Speed measurement error reduction via adaptive strong tracking Kalman filters

This paper proposes an adaptive strong tracking Kalman filters (ASTKF) for reducing the measurement deviation of speed and improving the tracking ability of the observer. The proposed ASTKF can limit the tracking mismatch caused by low- resolution encoders and track the load torque better in real time. The proposed ASTKF introduces a suboptimal scaling factor to the gain matrix and calculating the system noise matrix at the current time. The simulations illustrate the ASTKF can precisely obtain the information of both speed and load torque. And it can achieve smaller measurement deviation and faster response.