The distributed biased min-consensus protocol revisited: pre-specified finite time control strategies and small-gain based analysis

TL;DR

This paper enhances the distributed biased min-consensus protocol by introducing finite-time control strategies and employing small-gain analysis to ensure stability and convergence within user-defined time frames.

Contribution

It proposes two control strategies for finite-time convergence and applies small-gain based analysis to establish global exponential stability of the protocol.

Findings

Control strategies guarantee convergence to desired error levels.

Small-gain analysis proves global exponential input-to-state stability.

Simulations validate theoretical stability and convergence results.

Abstract

Unlike the classical distributed consensus protocols enabling the group of agents as a whole 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 generate advanced complexity pertaining to solving the shortest path problem. As such a protocol is commonly incorporated as the first step of a hierarchical architecture in real applications, e.g., robots path planning, management of dispersed computing services, an impedance limiting the application potential of DBMC lies in, the lack of results regarding to its convergence within a user-assigned time. In this paper, we first propose two control strategies ensuring the state error of DBMC decrease exactly to zero or a desired level manipulated by the user, respectively. To compensate the high feedback gains incurred by these two control strategies, this paper further investigates the nominal DBMC itself. By leveraging small gain based stability tools, this paper also proves the global exponential input-to-state stability of DBMC, outperforming its current stability results. Simulations have been provided to validate the efficacy of our theoretical result.