Estimation of Viscous Friction Parameters in Acrobot
This paper deals with estimation of coefficients of vis-
cous friction for a model of an acrobot. The acrobot
represents an underactuated nonlinear dynamic system,
where typically not all states are measurable. More-
over effect of noise corruption on remaining measured
states is often nonneglible. However, except for fric-
tion coefficient, all remaining parameters of the model
can usually be measured directly.
To overcome mentioned difficulties and to take advan-
tage 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 con-
trolled by properly designed experiment.