Identification of parsimonious continuous time LTI models with applications

Identifying lower order models is desirable both for control design and prediction purposes. In a few cases, a lower order model can be further reduced so that it contains the fewest number of parameters. In this paper, a sparsity seeking optimization method is proposed to identify such parsimonious continuous time (CT) linear time invariant (LTI) models. Theoretical analysis of convergence of estimates is presented. Numerical results on a variety of systems show that the algorithm accurately estimates the model parameters. Further, Monte Carlo simulations are used to verify the statistical convergence properties of the parameter estimates. Identification of a reduced order CT LTI model of tanks in series system demonstrates the practical applicability of the proposed method.