Publications

@article{Yakoub201525,

title = {A bias correction method for fractional closed-loop system identification},

author = {Z. Yakoub and M. Chetoui and M. Amairi and M. Aoun},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84935026123\&doi=10.1016%2fj.jprocont.2015.05.005\&partnerID=40\&md5=5611b28f7cb96778cb11b1057ae37bc4},

doi = {10.1016/j.jprocont.2015.05.005},

issn = {09591524},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Process Control},

volume = {33},

pages = {25-36},

publisher = {Elsevier Ltd},

abstract = {Abstract In this paper, the fractional closed-loop system identification using the indirect approach is presented. A bias correction method is developed to deal with the bias problem in the continuous-time fractional closed-loop system identification. This method is based on the least squares estimator combined with the state variable filter approach. The basic idea is to eliminate the estimation bias by adding a correction term in the least squares estimates. The proposed algorithm is extended, using a nonlinear optimization algorithm, to estimate both coefficients and commensurate-order of the process. Numerical example shows the performances of the fractional order bias eliminated least squares method via Monte Carlo simulations. © 2015 Elsevier Ltd.},

note = {cited By 21},

keywords = {Active filters; Algorithms; Continuous time systems; Electromagnetic wave attenuation; Identification (control systems); Intelligent systems; Least squares approximations; Monte Carlo methods; Nonlinear programming; Numerical methods; Optimization; Religious buildings, Bias-correction methods; Bias-eliminated least squares methods; Commensurate-order; Fractional differentiation; Least Square; Least-squares estimator; Non-linear optimization algorithms; State-variable filters, Closed loop systems},

pubstate = {published},

tppubtype = {article}

}