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  • This paper deals with estimation of coefficients of viscous friction for a model of an acrobot. The acrobot represents an underactuated nonlinear dynamic system, where typically not all states are measurable. Moreover effect of noise corruption on remaining measured states is often non negligible. However, except for friction coefficient, all remaining parameters of the model can usually be measured directly. To overcome mentioned difficulties and to take advantage of abundant prior knowledge, we applied hybrid extended Kalman filter to this task. Using Monte Carlo (MC) simulations we approximated probability density functions of friction coefficients estimate and showed that the bias and variance of the estimate can be controlled by properly designed experiment.
  • This paper deals with estimation of coefficients of viscous friction for a model of an acrobot. The acrobot represents an underactuated nonlinear dynamic system, where typically not all states are measurable. Moreover effect of noise corruption on remaining measured states is often non negligible. However, except for friction coefficient, all remaining parameters of the model can usually be measured directly. To overcome mentioned difficulties and to take advantage of abundant prior knowledge, we applied hybrid extended Kalman filter to this task. Using Monte Carlo (MC) simulations we approximated probability density functions of friction coefficients estimate and showed that the bias and variance of the estimate can be controlled by properly designed experiment. (en)
Title
  • Estimation of Viscous Friction Parameters in Acrobot
  • Estimation of Viscous Friction Parameters in Acrobot (en)
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  • Estimation of Viscous Friction Parameters in Acrobot
  • Estimation of Viscous Friction Parameters in Acrobot (en)
skos:notation
  • RIV/68407700:21230/11:00184561!RIV12-GA0-21230___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
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  • P(GAP103/10/0628)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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  • 198011
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  • RIV/68407700:21230/11:00184561
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  • Filtering and Identification; Nonlinear dynamics; Stochastic systems (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [661200635EAE]
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
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http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Dolinský, Kamil
  • Čelikovský, Sergej
http://localhost/t...ganizacniJednotka
  • 21230
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