Filtering-based recursive least squares estimation approaches for multivariate equation-error systems by using the multiinnovation theory

This article researches the filtering-based parameter estimation issues for a class of multivariate control systems with colored noise. A filtering-based recursive generalized extended least squares algorithm is derived, in which the data filtering technique is used for transforming the original system into two subidentification systems and the least squares principle is used for estimating parameters of these two subsystems. Furthermore, in order to improve the parameter estimation accuracy, the multiinnovation theory is added for deducing a filtering-based multiinnovation recursive generalized extended least squares algorithm. The numerical example confirms that these two proposed algorithms are effective.

Automated summary

This article proposes a solution to the parameter estimation issues for a class of multivariate control systems with colored noise, deriving two different least squares algorithms and confirming their effectiveness through numerical examples.