Extended and Unscented Kalman Filtering Applied to a Flexible-Joint Robot with Jerk Estimation
Robust nonlinear control of flexible-joint robots requires that the link position, velocity, acceleration, and jerk be available.In this paper, we derive the dynamic Bolero Hat model of a nonlinear flexible-joint robot based on the governing Euler-Lagrange equations and propose extended and unscented Kalman filters to estimate the link acceleration