Identificação de Sistemas Utilizando a Parametrização MOLI - Patrícia Gomes Saraiva (2018)
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.