Derivative based kalman filter and its implementation on tuning PI controller for the van de vusse reactor

Atanu Panda, Parijat Bhowmick, Soham Kanti Bishnu, Sanjay Bhadra, Arijit Ganguly, Malay Gangopadhyaya

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This work focusses on predictive PI (PPI) control law employing on a class of stable, nonlinear benchmark process. To facilitate controller parameter(s) updation, two different types of derivative based Kalman filter (KF) strategies like Extended Kalman filter (EKF) and Ensemble Kalman filter (EnKF) techniques were taken into consideration. The servo-regulatory performance of the PPI controller was found satisfactory even in presence of white Gaussian noise. From the extended simulation studies, it can be inferred that EnKF-PPI control logic implemented on the nonlinear dynamical systems are having slightly better performance over EKF-PPI control law. Demonstration and practical utility of the PPI control method in the presence of process uncertainty or process-model mismatch scenario have also been investigated in this work.

Original languageEnglish
Title of host publication2021 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2021 - Proceedings
EditorsSatyajit Chakrabarti, Rajashree Paul, Bob Gill, Malay Gangopadhyay, Sanghamitra Poddar
PublisherIEEE
ISBN (Electronic)9781665440677
ISBN (Print)9781665440677
DOIs
Publication statusPublished - 14 May 2021
Event2021 IEEE International IOT, Electronics and Mechatronics Conference - Toronto, Canada
Duration: 21 Apr 202124 Apr 2021

Publication series

Name2021 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2021 - Proceedings

Conference

Conference2021 IEEE International IOT, Electronics and Mechatronics Conference
Abbreviated titleIEMTRONICS 2021
Country/TerritoryCanada
CityToronto
Period21/04/2124/04/21

Keywords

  • EKF
  • EnKF
  • Parameter estimation
  • PPI
  • Van de Vusse Reactor

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