This paper presents an offline extended Kalman filtering based parameter identification and drift compensation for a MEMS ring vibratory gyroscope. Damping and stiffness imperfections are the major error sources in MEMS vibratory gyroscopes. In the rate integrating operation mode, where angle is output instead of angular velocity as in the case of the rate gyroscope, parameter identification is an essential prerequisite for any feedback control and compensation algorithm to minimize angle drift and other errors. The proposed EKF method provides five estimates for the resonator DC loop gain, and another four parameters related to the non-proportional damping and aniso-elasticities. The method is based on the slowly varying averaged dynamic model expressed in terms of orbital elements. The averaging methodology offers important advantages over similar attempts based directly on the dynamic model expressed in terms of fast time varying displacement and velocity of vibration. Firstly, the observed measurements are subjected to significantly lower levels of noise as a consequence of the narrowband demodulation process employed in the calculation of the orbital elements. Secondly, the EKF requires much lower update rate due to the slowly varying nature of the augmented states. These advantages result in a more accurate estimation, improved stability performance and the possibility for real time implementation of the EKF. Numerical simulation and offline implementation of the EKF using experimental gyroscope operation data are provided to validate the proposed method. Moreover, the identified damping imperfections have been used in the drift compensation control in a DSP based real time rate integrating gyroscope control system. Ultimately, the maximum angular drift has been reduced to 1 degrees per second. Spectrum analysis shows the angle drift error is dominated by 4th harmonics caused by dynamics not included in the conventional gyroscope model. (C) 2016 Elsevier B.V. All rights reserved.
