纽线路径跟踪实验中，模型预测控制方法的位置跟踪均方根误差为0.399 m，传统LOS-PID方法为0.851 m，位置跟踪误差约降低53.1%，跟踪精度提升约2.1倍；同时，该方法能够减小推进器控制输入的剧烈变化，提高无人艇航行过程的平稳性。研究结果验证了约束模型预测控制在小型双发尾推无人艇路径跟踪任务中的有效性和工程应用潜力。

Abstract

Small unmanned surface vehicles (USVs) exhibit characteristics such as small platform size

significant inertia and hydrodynamic effects

and limited actuators

making them prone to sideslip

overshoot

and frequent fluctuations in thruster control signals during curved path following. Traditional line-of-sight guidance and feedback control methods are structurally simple and easy to implement

but they often struggle to explicitly account for system constraints and future motion trends in the control solution process

which limits their control accuracy and operational smoothness in complex curved path following tasks. To address these issues

this paper proposes a path following method based on constrained model predictive control for a small twin-thruster aft-propelled USV

and completes platform construction

dynamic modeling

parameter identification

controller design

and experimental verification. First

a small USV experimental platform is constructed using glass fiber and unsaturated polyester resin composites

and a two-level autopilot system consisting of an upper-level mission computer and a lower-level embedded controller is designed. Second

considering the differential drive characteristics of the twin thrusters

a planar three-degree-of-freedom dynamic model is established for the USV

and system parameters are identified through static thrust tests and remote-controlled navigation data. Then

a reference heading and speed sequence is generated within the prediction horizon

and state

thrust

and thrust increment constraints are incorporated into the MPC optimization problem to achieve online receding-horizon optimization of the left and right thruster thrusts. Numerical simulations and experiments conducted on a lake demonstrate that the proposed method effectively improves the curved path following performance of the small USV. In the lemniscate path experiment

the root mean square error (RMSE) of position tracking is 0.399 m for the MPC method

compared to 0.851 m for the traditional LOS-PID method

representing a reduction in position tracking error of approximately 53.1% and the tracking accuracy has been improved by approximately 2.1 times. Furthermore

the proposed method mitigates drastic changes in thruster control inputs

enhancing the navigation smoothness of the USV. The results validate the effectiveness and engineering application potential of constrained MPC for path following tasks of small twin-thruster aft-propelled USVs.

关键词

Keywords

references

GONZALEZ G , CASTANEDA H . Guidance and control based on adaptive sliding mode strategy for a USV subject to uncertainties [J ] . IEEE Journal of Oceanic Engineering , 2021 , 46 ( 4 ), 1144 – 1154 .

王潋 , 李烨 , 陈霄 , 等 . 基于视线导引策略的无人艇航迹跟踪控制算法 [J ] . 兵工学报 , 2022 , (S 2 ), 20 - 25 .

WANG L , LI Y , CHEN X , et al . USV trajectory tracking control algorithm of based on the line-of-sight guidance strategy [J ] . Acta Armamentarii , 2022 , (S 2 ), 20 - 25 . (in Chinese)

张成举 , 王聪 , 王金强 , 等 . 欠驱动水面无人艇鲁棒自适应位置跟踪控制 [J ] . 兵工学报 , 2020 , 41 ( 7 ), 1393 - 1400 .

ZHANG C J , WANG C , WANG J Q , et al . Robust adaptive position tracking control of underactuated unmanned surface vehicle [J ] . Acta Armamentarii , 2020 , 41 ( 7 ), 1393 - 1400 . (in Chinese)

LIU Z Q , XIANG Q , YE X , et al . Unmanned surface vehicle heading and speed control method based on model predictive control [C ] . 2022 IEEE 5th International Conference on Electronics Technology (ICET) , 2022 , 612 – 616 .

YILMAZ I C , AHISKA K , LEBLEBICIOGLI M K . Parallel docking problem for unmanned surface vehicles [C ] . 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) , 2018 , 744 - 749 .

LI F , LI H P , WU C . Gaussian process based learning model predictive control with application to USV [J ] . IEEE Transactions on Industrial Electronics , 2024 , 71 ( 12 ), 16388 - 16397 .

