Model learning for switching linear systems with autonomous mode transitions

Lars Blackmore, Stephanie Gil, Seung Chung, Brian Williams

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

15 Scopus citations

Abstract

We present a novel method for model learning in hybrid discrete-continuous systems. The approach uses approximate Expectation-Maximization to learn the Maximum-Likelihood parameters of a switching linear system. The approach extends previous work by 1) considering autonomous mode transitions, where the discrete transitions are conditioned on the continuous state, and 2) learning the effects of control inputs on the system. We evaluate the approach in simulation.

Original languageEnglish (US)
Title of host publicationProceedings of the 46th IEEE Conference on Decision and Control 2007, CDC
Pages4648-4655
Number of pages8
DOIs
StatePublished - Dec 1 2007
Event46th IEEE Conference on Decision and Control 2007, CDC - New Orleans, LA, United States
Duration: Dec 12 2007Dec 14 2007

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other46th IEEE Conference on Decision and Control 2007, CDC
CountryUnited States
CityNew Orleans, LA
Period12/12/0712/14/07

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Fingerprint Dive into the research topics of 'Model learning for switching linear systems with autonomous mode transitions'. Together they form a unique fingerprint.

  • Cite this

    Blackmore, L., Gil, S., Chung, S., & Williams, B. (2007). Model learning for switching linear systems with autonomous mode transitions. In Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC (pp. 4648-4655). [4434779] (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2007.4434779