Recursive identification of multivariable systems using matchable-observable linear models

Abstract

This paper presents a recursive parameter estimation algorithm based on a matchable-observable parameterization of multivariable process models. As a consequence of the properties of the models used, no undesired pole-zero cancellations appear, the number of model parameters is not excessive, linear least-squares estimation methods are applicable, and parameter estimation can be accomplished without the need for iterative or nonlinear optimization. The performance of the algorithm developed is assessed in comparison with a well-established recursive subspace method, in a simulation study with time-invariant and time-varying scenarios. The results obtained demonstrate the accuracy and effectiveness of the proposed approach. © 2016 IEEE.