Robust Estimation of the State Variables of Two-Mass System using Multilayer Observer

Kacper Sleszycki, Karol Wrobel, Krzysztof Szabat, Seiichiro Katsura

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

Abstract

In the paper the issues related to the application of the Multi-Layer Observer (MLO) for a drive system with elastic joint in the case of parameter mismatch are presented. After an introduction where the topic of estimation in the field of drive systems with an elastic joint is briefly presented, the main point of the paper is described. Then the mathematical model of the drive with elasticity is demonstrated and control structure is shown. Then, the idea of the MLO is discussed. Simulation and experimental results showing the properties of the MLO working in closed-loop structure are included, accompanied by experimental results.

Original languageEnglish
Title of host publication2022 IEEE 20th International Power Electronics and Motion Control Conference, PEMC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages728-733
Number of pages6
ISBN (Electronic)9781665496810
DOIs
Publication statusPublished - 2022
Event20th IEEE International Power Electronics and Motion Control Conference, PEMC 2022 - Brasov, Romania
Duration: 2022 Sep 252022 Sep 28

Publication series

Name2022 IEEE 20th International Power Electronics and Motion Control Conference, PEMC 2022

Conference

Conference20th IEEE International Power Electronics and Motion Control Conference, PEMC 2022
Country/TerritoryRomania
CityBrasov
Period22/9/2522/9/28

Keywords

  • multilayer observer
  • state estimation
  • two-mass system

ASJC Scopus subject areas

  • Artificial Intelligence
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Computer Science Applications
  • Energy Engineering and Power Technology

Fingerprint

Dive into the research topics of 'Robust Estimation of the State Variables of Two-Mass System using Multilayer Observer'. Together they form a unique fingerprint.

Cite this