ZHAO M Z , LI H P . Distributed model predictive contouring control of unmanned surface vessels [J ] . IEEE Transactions on Industrial Electronics , 2024 , 71 ( 10 ), 13012 - 13019 .

FOSSEN T I . Handbook of marine craft hydrodynamics and motion control [M ] . Chichester : Wiley , 2011 .

GU N , WANG D , PENG Z H , et al . Advances in line-of-sight guidance for path following of autonomous marine vehicles: an overview [J ] . IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2023 , 53 ( 1 ), 12 - 28 .

ROGER S , FOSSEN T I , PETAR V K . Adaptive maneuvering, with experiments, for a model ship in a marine control laboratory [J ] . Automatica , 2005 , 41 ( 2 ), 289 - 298 .

CHEN X , LIU Z , ZHANG J Q , et al . Adaptive sliding-mode path following control system of the underactuated USV under the influence of ocean currents [J ] . Journal of Systems Engineering and Electronics , 2018 , 29 ( 6 ), 1271 – 1283 .

XAVIER V , EDOARDO I S , MANHAR R D . Fuzzy logic-assisted PID control for improved station-keeping of a catamaran-style USV [C ] . OCEANS 2025 - Great Lakes , Chicago , IL , USA , 2025 , 1 - 8 .

KARIMI H R , LU Y Y . Guidance and control methodologies for marine vehicles: a survey [J ] . Control Engineering Practice , 2021 , 111 , 104785 .

RAWLINGS J B , MAYNE D Q , DIEHL M M . Model predictive control: theory, computation, and design [M ] . Madison : Nob Hill Publishing , 2017 .

CHEN Y H , ZHANG H C , BIAN Y G , et al . Event-triggered safety control strategy in learning-based model predictive control of partially unknown nonlinear System [J ] . IEEE Transactions on Automation Science and Engineering , 2026 , 23 , 222 - 236 .

ZHAO S Q , MU J R , LIU H D , et al . Heading control of USV based on fractional-order model predictive control [J ] . Ocean Engineering , 2025 , 322 , 120476 .

BEJARANO G , MANZANO J M , SALVADOR J R , et al . Nonlinear model predictive control-based guidance law for path following of unmanned surface vehicles [J ] . Ocean Engineering , 2022 , 258 , 111764 .

MA Y K , LIU Z L , WANG T , et al . Multi-model predictive control strategy for path-following of unmanned surface vehicles in wide-range speed variations [J ] . Ocean Engineering , 2024 , 295 , 116845 .

LI W H , ZHANG X X . Data-driven model predictive control for underactuated USV path tracking with unknown dynamics [J ] . Ocean Engineering , 2025 , 333 , 121457 .

SONG C Y , QI Q , SUI J H . Obstacle avoidance tracking control of underactuated surface vehicles based on improved MPC [J ] . Journal of Marine Science and Engineering , 2025 , 13 ( 9 ), 1603 .

NOMOTO K , TAGUCHI T , HONDA K , et al . On the steering qualities of ships [J ] . International Shipbuilding Progress , 1957 , 4 ( 35 ), 354 – 370 .

范云生，王国峰，赵永生 . 无人水面艇运动建模与自主控制 [M ] . 大连：大连海事大学出版社， 2020 .

FAN Y S , WANG G F , ZHAO Y S . Modeling and autonomous control of unmanned surface Vehicle [M ] . Dalian : Dalian Maritime University Press , 2020 . (in Chinese)

COLEMAN T F , LI Y Y . A reflective newton method for minimizing a quadratic function subject to bounds on some of the variables [J ] . SIAM Journal on Optimization , 1996 , 6 ( 4 ), 1040 – 1058 .

STELLATO B , BANJAC G , GOULART P , et al . OSQP: an operator splitting solver for quadratic programs [J ] . Mathematical Programming Computation , 2020 , 12 ( 4 ), 637 - 672 .

浏览量

下载量

CNKI被引量

文章被引用时，请邮件提醒。

提交

工具集

关联资源

可重构无人车轨迹优化与横向位置-航向角度协同跟踪控制

基于双层模型预测控制的双轮足机器人运动控制

基于高斯过程回归的无人车辆轨迹跟踪MPC

模型失配条件下混合动力两栖车功率协调预测控制

安全凸空间与深度强化学习结合的机器人导航方法