Hierarchical Least Squares Identification for Linear SISO Systems With Dual-Rate Sampled-Data

Abstract

This technical note studies identification problems for dual-rate sampled-data linear systems with noises. A hierarchical least squares (HLS) identification algorithm is presented to estimate the parameters of the dual-rate ARMAX models. The basic idea is to decompose the identification model of a dual-rate system into several sub-identification models with smaller dimensions and fewer parameters. The proposed algorithm is more computationally efficient than the recursive least squares (RLS) algorithm since the RLS algorithm requires computing the covariance matrix of large sizes, while the HLS algorithm deals with the covariance matrix of small sizes. Compared with our previous work, a detailed study of the HLS algorithm is conducted in this technical note. The performance analysis and simulation results confirm that the estimation accuracy of the proposed algorithm are close to that of the RLS algorithm, but the proposed algorithm retains much less computational burden.