Input design and online system identification based on Poisson moment functions for system outputs with quantization noise

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

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Abstract

We study optimal input design and bias-compensating parameter estimation methods for continuous-time models applied on a mechanical laboratory experiment. Within this task we compare two online estimation methods that are based on Poisson moment functions with focus on quantized system outputs due to an angular encoder: The standard recursive least-squares (RLS) approach and a bias-compensating recursive least-squares (BCRLS) approach. The rationale is to achieve acceptable estimation results in the presence of white noise, caused by low-budget encoders with low resolution. The input design and parameter estimation approaches are assessed and compared, experimentally, resorting to measurements taken from a laboratory cart system.

OriginalspracheEnglisch
Titel2017 25th Mediterranean Conference on Control and Automation, MED 2017
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten23-29
Seitenumfang7
ISBN (elektronisch)9781509045334
DOIs
PublikationsstatusVeröffentlicht - 18 Jul 2017
Veranstaltung25th Mediterranean Conference on Control and Automation, MED 2017 - Valletta, Malta
Dauer: 3 Jul 20176 Jul 2017

Publikationsreihe

Name2017 25th Mediterranean Conference on Control and Automation, MED 2017

Konferenz

Konferenz25th Mediterranean Conference on Control and Automation, MED 2017
LandMalta
OrtValletta
Zeitraum03.07.201706.07.2017

Schlagwörter

  • Parameter estimation
  • quantization noise
  • optimization input design

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