A convex optimization approach to model (in)validation of switched ARX systems with unknown switches

Y. Cheng, Y. Wang, M. Sznaier, N. Ozay, C. Lagoa

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations

Abstract

This paper considers the problem of (in)validating switched affine models from noisy experimental data, in cases where the mode-variable is not directly observable. This problem, the dual of identification, is a crucial step when designing controllers using models identified from experimental data. Our main results are convex certificates, obtained by exploiting a combination of sparsification and polynomial optimization tools, for a given model to either be consistent with the observed data or be invalidated by it. These results are illustrated using both academic examples and a non-trivial application: detecting abnormal activities using video data.

Original languageEnglish (US)
Article number6426518
Pages (from-to)6284-6290
Number of pages7
JournalProceedings of the IEEE Conference on Decision and Control
DOIs
StatePublished - 2012
Event51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States
Duration: Dec 10 2012Dec 13 2012

All Science Journal Classification (ASJC) codes

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

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