基于分布式滑模预测控制的AUV集群多目标围捕方法

Distributed sliding model predictive control-based multi-target enclosing for autonomous underwater vehicle swarm

摘要

针对自主水下潜航器集群多目标围捕任务中存在的局部感知受限、模型不确定性及未知外部扰动等问题，提出一种融合分布式状态观测与模型预测控制的分组协同围捕方案。首先，构建多目标围捕任务场景下的潜航器集群动力学模型，设计具备局部信息融合能力的分布式状态观测器，实现多目标状态的并行估计与全局一致性重构。进而，基于任务关联性与空间拓扑特征，将集群划分为若干功能子群体，形成“子群体–目标”耦合结构。在此基础上，针对每个子群体设计带约束的模型预测控制器，通过在有限时域内滚动求解最优控制序列，实现对各自目标的动态围捕。所提方法在控制过程中综合考虑输入饱和及协同约束，兼顾控制精度与系统鲁棒性。仿真结果验证了该方法在多目标动态环境下的有效性与稳定性，具有良好的工程适应性与推广潜力。

Abstract

To address the challenges of limited local perception, model uncertainty, and unknown external disturbances in multi-target enclosing tasks for autonomous underwater vehicle (AUV) swarms, this paper proposes a group cooperative enclosing strategy that integrates a distributed observer with model predictive control (MPC). First, a dynamic model of the AUV swarm is constructed under a multi-target enclosing scenario. A distributed observer with local information fusion capability is designed to enable parallel estimation of multiple target states and achieve global consensus-based reconstruction. Subsequently, based on task relevance and spatial topology, the swarm is divided into several functional subgroups, forming a dynamic “subgroup–target” coupling structure. For each subgroup, a constrained MPC controller is designed to dynamically enclose its assigned target by solving an optimal control sequence over a finite prediction horizon in a receding horizon fashion. The proposed approach explicitly considers input saturation and inter-agent cooperation constraints, thereby achieving a balance between control accuracy and system robustness. Simulation results demonstrate the effectiveness and stability of the proposed method in dynamic multi-target environments, highlighting its strong engineering applicability and potential for practical deployment.

关键词

AUV集群 / 多目标围捕 / 滑模预测控制 / 分布式观测器

Key words

autonomous underwater vehicle swarm / multi-target enclosing / sliding model predictive control / distributed observer

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基金资助

四川省自然科学基金面上重点项目(2024NSFSC0021)

中央高校基本科研业务费专项资金(ZYGX2025K028)