AI 中文总结
本文针对分布式有偏最小共识协议衍生的GABF算法仅渐近稳定、收敛速度不明的问题，提出两种控制策略实现其规定时间镇定，并通过仿真验证了方法有效性。
AI 中文摘要
基于成熟的分布式有偏最小共识协议（一种高效解决分布式最短路径问题的方法），连续时间广义自适应贝尔曼-福特算法（GABF）通过适配多种形式的距离度量引入了灵活性，使其适用于更复杂场景，如时变最短路径问题和机器人路径规划。然而，现有针对该协议的研究主要聚焦于渐近稳定性，未涉及收敛速度，这限制了其实际应用。为解决这一缺口，本文提出两种控制策略，通过确保GABF在用户定义的时间内收敛到稳态值，实现其规定时间镇定，从而拓宽其适用性。本文提供了包含真实数据的机器人操纵器路径规划、基于学习的路径规划等仿真场景，以验证所提方法的有效性。
英文摘要
Building upon the well-established distributed biased min-consensus protocol, which serves as an efficient approach to address the shortest path problem in a distributed fashion, the continuous-time generalized adaptive Bellman-Ford algorithm (GABF) introduces flexibility by accommodating various forms of distance metrics. This adaptability makes GABF suitable for more complex scenarios, such as time-dependent shortest path problem and robotic path planning. However, existing research on this protocol primarily focuses on asymptotic stability, providing no insights into convergence speed, which limits its practical applications. To address this gap, this paper proposes two control strategies that achieve prescribed-time stabilization of GABF by ensuring its convergence to the stationary value within a user-defined time, thereby broadening its applicability. Simulation scenarios, including robotic manipulator path planning with real-world data and learning-based path planning, are provided to validate the effectiveness of the proposed approaches.
Comments12 pages, 5 figures
