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.