Dissipativity learning control (DLC): Theoretical foundations of input–output data-driven model-free control

Wentao Tang, Prodromos Daoutidis

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Data-driven, model-free control strategies leverage statistical or learning techniques to design controllers based on data instead of dynamic models. We have previously introduced the dissipativity learning control (DLC) method, where the dissipativity property is learned from the input–output trajectories of a system, based on which L2-optimal P/PI/PID controller synthesis is performed. In this work, we analyze the statistical conditions on dissipativity learning that enable control performance guarantees, and establish theoretical results on performance under nominal conditions as well as in the presence of statistical errors. The implementation of DLC is further formalized and is illustrated on a two-phase chemical reactor, along with a comparison to model identification-based LQG control.

Original languageEnglish (US)
Article number104831
JournalSystems and Control Letters
StatePublished - Jan 1 2021

Bibliographical note

Funding Information:
This work was supported by NSF-CBET, USA .

Publisher Copyright:
© 2020 Elsevier B.V.


  • Data-driven control
  • Dissipative systems
  • Model-free control


Dive into the research topics of 'Dissipativity learning control (DLC): Theoretical foundations of input–output data-driven model-free control'. Together they form a unique fingerprint.

Cite this