Identificação de Sistemas Utilizando a Parametrização MOLI
In this thesis, new system identification algorithms are proposed for linear and time invariant systems with multiple input and single output. The system is described by a state-space model in the canonical observable form and represented by a Luenberger observer with a known state matrix. Thence, t...
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| Format: | masterThesis |
| Language: | por |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10216/113438 |
| Country: | Portugal |
| Oai: | oai:repositorio-aberto.up.pt:10216/113438 |
| Summary: | In this thesis, new system identification algorithms are proposed for linear and time invariant systems with multiple input and single output. The system is described by a state-space model in the canonical observable form and represented by a Luenberger observer with a known state matrix. Thence, the identification problem is reduced to the estimation of the system input matrix and the observer gain which can be performed by a simple Least Square Estimator. The quality of the estimator depends on the observer state matrix. In the proposed algorithms, this matrix is found by iterative processes using a barycenter and a subspace approaches. All algorithms are free derivative optimization methods. |
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