Flocking for multi-agent systems with optimally rigid topology based on information weighted Kalman consensus filter

Xiaoyuan Luo, Xiaolei Li, Shaobao Li, Zhong-Ping Jiang, Xinping Guan

Research output: Contribution to journalArticle

Abstract

This paper investigates the leader-follower flocking problem of multi-agent systems. The leader with input noise is estimated by a proposed continuous-time information weighted Kalman consensus filter (IWKCF) for agents. A novel distributed flocking algorithm based on the IWKCF is further presented to make agents achieve flocking to the leader. It is shown that the proposed flocking algorithm based on the continuous-time IWKCF is asymptotically stable. Applying the topology optimization scheme, the communication complexity of system topologies of multi-agent systems is effectively reduced. Finally, simulations are provided to demonstrate the effectiveness of the proposed results.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalInternational Journal of Control, Automation and Systems
DOIs
StateAccepted/In press - Dec 23 2016

Fingerprint

Multi agent systems
Kalman filters
Topology
Shape optimization
Parallel algorithms
Communication

Keywords

  • Consensus estimation
  • flocking control
  • multi-agent systems
  • topology optimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications

Cite this

Flocking for multi-agent systems with optimally rigid topology based on information weighted Kalman consensus filter. / Luo, Xiaoyuan; Li, Xiaolei; Li, Shaobao; Jiang, Zhong-Ping; Guan, Xinping.

In: International Journal of Control, Automation and Systems, 23.12.2016, p. 1-11.

Research output: Contribution to journalArticle

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