Abstract
Abstract This paper examines the application of the frequency sampling filter (FSF) model in process identification. The study has been performed on a refinery distillation train with the objective being to obtain step response models from plant dynamic test data for model predictive controller design. The results focus on: (a) selection of the FSF model-based multivariable system structure using the sum of squared true prediction errors (PRESS): (b) development of noise models for use in prefiltering to remove the effect of any feedback on the step response estimates and to produce white residuals for estimation of the covariance matrix; and (c) presentation of statistical confidence bounds for the step response models. The FSF results are compared with those obtained using the finite impulse response (FIR) model structure and with results obtained from a commercially available identification software package.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
