Identification of time-varying linear models

1 January 1983

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

Vol. 22, 604-606
https://doi.org/10.1109/cdc.1983.269589

Abstract

This paper presents a convergence analysis of a least squares type recursive identification algorithm for time-varying linear-in-the parameters models. It is shown that under persistent excitation conditions, the number of the associated gain matrix is bounded. This is obtained by normalizing the observation vector entering in the identification algorithm. This allows a simple convergence analysis to study the influence of plant parameters changes and noise in the estimates.

Keywords

This publication has 5 references indexed in Scilit:

Persistence of excitation conditions and the convergence of adaptive schemes
IEEE Transactions on Information Theory, 1984
Convergence analysis of recursive identification algorithms with forgetting factor
Automatica, 1983
Persistence of excitation in extended least squares
IEEE Transactions on Automatic Control, 1983
Exponential convergence of adaptive identification and control algorithms
Automatica, 1982
Unification of discrete time explicit model reference adaptive control designs
Automatica, 1981

Cited by 5 articles