Dissipativity learning control (DLC): A framework of input–output data-driven control

Wentao Tang, Prodromos Daoutidis

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The paper addresses data-driven control based on input–output data in the absence of an underlying dynamic model. It proposes a dissipativity learning control (DLC) framework which involves the data-based learning of the dissipativity property of the control system, followed by a dissipativity-based controller design procedure. Specifically, independent component analysis and parametric distribution inference are adopted to estimate a polyhedral region of input–output trajectory samples, whose dual cone characterizes the dissipativity property; subsequently, an optimal controller in the L2 sense is designed by solving a nonlinear semidefinite programming problem. The applicability of the proposed method is demonstrated by case studies on regulating control of a polymerization reactor and tracking control of an oscillatory chemical reactor.

Original languageEnglish (US)
Article number106576
JournalComputers and Chemical Engineering
Volume130
DOIs
StatePublished - Nov 2 2019

Bibliographical note

Funding Information:
This work was supported by National Science Foundation (NSF-CBET) and a Doctoral Dissertation Fellowship (DDF) of University of Minnesota for Wentao Tang.

Keywords

  • Data-driven control
  • Dissipative systems
  • Machine learning

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