Subspace identification of linear systems with partial eigenvalue constraints.
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2019
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Resumo
For subspace identification methods with eigenvalue
constraints, the constraints are enforced by means of an optimization problem subject to LMI constraints. First principals or step
response tests could be used as a source of auxiliary information
in order to build LMI regions. In these cases, all the eigenvalues
of the identified state-space model are subject to the same
constraints. However, it often happens that the non-dominant
eigenvalues have larger real part or larger natural frequencies.
In this paper, we propose a two-step method in order to constrain
the dominant dynamics of SISO models into LMI regions. In
virtue of this result, in addition, the model eigenvalues could
be constrained into disjoint LMI regions. Numerical examples
illustrate the effectiveness of our proposed method.
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Gray-box identification, Linear matrix inequalities, Single-input single-output systems
Citação
RICCO, R. A. et al. Subspace identification of linear systems with partial eigenvalue constraints. IEEE Latin America Transactions, v. 17, n. 2, p. 288-296, fev. 2019. Disponível em: <https://ieeexplore.ieee.org/document/8863175>. Acesso em: 10 mar. 2020.