On constructing multiple Lyapunov functions for tracking control of multiple agents with switching topologies

Guanghui Wen, Wei Xing Zheng

Research output: Contribution to journalArticlepeer-review

Abstract

Distributed consensus tracking for linear multi-agent systems (MASs) with directed switching topology and a dynamic leader is investigated in this paper. By fully considering the special feature of Laplacian matrices for topology candidates, several new classes of MLFs are constructed in this paper for leader-following MASs with respectively an autonomous leader and a nonautonomous leader. Under the condition that each possible topology graph contains a spanning tree rooted at the leader node, some efficient criteria for achieving consensus tracking in the considered MASs are provided. Specifically, it is proven that consensus tracking in the closed-loop MASs can be ensured if the average dwell time (ADT) for switching among different topologies is larger than a derived positive quantity and the control parameters in tracking protocols are appropriately designed. It is further theoretically shown that the present Lyapunov inequality based criteria for consensus tracking with an autonomous leader are much less conservative than the existing ones derived by the M-matrix theory. The results are then extended to the case where the topology graph only frequently contains a directed spanning tree as the MASs evolve over time. At last, numerical simulations are performed to illustrate the effectiveness of the analytical analysis and the advantages of the proposed MLFs.
Original languageEnglish
Pages (from-to)3796-3803
Number of pages8
JournalIEEE Transactions on Automatic Control
Volume64
Issue number9
DOIs
Publication statusPublished - 2019

Keywords

  • Lyapunov functions
  • computer algorithms
  • computer networks
  • multiagent systems
  • topology

Fingerprint

Dive into the research topics of 'On constructing multiple Lyapunov functions for tracking control of multiple agents with switching topologies'. Together they form a unique fingerprint.

Cite this