Decentralised Probabilistic Consensus Control for Stochastic Complex Dynamical Networks

Research output: Contribution to journalArticlepeer-review

Abstract

This letter is concerned with the consensus analysis and control problems for a class of stochastic complex dynamical networks (SCDNs) that consists of a large number of interconnected nodes. In particular, a unified probabilistic decentralised consensus control framework is established where decentralised randomised controllers are designed such that the individual subsystems in a network synchronise their states with each other to achieve consensus of the whole network. The proposed framework is quite general, where all the components within this framework including local controllers, systems' models, and communications between the subsystems of a complex system are modelled using probabilistic models. The general solution for arbitrary probabilistic models of the framework components is obtained then demonstrated on a class of linear Gaussian complex systems, thus obtaining the desired results. Furthermore, a numerical example is presented to illustrate the effectiveness and the usefulness of the theoretical development.

Original languageEnglish
Article number9123427
Pages (from-to)577-582
Number of pages6
JournalIEEE Control Systems Letters
Volume5
Issue number2
DOIs
Publication statusPublished - 23 Jun 2020

Bibliographical note

© 2020 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.

Keywords

  • Fully probabilistic design
  • consensus control
  • coupled stochastic complex systems
  • decentralised control

Fingerprint Dive into the research topics of 'Decentralised Probabilistic Consensus Control for Stochastic Complex Dynamical Networks'. Together they form a unique fingerprint.

Cite this