Acceleration Measurement Drift Rejection in Motion Control Systems by Augmented-State Kinematic Kalman Filter

This paper deals with the use of MEMS accelerometers to improve the performances of positioning control systems equipped with low-resolution positioning sensors. A kinematic Kalman filter (KKF) is used to combine the position and acceleration measurements and get a smooth estimate of the kinematic variables, even in the presence of a coarse position quantization. Compared to similar schemes existing in literature, the state of the proposed KKF is augmented, to include the accelerometer output bias/drift among the variables estimated by the filter. In this way, the intrinsic robustness of the KKF scheme is further improved, by making the estimation process of the kinematic variables practically insensitive to the variation of the sensor bias/drift. The proposed KKF is used to provide a smooth and robust estimate of the kinematic variables to a positioning control system consisting of a two degrees-of-freedom (DOF) proportional–derivative (PD) position control combined with an acceleration-based disturbance observer (ADOB). Compared to a solution based on a conventional KKF, not accounting for the accelerometer output disturbance, the proposed solution exhibits better positioning performances, and insensitivity to the accelerometer output bias/drift. This feature is validated through several experimental tests on a positioning system based on a linear motor.