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 com- bine 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 pro- posed KKF is augmented, to include the accelerometer output bias/drift among the variables estimated by the fil- ter. 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 vari- ation 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 dis- turbance observer (ADOB). Compared to a solution based on a conventional KKF, not accounting for the accelerom- eter 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.