欠驱动
控制理论(社会学)
障碍物
补偿(心理学)
弹道
执行机构
计算机科学
避障
自适应控制
无人机
缩放比例
控制工程
跟踪(教育)
领域(数学)
动态缩放
控制器(灌溉)
扰动(地质)
矢量场
控制(管理)
模型预测控制
工程类
鲁棒控制
滑模控制
运动学
势场
控制系统
避碰
车辆动力学
混蛋
限制
碰撞
地平线
跟踪误差
运动控制
作者
Zihan Ding,Wei Shen,Guzi Xu,Yu Wu
标识
DOI:10.1016/j.oceaneng.2026.124442
摘要
• A multi-layer cooperative control framework integrating adaptive LOS guidance, DMA-AAPF, and NDOB-based ASMC is proposed for underactuated USV formations. • DMA-AAPF with velocity prediction and dynamic scaling enables smooth, proactive obstacle avoidance. • Energy-smoothing in DMA-AAPF suppresses trajectory oscillations and alleviates local minima. • Formation errors remain below 0.2 m under severe Sea State 2 disturbances. • Adaptive scaling of repulsive gains reduces peak control effort by more than 55% , limiting repulsive forces to below 9 N and preventing actuator saturation. Precise formation control and autonomous obstacle avoidance for underactuated unmanned surface vehicles (USVs) in dynamic marine environments remain challenging due to fixed guidance parameters and rigid potential fields. This paper proposes a multi-layer cooperative control framework featuring a Disturbance-Mediated Adaptive Artificial Potential Field (DMA-APF), integrated with an adaptive LOS guidance law and an adaptive sliding mode control (ASMC) scheme. The DMA-APF synchronizes its predictive horizon with USV surge velocity and dynamically scales repulsive gains according to real-time environmental threats. Simulation results demonstrate that the proposed framework significantly improves tracking accuracy and actuator efficiency. Steady-state formation errors are reduced from 0.4 m to within 0.2 m, and in dense obstacle scenarios, peak repulsive forces are limited to below 9 N, well below the 20 N saturation threshold. The results indicate that the DMA-APF effectively balances formation maintenance with safe, smooth, and energy-efficient navigation in complex maritime conditions.
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