The Distributed Biased Min-Consensus Protocol Revisited: Pre-Specified Finite Time Control Strategies and Small-Gain Based Analysis
Mo, Yuanqiu; Wang, He; Li, Shuai; Yu, Wenwu (2025-06-20)
IEEE
20.06.2025
Y. Mo, H. Wang, S. Li and W. Yu, "The Distributed Biased Min-Consensus Protocol Revisited: Prespecified Finite Time Control Strategies and Small-Gain-Based Analysis," in IEEE Transactions on Automatic Control, vol. 70, no. 12, pp. 8210-8225, Dec. 2025, doi: 10.1109/TAC.2025.3581845
https://rightsstatements.org/vocab/InC/1.0/
© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202604282806
https://urn.fi/URN:NBN:fi:oulu-202604282806
Tiivistelmä
Abstract
Unlike the classical distributed consensus protocols that enable a group of agents to reach an agreement regarding a certain quantity of interest in a distributed fashion, the distributed biased min-consensus protocol (DBMC) has been proven to handle the advanced complexity of solving the shortest path problem. Such a protocol is commonly incorporated as the first step of a hierarchical architecture in real applications, such as robot path planning and the management of dispersed computing services. However, a major limitation of DBMC is the lack of results regarding its convergence within a user-assigned time frame. In this article, we first propose two control strategies to ensure that the state error of DBMC decreases exactly to zero or a desired level within a finite time specified by the user. This article further investigates the nominal DBMC itself. By leveraging small-gain-based stability tools and embedding DBMC into a framework applicable to such tools, this article also proves the global exponential input-to-state stability of DBMC, outperforming its current stability results. Simulations are provided to validate the efficacy of our theoretical results.
Unlike the classical distributed consensus protocols that enable a group of agents to reach an agreement regarding a certain quantity of interest in a distributed fashion, the distributed biased min-consensus protocol (DBMC) has been proven to handle the advanced complexity of solving the shortest path problem. Such a protocol is commonly incorporated as the first step of a hierarchical architecture in real applications, such as robot path planning and the management of dispersed computing services. However, a major limitation of DBMC is the lack of results regarding its convergence within a user-assigned time frame. In this article, we first propose two control strategies to ensure that the state error of DBMC decreases exactly to zero or a desired level within a finite time specified by the user. This article further investigates the nominal DBMC itself. By leveraging small-gain-based stability tools and embedding DBMC into a framework applicable to such tools, this article also proves the global exponential input-to-state stability of DBMC, outperforming its current stability results. Simulations are provided to validate the efficacy of our theoretical results.
Kokoelmat
- Avoin saatavuus [42834]