Integrating Multiple Hierarchical Parameters to Achieve the Self-Compensation of Scale Factor in a Micro-Electromechanical System Gyroscope.

Abstract

The scale factor of thermal sensitivity serves as a crucial performance metric for micro-electromechanical system (MEMS) gyroscopes, and is commonly employed to assess the temperature stability of inertial sensors. To improve the temperature stability of the scale factor of MEMS gyroscopes, a self-compensation method is proposed. This is achieved by integrating the primary and secondary relevant parameters of the scale factor using the partial least squares regression (PLSR) algorithm. In this paper, a scale factor prediction model is presented. The model indicates that the resonant frequency and demodulation phase angle are the primary correlation terms of the scale factor, while the drive control voltage and quadrature feedback voltage are the secondary correlation terms of the scale factor. By employing a weighted fusion of correlated terms through PLSR, the scale factor for temperature sensitivity is markedly enhanced by leveraging the predicted results to compensate for the output. The results indicate that the maximum error of the predicted scale factor is 0.124% within the temperature range of -40 °C to 60 °C, and the temperature sensitivity of the scale factor decreases from 6180 ppm/°C to 9.39 ppm/°C.