Unbiased identification of a class of multi-input single-output systems with correlated disturbances using bias compensation methods. (English) Zbl 1219.93141

Summary: This paper studies the modelling and identification problems for multi-input single-output (MISO) systems with colored noises. In order to obtain the unbiased recursive estimates of the systems, this paper presents a recursive least squares (RLS) identification algorithm based on bias compensation technique. The basic idea is to eliminate the estimation bias by adding a correction term in the least squares (LS) estimates, a set of stable digital prefilters are suitably designed to preprocess the input sampled data from multi-input channels for the purpose of getting the bias term arisen by colored noises in LS estimates, and further to derive a bias compensation based RLS algorithm. The performance of the developed method is both analyzed theoretically and shown by means of simulation results.


93E12 Identification in stochastic control theory
93E24 Least squares and related methods for stochastic control systems
Full Text: DOI


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