For elastic drive systems the motor shaft position does not correspond to the load position. Therefore, load side position feedback is usually used in the control to achieve correct positioning of the system. This paper proposes an alternative approach, that allows for a correct positioning using the motor position feedback for control. This is achieved through the online correction of the reference position using a robust Extended Kalman Filter. Using the load feedback it estimates an idealised, offset-free motor position. The correction factor calculated from the difference between the measured and the estimated motor position is then used for reference position correction (RPC). The proposed RPC method is implemented on a belt-driven test bed and evaluated for different movements, load oscillations and significant parameter errors to show its functionality and its suitability for industrial application.

Real-time, high-rate Extended Kalman Filter (EKF) execution must include considerations of its computational load, which can pose a challenge for the implementation depending on the specific observer rate. While methods for the reduction of the computational load exist, this paper seeks to circumvent the problem, by reducing the EKF sampling rate. This is explored for the case of multi-rate sensor fusion for drive control applications where at least one sensor sampling rate exceeds the control cycle rate. A lower rate EKF is implemented where, in contrast to a single-rate EKF approach, none of the higher rate measurements are neglected, but instead collected and sequentially processed during each EKF execution. Two formulations based on this concept are introduced. The first optimises the estimation error, accepting a significant increase in computational load, while the second seeks the best compromise between estimation error and computational load.