Novel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary model

This communique uses the auxiliary model method to study the identification problem of a multiple input multiple-output (MIMO) system. For such a MIMO system whose outputs are contaminated by an ARMA noise process (i.e., correlated noise), an auxiliary model based recursive least squares parameter estimation algorithm is presented through filtering input-output data. The proposed algorithm has higher estimation accuracy than the existing multivariable identification algorithm. The simulation example is given. (C) 2016 Elsevier Ltd. All rights reserved.