Batch-Least Squares System Identification Algorithm for 2D Repetitive Processes

Dustin M. Seltzer, Jeffrey L. Schiano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The batch-least squares (BLS) approach to parameter identification is a well-known technique utilized for system identification. In this paper, we adapt the BLS approach to a 2D repetitive process model. Our approach first derives a regression model for the 2D repetitive process, which is then used for implementing the BLS algorithm. Our adapted BLS algorithm then uses the repetitive nature of the system to identify both the parameters associated with the system repetition and inputs applied. We evaluated the adapted BLS algorithm on an additive manufacturing process.

Original languageEnglish (US)
Title of host publication2022 IEEE 61st Conference on Decision and Control, CDC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages862-867
Number of pages6
ISBN (Electronic)9781665467612
DOIs
StatePublished - 2022
Event61st IEEE Conference on Decision and Control, CDC 2022 - Cancun, Mexico
Duration: Dec 6 2022Dec 9 2022

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2022-December
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370

Conference

Conference61st IEEE Conference on Decision and Control, CDC 2022
Country/TerritoryMexico
CityCancun
Period12/6/2212/9/22

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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