基于分布式滑模预测控制的AUV集群多目标围捕方法OA

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.