Sliding mode SISO control of model parameters for implicit dynamic feedback estimation of industrial tracking simulation systems

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

Researchers

Research units

  • VTT Technical Research Centre of Finland
  • Luleå University of Technology

Abstract

A tracking simulator is an online simulation system that achieves a permanent state synchronization with the targeted process by dynamically calibrating the model state after comparing process measurements with model results. Tracking simulators are a powerful industrial application that can be utilized as a plant-wide virtual sensor for process monitoring and diagnosis as well as a predictive tool to provide production forecasts based on the current state of the plant. In a tracking simulator, the online calibration is performed by a dynamic estimation method. One of the most adopted dynamic estimaton methods is implicit dynamic feedback, which is based on the adjustment of model parameter using feedback controllers to align simulation results and process outputs. Thus far, PI controllers have been the most popular approach for the implementation of implicit dynamic feedback estimators. Other feedback control techniques could be employed to improve the reliability of applications based on this estimation method. This paper presents an implicit dynamic feedback estimation approach based on sliding mode controllers (SMC) for industrial tracking simulation systems. In contrast to PI controllers, SMC controllers can be more easily tuned and they are robust against uncertainties related to the simulation model behavior and measurement noise. In this work, the SMC-based approach for estimation of tracking simulation systems is described, implemented and tested using a representative laboratory-scale process.

Details

Original languageEnglish
Title of host publicationProceedings of the 43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventAnnual Conference of the IEEE Industrial Electronics Society - Beijing, China
Duration: 29 Oct 20171 Nov 2017
Conference number: 43
http://iecon2017.csp.escience.cn/

Publication series

NameProceedings of the Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE
ISSN (Print)1553-572X

Conference

ConferenceAnnual Conference of the IEEE Industrial Electronics Society
Abbreviated titleIECON
CountryChina
CityBeijing
Period29/10/201701/11/2017
Internet address

    Research areas

  • dynamic estimation, dynamic process simulation, model calibration, online simulation, sliding mode control, tracking simulation, implicit dynamic feedback

ID: 17381405