Filtering‐based iterative identification for multivariable systems

This study applies the filtering technique to system identification to study the data filtering‐based parameter estimation methods for multivariable systems, which are corrupted by correlated noise – an autoregressive moving average process. To solve the difficulty that the identification model contains the unmeasurable variables and noise terms in the information matrix, the authors present a hierarchical gradient‐based iterative (HGI) algorithm by using the hierarchical identification principle. To improve the convergence rate, they apply the filtering technique to derive a filtering‐based HGI algorithm and a filtering‐based hierarchical least squares‐based iterative (HLSI) algorithm. The simulation examples indicate that the filtering‐based HLSI algorithm has the highest computational efficiency among these three algorithms.