Bias compensation based recursive least-squares identification algorithm for MISO system with input and output noises

Wu, Ai‐Guo; Qi, Wen‐Nian; Dong, Rui‐Qi

doi:10.1109/ascc.2017.8287275

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…15 Wu et al combined bias compensation technology with LS estimation algorithm with forgetting factor to estimate parameters of output error model with moving average noise, 16 then, proposed an LS algorithm based on BCP for parameter estimation of MISO system under white noise. 17 Mu et al proposed a globally convergent identification algorithm, modified BCP, and gave a consistent estimation of noise variance rather than introducing auxiliary vectors. 18 Jia et al presented a unified framework based on the BCP, which introduces a nonsingular matrix and a noise-independent auxiliary vector to identify the system affected by correlated noise.…”

Section: Introductionmentioning

confidence: 99%

Least square algorithm based on bias compensated principle for parameter estimation of canonical state space model

Liu

Zhang

Azar

et al. 2022

Measurement and Control

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Due to the existence of system noise and unknown state variables, it is difficult to realize unbiased estimation with minimum variance for the parameter estimation of canonical state space model. This paper presents a new least squares estimator based on bias compensation principle to solve this problem, transforms canonical state space into the form suitable for the least square algorithm, introduces an augmented parameter vector and an auxiliary variable, derives parameter estimation formula based on noise compensation, realizes the unbiased estimation, and gives the specific algorithm. A simulation example is provided to verify the effectiveness of the estimator.

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