arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2407.00570eess.SYcs.SY
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模型参考自适应控制在无人机网络多智能体同步中的应用

An Application of Model Reference Adaptive Control for Multi-Agent Synchronization in Drone Networks

Miguel F. Arevalo-Castiblanco, Yejin Wi, Marzia Cescon and, Cesar A. Uribe

AI总结:

本文提出分布式模型参考自适应控制策略,用于无人机网络的多智能体鲁棒同步,通过实时参数更新减少能耗并延长运行时间,实验证明其在垂直速度控制上优于经典PID控制器。

摘要:

本文介绍了分布式模型参考自适应控制(DMRAC)策略在无人机网络鲁棒多智能体同步中的应用。所提出的方法能够开发出可适应智能体之间实际模型参数差异的控制器,从而提升整体网络性能。我们将自适应控制律与经典PID控制器在参考跟踪任务中的性能进行了比较。每架跟随无人机都配备了一个模型参考自适应控制器,该控制器根据实时反馈和参考模型信息持续更新其参数。这种自适应性确保了足够的性能,与传统的非自适应技术相比,能够减少所需能量,从而延长无人机的运行时间。实验结果,尤其是在垂直速度控制方面,突显了所提方法在实现同步行为方面的有效性。

英文摘要:

This paper presents the application of a Distributed Model Reference Adaptive Control (DMRAC) strategy for robust multi-agent synchronization of a network of drones. The proposed approach enables the development of controllers capable of accommodating differences in real-life model parameters between agents, thereby enhancing overall network performance. We compare the performance of the adaptive control laws with classical PID controllers for the reference tracking task. Each follower drone has a model reference adaptive controller that continuously updates its parameters based on real-time feedback and reference model information. This adaptability ensures an adequate performance that, compared to conventional non-adaptive techniques, can reduce the amount of energy required and consequently increase the operating duration of the drones. The experimental results, particularly in vertical velocity control, underscore the effectiveness of the proposed approach in achieving synchronized behavior.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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