Attack-resilient distributed formation control via online adaptation

Minghui Zhu, Sonia Martínez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations

Abstract

This paper investigates a distributed formation control problem in an operator-vehicle network where each vehicle is remotely controlled by an operator. Each operator-vehicle pair is attacked by an adversary, who corrupts the commands sent from the operator to the vehicle following a partially unknown strategy. We propose a novel distributed control algorithm that allows operators to adapt their policies online by exploiting the latest collected information about adversaries. The algorithm enables vehicles to asymptotically achieve the desired formation from any initial configuration and initial estimate of the adversaries' strategies. It is shown that the sequence of the distances to the desired formation is summable. A numerical example is provided to illustrate the performance of the algorithm. In particular, we observe that the rate of convergence to the desired formation is exponential, outperforming our theoretical result.

Original languageEnglish (US)
Title of host publication2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6624-6629
Number of pages6
ISBN (Print)9781612848006
DOIs
StatePublished - 2011
Event2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 - Orlando, FL, United States
Duration: Dec 12 2011Dec 15 2011

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Other

Other2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
Country/TerritoryUnited States
CityOrlando, FL
Period12/12/1112/15/11

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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