Data-Driven Multiparameter Fusion for MEMS Gyroscope ZRO Self-Compensation in Full Temperature Range

The full-temperature bias stability (σs) is a common indicator used to evaluate the performance of micro-electromechanical systems (MEMS) gyroscopes over the entire temperature range. This article proposes a full-temperature multiparameter zero-rate output (ZRO) thermal drift compensation method based on a feedforward neural network (FFN). Notably, the proposed method eliminates the need for an external temperature sensor and is applicable to all MEMS gyroscopes. The model integrates four temperature-related parameters: resonant frequency, drive excitation voltage, quadrature suppression voltage, and reference source voltage. After hyperparameter optimization and training, the FFN model was validated on four gyroscopes with different performance levels to assess its generalization capability. Experimental results show that for Gyroscope #1, the full-temperature bias stability improved by 93.8% (reduced from 0.671°/s to 0.042°/s), and the full-temperature ZRO (3σ) suppression rate reached 97.9%. Notably, compared with the traditional multiple linear regression model, the proposed FFN demonstrates strong generalization, reducing the full-temperature bias stability (σs) by 66%, 67%, 77%, and 44% across the four tested gyroscopes, respectively. Furthermore, implementation on a field-programmable gate array (FPGA) platform verifies the potential of the FFN model for real time, in situ temperature drift compensation.