The Unscented Kalman Filter (UKF) was applied
to state and parameter estimation of a one degree of freedom
robot link with an elastic, cable-driven transmission. Only motor
encoder and command torque data was used as input to the ﬁlter.
The UKF was used ofﬂine for joint state and model-parameter
estimation, and online for state estimation. This paper presents
an analysis of the robustness of the UKF to unknown/unmodeled
variation in inertia, cable tension and contact forces, using
experimental data collected with the robot.
Using model parameters found ofﬂine the UKF successfully
estimated motor and link angles and velocities online. Although
the transmission was very stiff, and hence the motor and link
states almost equal, information about the individual states was
obtained. Irrespective of variation from nominal conditions the
UKF link angle estimate was better than using motor position
as an approximation (i.e. inelastic transmission assumption). The
angle estimates were particularly robust to variation in operating
conditions, velocity estimates less so. A near-linear relationship
between contact forces and estimation errors suggested that
contact forces might be estimated using this error information.
(Best Paper Award Candidate)
Updated: Mon Jun 15 12:39:14 2015