Coupled‐least‐squares identification for multivariable systems

Abstract

This article studies identification problems of multiple linear regression models, which may be described a class of multi‐input multi‐output systems (i.e. multivariable systems). Based on the coupling identification concept, a novel coupled‐least‐squares (C‐LS) parameter identification algorithm is introduced for the purpose of avoiding the matrix inversion in the multivariable recursive least‐squares (RLS) algorithm for estimating the parameters of the multiple linear regression models. The analysis indicates that the C‐LS algorithm does not involve the matrix inversion and requires less computationally efforts than the multivariable RLS algorithm, and that the parameter estimates given by the C‐LS algorithm converge to their true values. Simulation results confirm the presented convergence theorems.