Real-Time Reconfiguration and Connectivity Maintenance for AUVs Network Under External Disturbances using Distributed Nonlinear Model Predictive Control
Nhat Minh Nguyen, Stephen McIlvanna, Jack Close, Mien Van
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引用次数: 0
Abstract
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.
水下航行器技术的进步极大地扩展了在新型实际应用中部署自主或遥控水下航行器的潜在范围。然而,这些水下航行器的效率和可操控性仍然是至关重要的挑战,尤其是在动态水下环境中。在这项工作中,我们提出了一种新型控制方案,用于在单个代理之间通信有限的情况下创建多代理分布式编队控制。此外,多代理的编队可以实时重新配置,并保持网络连接。该方案的建议用例包括创建能适应环境或网络条件的水下移动通信网络,以保持通信链路的质量,用于远距离勘探、海底监测或水下基础设施检测。本研究介绍了一种新型分布式非线性模型预测控制(DNMPC)策略,该策略集成了控制李亚普诺夫函数(CLF)和具有宽松衰减率的控制屏障函数(CBF),特别适用于 6-DOF 水下机器人。通过对动态环境中的轨迹跟踪和编队重构进行严格的 MATLAB 仿真,证明了我们提出的 DNMPC 方案的有效性。我们的研究结果得到了使用软件循环(Software In The Loop,SITL)仿真进行的测试的支持,证实了该方法在实时场景中的适用性。