In this paper a comparison of three methods for online parameter estimation is presented. The analyzed algorithms are a well known recursive least squares method (RLS), an Extended Kalman Filter (EKF) in joint state form, and an adaptive Extended Kalman Filter (aEKF). The methods' performances regarding accuracy, respond time and computing time are compared using a commercial industrial testbed, consisting of a linear belt drive for positioning tasks, an industrial servo inverter and a programmable logic controller. In addition, the online state sensitivity w.r.t. the parameters provided by the aEKF is analyzed to check the parameter excitation. These signals can be used to stop the parameter estimation for insufficient excitation.
['Daniel Beckmann', 'Mauro Hernan Riva', 'Matthias Dagen', 'Tobias Ortmaier']
Comparison of online-parameter estimation methods applied to a linear belt drive system