Real-Time Reconfiguration and Connectivity Maintenance for AUVs Network Under External Disturbances using Distributed Nonlinear Model Predictive Control
<|reference_start|>Real-Time Reconfiguration and Connectivity Maintenance for AUVs Network Under External Disturbances using Distributed Nonlinear Model Predictive Control: Advancements in underwater vehicle technology have significantly expanded the potential scope for deploying autonomous or remotely operated underwater vehicles in novel practical applications. However, the efficiency and maneuverability of these vehicles remain critical challenges, particularly in the dynamic aquatic environment. In this work, we propose a novel control scheme for creating multi-agent distributed formation control with limited communication between individual agents. In addition, the formation of the multi-agent can be reconfigured in real-time and the network connectivity can be maintained. The proposed use case for this scheme includes creating underwater mobile communication networks that can adapt to environmental or network conditions to maintain the quality of communication links for long-range exploration, seabed monitoring, or underwater infrastructure inspection. This work introduces a novel Distributed Nonlinear Model Predictive Control (DNMPC) strategy, integrating Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF) with a relaxed decay rate, specifically tailored for 6-DOF underwater robotics. The effectiveness of our proposed DNMPC scheme was demonstrated through rigorous MATLAB simulations for trajectory tracking and formation reconfiguration in a dynamic environment. Our findings, supported by tests conducted using Software In The Loop (SITL) simulation, confirm the approach's applicability in real-time scenarios.<|reference_end|>
@article{nguyen2024real-time,
    title={Real-Time Reconfiguration and Connectivity Maintenance for AUVs Network
  Under External Disturbances using Distributed Nonlinear Model Predictive
  Control},
    author={Nhat Minh Nguyen, Stephen McIlvanna, Jack Close, and Mien Van},
    journal={arXiv preprint arXiv:2403.15671},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15671},
    primaryClass={eess.SY cs.SY}
}