首页|期刊导航|中国舰船研究|事件驱动量测-通信联合框架下基于LMPC的多AUV编队控制方法

事件驱动量测-通信联合框架下基于LMPC的多AUV编队控制方法OA

Event-driven metrology-communication joint framework based on LMPC multi-AUV formation control

中文摘要英文摘要

[目的]针对多自主水下航行器(AUV)编队运动中出现的系统状态感知及传输能力受限及无位置信息交互导致可观测性不足的问题,提出一种用于编队控制的事件驱动量测-通信联合框架下基于 Lyapunov 理论的模型预测控制(ETMCU-LMPC)策略,以提升编队稳定性与跟踪精度.[方法]首先,融合编队通信拓扑与系统状态,建立基于状态观测的事件触发机制,利用各 AUV 之间相对量测信息抑制水声广播失效带来的延迟与丢包,增强无位置信息交互时的系统可观测性;然后,设计基于 Lyapunov 理论的分布式模型预测控制(LMPC)器,采用反步法构造收缩约束保证递归可行性,并引入自适应卡尔曼滤波(AKF)补偿量测噪声,确保闭环稳定性.[结果]对 1 艘领航、4 艘跟随共 5 艘 AUV 的编队仿真表明,与传统LMPC 相比,ETMCU-LMPC方法的收敛时间由 8 s 缩短至 6 s,最大误差由 1.12 m 降至 0.36 m,稳态误差由 0.57 m 降至 0.06 m,且控制输入更平稳.[结论]所提方法可有效应对通信异常,提升状态感知与传输受限场景下多 AUV 编队的可靠性,具有实际工程价值.

[Objective]To address the challenges in multi-AUV formation maneuvering,such as limited state perception and transmission capabilities,acoustic communication delays,data loss,and reduced observ-ability due to the lack of position information exchange,this study proposes an event-triggered metrology-communication unified framework with a Lyapunov-based model predictive formation control method(ETMCU-LMPC).The proposed approach aims to enhance formation stability and tracking accuracy.[Method]First,by integrating the formation communication topology with system states,an event-trig-gered mechanism based on state observation is established.This mechanism leverages relative measurements among AUVs to mitigate delays and data loss caused by acoustic communication failures,while improving system observability in the absence of position information exchange.Second,a distributed model predictive controller based on Lyapunov theory is designed.The controller employs backstepping to construct contrac-tive constraints,ensuring recursive feasibility,and incorporates adaptive Kalman filtering(AKF)to compen-sate for measurement noise,thereby guaranteeing closed-loop stability.[Results]Simulation results of the formation control for five AUVs(1 leader and 4 followers)show that,compared with the traditional LMPC,the proposed ETMCU-LMPC method reduces the convergence time from 8 s to 6 s,the maximum error from 1.12 m to 0.36 m,and the steady-state error from 0.57 m to 0.06 m.Additionally,the control input exhibits greater stability.[Conclusion]The proposed method can effectively cope with communication anomalies,improve the relia-bility of multi-AUV formations under scenarios with limited state perception and transmission,and thus pos-sesses practical engineering significance.

徐博;左一兵;王朝阳;马雪飞;朱海峰

哈尔滨工程大学 智能科学与工程学院,黑龙江 哈尔滨 150001||哈尔滨工程大学 南海研究院,海南 三亚 572024哈尔滨工程大学 智能科学与工程学院,黑龙江 哈尔滨 150001||哈尔滨工程大学 南海研究院,海南 三亚 572024哈尔滨工程大学 智能科学与工程学院,黑龙江 哈尔滨 150001||中原工学院 智能感知与仪器学院,河南 郑州 451191哈尔滨工程大学 智能科学与工程学院,黑龙江 哈尔滨 150001||哈尔滨工程大学 南海研究院,海南 三亚 572024哈尔滨工程大学 智能科学与工程学院,黑龙江 哈尔滨 150001||哈尔滨工程大学 南海研究院,海南 三亚 572024

交通工程

自主水下航行器编队控制路径跟踪轨迹跟踪事件驱动量测-通信联合框架模型预测控制Lyapunov理论自适应卡尔曼滤波

autonomous underwater vehiclesformation controlpath followingtrajectory trackingevent-triggered metrology-communication unified frameworkmodel predictive controlLyapunov methodsadaptive Kalman filter(AKF)

《中国舰船研究》 2026 (2)

89-100,12

微系统技术国防科技重点实验室开放课题(6142804230106)

10.19693/j.issn.1673-3185.04586

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