With the introduction of technologies such as structural optimization and error correction,the performance of the MEMS quad-mass gyroscope(QMG)has significantly improved,while noise has gradually become a critical fac...With the introduction of technologies such as structural optimization and error correction,the performance of the MEMS quad-mass gyroscope(QMG)has significantly improved,while noise has gradually become a critical factor limiting its performance.For ease of analysis,this paper categorizes noise into two types:noise at the signal detection end and noise at the excitation end.Firstly,a closed-loop noise model for QMG is established,and the effects of these two types of noise on the dynamic and static performance of QMG are investigated.Additionally,the correlation between structural parameters and noise transmission is analyzed,and the dual impact of DC Bias Voltage optimization on improving QMG performance is explored.Based on the above analysis,a force-to-rebalance(FTR)dual-loop control method incorporating SID and normalized least mean squares(NLMS)is proposed and applied to the MEMS QMG,where SID and NLMS are respectively employed to mitigate the influence of detection-end and driveend noise on the bias performance.Compared to the traditional method,the proposed approach reduces the bias instability(BI)of the MEMS QMG from 0.407°/h to 0.024°/h and the angular random walk(ARW)from 0.137°/√h to 0.006°/√h,achieving improvements of 16.96 times and 22.83 times,respectively.Furthermore,the system achieves a threshold of 0.0001°/s.展开更多
基金supported in part by the National Natural Science Foundation of China under Grants 61971466in part by the Equipment Pre-Research Foundation of China under Grant 80917020506.
文摘With the introduction of technologies such as structural optimization and error correction,the performance of the MEMS quad-mass gyroscope(QMG)has significantly improved,while noise has gradually become a critical factor limiting its performance.For ease of analysis,this paper categorizes noise into two types:noise at the signal detection end and noise at the excitation end.Firstly,a closed-loop noise model for QMG is established,and the effects of these two types of noise on the dynamic and static performance of QMG are investigated.Additionally,the correlation between structural parameters and noise transmission is analyzed,and the dual impact of DC Bias Voltage optimization on improving QMG performance is explored.Based on the above analysis,a force-to-rebalance(FTR)dual-loop control method incorporating SID and normalized least mean squares(NLMS)is proposed and applied to the MEMS QMG,where SID and NLMS are respectively employed to mitigate the influence of detection-end and driveend noise on the bias performance.Compared to the traditional method,the proposed approach reduces the bias instability(BI)of the MEMS QMG from 0.407°/h to 0.024°/h and the angular random walk(ARW)from 0.137°/√h to 0.006°/√h,achieving improvements of 16.96 times and 22.83 times,respectively.Furthermore,the system achieves a threshold of 0.0001°/s.
