Dynamic maximum entropy algorithms for clustering and coverage control

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

Abstract

The dynamic coverage problem is increasingly found in a wide variety of areas, for example, from the development of mobile sensor networks, to the analysis of clustering in spatio-temporal dynamics of brain signals. In this paper, we apply control-theoretic methods to locate and track cluster center dynamics and show that dynamic control design is necessary to achieve dynamic coverage of mobile objects under acceleration fields. This is the first work to consider tracking cluster centers when site dynamics involve accelerations. We focus on the relationship between the objective of maximizing coverage in real-time and the Maximum Entropy Principle, and develop the ability to identify inherent cluster dynamics in a dataset. Algorithms are presented that guarantee asymptotic tracking of cluster centers, and for which we prove continuity and boundedness of the corresponding control laws. Simulations are provided to corroborate these results.

Original languageEnglish (US)
Title of host publication2010 49th IEEE Conference on Decision and Control, CDC 2010
Pages1836-1841
Number of pages6
DOIs
StatePublished - 2010
Event2010 49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, GA, United States
Duration: Dec 15 2010Dec 17 2010

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other2010 49th IEEE Conference on Decision and Control, CDC 2010
CountryUnited States
CityAtlanta, GA
Period12/15/1012/17/10

ASJC Scopus subject areas

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

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