An auxiliary model based on a recursive least-squares parameter estimation algorithm for non-uniformly sampled multirate systems

Abstract

The lifted state-space models for a class of multirate systems non-uniformly sampled from their continuous-time systems are derived, and the corresponding input-output relationship is obtained. For unmeasurable information vectors arising in the identification models, this paper gives an auxiliary-model-based recursive least-squares (AM-RLS) identification algorithm to estimate the parameters of non-uniformly sampled data systems using the auxiliary model method. The basic idea is to replace the unknown inner variables in the information vector with the outputs of the auxiliary model. Convergence properties of the algorithm proposed show that the parameter estimation error consistently converges to zero under the generalized excitation condition and bounded noise variance. A simulation example is included.