Mixed \(L_p\) estimators variety for model order reduction in control oriented system identification (Q1665419)

Summary: A new family of MLE type \(L_p\) estimators for model order reduction in dynamical systems identification is presented in this paper. A family of \(L_p\) distributions proposed in this work combines \(L_{p_2}\) (\(1 < p_2 < 2\)) and \(L_{p_1}\) (\(0 < p_1 < 1\)) distributions which are quantified by four parameters. The main purpose is to show that these parameters add degrees of freedom (DOF) in the estimation criterion and reduce the estimated model complexity. Convergence consistency properties of the estimator are analysed and the model order reduction is established. Experimental results are presented and discussed on a real vibration complex dynamical system and pseudo-linear models are considered.