Abstract
In this paper, we present an identification algorithm for continuous-time Multiple-Input Multiple-Output (MIMO) state-space models and also for determining the order of models from the samples of the input-output data. In the proposed algorithm, from the sampled data first an equivalent discrete-model is identified, then the model is transformed to the corresponding continuous-time model. The parametric discrete-time canonical-formed MIMO state-space model is identified based on Maximum-Likelihood (ML) and Akaike's Information Criterion(AIC). For obtaining the maximum-likelihood estimates of the model parameters, we apply Expectation-Maximization(EM) algorithms which are iterative methods such that the choice of the initial estimates is most important. The initial estimates of parameters in canonical-formed state-space models are obtained by MOESP[1] or N4SID[2] methods where the similarity transformation plays a key role.
