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Subspace Identification Methods for Hammerstein Systems: Rank Constraint and Dimension Problem

Abstract : This article studies the subspace identification methods (SIMs) for Hammerstein systems with major focus on a rank constraint and the related dimension problem. We analyse the effects of the rank constraint on the three steps of a unifying framework for SIMs: the rank constraint has no effect on the first two steps, but does so on the third step. If the rank constraint is ignored, as in the existing over-parametrised method (OPM) for Hammerstein system identification, the optimality of the resulting estimate can still be established. Even so, the OPM may suffer from the dimension problem resulting in a low numerical efficiency. To resolve the dimension problem, we propose a new subspace-based method, named as the least-parametrised method (LPM), for identification of Hammerstein systems with non-coupling input nonlinearities. Simulation results are provided to demonstrate the effectiveness of the LPM, and show the necessity of considering the rank constraint to improve the numerical efficiency.
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https://hal.inria.fr/hal-00777470
Contributor : Qinghua Zhang <>
Submitted on : Thursday, January 17, 2013 - 3:33:26 PM
Last modification on : Tuesday, December 10, 2019 - 7:56:01 PM

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Chunyun Yan, Jiandong Wang, Qinghua Zhang. Subspace Identification Methods for Hammerstein Systems: Rank Constraint and Dimension Problem. International Journal of Control, Taylor & Francis, 2010, 83 (11), pp.2204-2216. ⟨10.1080/00207179.2010.506658⟩. ⟨hal-00777470⟩

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