Hybrid system identification: An SDP approach

C. Feng, C. M. Lagoa, N. Ozay, M. Sznaier

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

10 Scopus citations


The problem of identifying discrete time affine hybrid systems with noisy measurements is addressed in this paper. Given a finite number of measurements of input/output and a bound on the measurement noise, the objective is to identify a switching sequence and a set of affine models that are compatible with the a priori information, while minimizing the number of affine models. While this problem has been successfully addressed in the literature if the input/output data is noise-free or corrupted by process noise, results for the case of measurement noise are limited, e.g., a randomized algorithm has been proposed in a previous paper [3]. In this paper, we develop a deterministic approach. Namely, by recasting the identification problem as polynomial optimization, we develop deterministic algorithms, in which the inherent sparse structure is exploited. A finite dimensional semi-definite problem is then given which is equivalent to the identification problem. Moreover, to address computational complexity issues, an equivalent rank minimization problem subject to deterministic LMI constraints is provided, as efficient convex relaxations for rank minimization are available in the literature. Numerical examples are provided, illustrating the effectiveness of the algorithms.

Original languageEnglish (US)
Title of host publication2010 49th IEEE Conference on Decision and Control, CDC 2010
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Print)9781424477456
StatePublished - 2010
Event49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, United States
Duration: Dec 15 2010Dec 17 2010

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference49th IEEE Conference on Decision and Control, CDC 2010
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

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


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