Control-Oriented Learning of Lagrangian and Hamiltonian Systems

Mohamadreza Ahmadi, Ufuk Topcu, Clarence Rowley

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

12 Scopus citations


We propose a method based on quadratic programming for learning control-oriented models of physical systems for which limited data from only one trajectory is available. To this end, we take advantage of the principle of least action from physics. We propose two methods based on quadratic programming to approximate either the Lagrangian or the Hamiltonian of the system from the data. We show how the learning methods based on convex optimization can accommodate symmetries about the underlying system if they are known a priori. Furthermore, we incorporate the error in the approximation to build a data-driven differential inclusion, that is suitable for control purposes. We illustrate the results by an example.

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781538654286
StatePublished - Aug 9 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2018 Annual American Control Conference, ACC 2018
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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