Weighted H mixed-sensitivity minimization for stable distributed parameter plants under sampled-data control

Delano R. Carter, Armando Rodriguez

Research output: Contribution to journalArticle

Abstract

This paper considers the problem of designing near-optimal finite-dimensional controllers for stable multiple-input multiple-output (MIMO) distributed parameter plants under sampled-data control. A weighted H-style mixed-sensitivity measure which penalizes the control is used to define the notion of optimality. Controllers are generated by solving a "natural" finite-dimensional sampled-data optimization. A priori computable conditions are given on the approximants such that the resulting finite-dimensional controllers stabilize the sampled-data controlled distributed parameter plant and are near-optimal. The proof relies on the fact that the control input is appropriately penalized in the optimization. This technique also assumes and exploits the fact that the plant can be approximated uniformly by finite-dimensional systems. Moreover, it is shown how the optimal performance may be estimated to any desired degree of accuracy by solving a single finite-dimensional problem using a suitable finite-dimensional approximant. The constructions given are simple. Finally, it should be noted that no infinite-dimensional spectral factorizations are required. In short, the paper provides a straight forward control design approach for a large class of MIMO distributed parameter systems under sampled-data control.

Original languageEnglish (US)
Pages (from-to)527-554
Number of pages28
JournalKybernetika
Volume35
Issue number5
StatePublished - Dec 1 1999

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Information Systems
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Weighted H<sup>∞</sup> mixed-sensitivity minimization for stable distributed parameter plants under sampled-data control'. Together they form a unique fingerprint.

  • Cite this