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Published December 2012 | public
Book Section - Chapter

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

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.

Additional Information

© 2012 IEEE. This work was supported in part by NSF grant ECCS–0901433; AFOSR grant FA9559–12–1–0271; and DHS grant 2008-ST-061-ED0001.

Additional details

Created:
August 19, 2023
Modified:
October 24, 2023