Use of Discrete-Time Forecast Modeling to Enhance Feedback Control and Physically Unrealizable Feedforward Control with Applications

dc.contributor.author Rollins, Derrick
dc.contributor.author Rollins, Derrick K
dc.contributor.department Statistics
dc.contributor.department Chemical and Biological Engineering
dc.date 2021-08-30T19:30:17.000
dc.date.accessioned 2021-09-09T16:32:01Z
dc.date.available 2021-09-09T16:32:01Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2021
dc.date.issued 2021-08-19
dc.description.abstract <p>When the manipulated variable (MV) has significantly large time delay in changing the control variable (CV), use of the currently measured CV in the feedback error can result in very deficient feedback control (FBC). However, control strategies that use forecast modeling to estimate future CV values and use them in the feedback error have the potential to control as well as a feedback controller with no MV deadtime using the measured value of CV. This work evaluates and compares FBC algorithms using discrete-time forecast modeling when MV has a large deadtime. When a feedforward control (FFC) law results in a physically unrealizable (PU) controller, the common approach is to use approximations to obtain a physically realizable feedforward controller. Using a discrete-time forecast modeling method, this work demonstrates an effective approach for PU FFC. The Smith Predictor is a popular control strategy when CV has measurement deadtime but not MV deadtime. The work demonstrates equivalency of this discrete-time forecast modeling approach to the Smith Predictor FBC approach. Thus, this work demonstrates effectiveness of the discrete-time forecast modeling approach for FBC with MV or DV deadtime and PU FFC.</p>
dc.description.comments <p>This book chapter is published as Rollins, Derrick K. "Use of Discrete-Time Forecast Modeling to Enhance Feedback Control and Physically Unrealizable Feedforward Control with Applications." In <em>Model Predictive Control - Recent Design and Implementations for Varied Applications</em>, edited by Umar Zakir Abdul Hamid and Ahmad 'Athif Mohd Faudzi. InTech Open, 2021. DOI: <a href="http://dx.doi.org/10.5772/intechopen.99340" target="_blank">10.5772/intechopen.99340</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/cbe_pubs/485/
dc.identifier.articleid 1486
dc.identifier.contextkey 24582324
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath cbe_pubs/485
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/kv7k9byv
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/cbe_pubs/485/2021_RollinsDerrick_UseDiscrete.pdf|||Sat Jan 15 00:28:23 UTC 2022
dc.source.uri 10.5772/intechopen.99340
dc.subject.disciplines Applied Statistics
dc.subject.disciplines Statistical Models
dc.subject.keywords Model Predictive Control
dc.subject.keywords Nonlinear Dynamic Modeling
dc.subject.keywords Artificial Pancreas
dc.title Use of Discrete-Time Forecast Modeling to Enhance Feedback Control and Physically Unrealizable Feedforward Control with Applications
dc.type article
dc.type.genre book_chapter
dspace.entity.type Publication
relation.isAuthorOfPublication 95eeb5bf-f38c-45e2-9857-0f9223053e09
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
relation.isOrgUnitOfPublication 86545861-382c-4c15-8c52-eb8e9afe6b75
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