Auxiliary model-based interval-varying multi-innovation least squares identification for multivariable OE-like systems with scarce measurements

• Identification method is for multivariable OE-like systems with scarce measurements.
• The auxiliary model-based multi-innovation least squares algorithm is proposed.
• The interval-varying recursive computation is to skip the unavailable measurements including outliers.
• For scarce measurement patterns, the convergence analysis of the proposed identification method is given.
• The identification model and the proposed method are still effective for sampled continuous multivariable systems.

The identification problem of multivariable OE-like systems with scarce measurements is considered in this paper. By replacing the unknown inner variables in the information matrix with the outputs of the auxiliary model and by expanding the scalar innovation to an innovation vector, an auxiliary model-based multi-innovation least squares (AM-MILS) algorithm is proposed. In order to deal with the scarce measurement pattern, the algorithm takes the form of interval-varying recursive computation to skip the unavailable measurements including outliers. The introduction of the multi-innovation concept improves the parameter estimation accuracy and makes the identification algorithm more efficient. The convergence analysis shows that for the proposed algorithm, the parameter estimates can converge to their true values in the scarce output measurement pattern. Illustrative examples are given to demonstrate the effectiveness and accuracy of the proposed method.