Identification of spatiotemporally invariant systems for control adaptation

  • Authors:

  • Azeem Sarwar;Petros G. Voulgaris;Srinivasa M. Salapaka

  • Affiliations:

  • University of Maryland, College Park, MD, USA;University of Illinois at Urbana Champaign, IL, USA;University of Illinois at Urbana Champaign, IL, USA

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2012

Quantified Score

Hi-index 22.14

Visualization

Abstract

We present a distributed projection algorithm for system identification of spatiotemporally invariant systems with the ultimate purpose of utilizing it in an indirect adaptive control scheme. Each subsystem communicates only with its immediate neighbors to share its current estimate along with a cumulative improvement index. On the basis of the cumulative improvement index, the best estimate available is picked in order to carry out the next iteration. For small estimation error, the scheme switches over to a ''smart'' averaging routine. The proposed algorithm guarantees to bring the local estimates arbitrarily close to one another, developing a ''local consensus'', which makes it amenable to control by the application of indirect distributed adaptive control schemes. It is also shown through simulations that the proposed algorithm has a clear advantage over the standard projection algorithm. Our proposed algorithm is also suitable for addressing the estimation problem in distributed networks that arise in a variety of applications, such as environment monitoring, target localization and potential sensor network problems.