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

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

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2017 25th Mediterranean Conference on Control and Automation, MED 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages23-29
Number of pages7
ISBN (Electronic)9781509045334
DOIs
Publication statusPublished - 18 Jul 2017
Event25th Mediterranean Conference on Control and Automation, MED 2017 - Valletta, Malta
Duration: 3 Jul 20176 Jul 2017

Publication series

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

Conference

Conference25th Mediterranean Conference on Control and Automation, MED 2017
CountryMalta
CityValletta
Period03.07.201706.07.2017

Keywords

  • Parameter estimation
  • quantization noise
  • optimization input design

Fingerprint Dive into the research topics of 'Input design and online system identification based on Poisson moment functions for system outputs with quantization noise'. Together they form a unique fingerprint.

Cite this