Boundary-locked event-triggered mechanism-based adaptive flocking control for multi-USV systems
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an adaptive flocking control law for unmanned surface vehicle swarms (USVS) to improve coordination. The novel approach enhances flock cohesion, reduces communication load, and boosts robustness against disturbances.
Area Of Science
- Marine robotics
- Control systems engineering
- Artificial intelligence
Background
- Unmanned surface vehicle swarms (USVS) are vital for marine operations but face coordination challenges.
- Flocking cohesion, communication, and uncertainty hinder effective USVS deployment.
- Existing control strategies struggle with dynamic marine environments and communication constraints.
Purpose Of The Study
- To develop an event-based adaptive flocking control law for USVS.
- To enhance flock cohesion and ensure network connectivity.
- To reduce communication overhead and improve system robustness against uncertainties.
Main Methods
- Designed an integrated dual-mode potential function (APF and DCPF) for flock cohesion and connectivity.
- Implemented a boundary-locked event-triggered (BLET) mechanism with MIET and MTIT to minimize communication.
- Incorporated a reinforcement learning-based echo state network (RLESN) for adaptive control and disturbance rejection.
Main Results
- The proposed control law effectively enhances flock cohesion and maintains connectivity.
- The BLET mechanism significantly reduces communication load while ensuring system stability.
- The RLESN successfully compensates for unmodeled dynamics and external disturbances, improving robustness.
Conclusions
- The developed event-based adaptive flocking control law offers a robust and efficient solution for USVS coordination.
- The integrated approach addresses key challenges in flock cohesion, communication, and uncertainty management.
- Simulation results validate the superiority of the proposed methodology over existing methods.
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