This paper reports an approach of in-operation temperature bias drift compensation based on phase-based calibration for a stiffness-tunable MEMS accelerometer with double-sided parallel plate(DSPP)capacitors.The tempe...This paper reports an approach of in-operation temperature bias drift compensation based on phase-based calibration for a stiffness-tunable MEMS accelerometer with double-sided parallel plate(DSPP)capacitors.The temperature drifts of the components of the accelerometer are characterized,and analytical models are built on the basis of the measured drift results.Results reveal that the temperature drift of the acceleration output bias is dominated by the sensitive mechanical stiffness.An out-of-bandwidth AC stimulus signal is introduced to excite the accelerometer,and the interference with the acceleration measurement is minimized.The demodulated phase of the excited response exhibits a monotonic relationship with the effective stiffness of the accelerometer.Through the proposed online compensation approach,the temperature drift of the effective stiffness can be detected by the demodulated phase and compensated in real time by adjusting the stiffness-tuning voltage of DSPP capacitors.The temperature drift coefficient(TDC)of the accelerometer is reduced from 0.54 to 0.29 mg/℃,and the Allan variance bias instability of about 2.8μg is not adversely affected.Meanwhile,the pull-in resulting from the temperature drift of the effective stiffness can be prevented.TDC can be further reduced to 0.04 mg/℃through an additional offline calibration based on the demodulated carrier phase representing the temperature drift of the readout circuit.展开更多
A method is proposed to compensate the output drift for cooled infrared imaging systems at various ambient temperatures. By calibrating the cryogenic infrared detector which absorbs the radiant flux of blackbody direc...A method is proposed to compensate the output drift for cooled infrared imaging systems at various ambient temperatures. By calibrating the cryogenic infrared detector which absorbs the radiant flux of blackbody directly, the internal factors can be obtained. Then, by combining the calibration result of infrared imaging system at an arbitrary ambient temperature, the output drift can be calculated and compensated at various integration time and ambient temperatures. Experimental results indicate that the proposed method can eliminate the effect of ambient temperature fluctuation on the system output efficiently.展开更多
Cable-driven soft robots exhibit complex deformations,making state estimation challenging.Hence,this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficien...Cable-driven soft robots exhibit complex deformations,making state estimation challenging.Hence,this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficients.These coefficients combine measurements from proprioceptive sensors,such as resistive flex sensors,to determine the bending angle.Additionally,the fusion strategy adopted provides robust state estimates,overcoming mismatches between the flex sensors and soft robot dimensions.Furthermore,a nonlinear differentiator is introduced to filter the differentiated sensor signals to address noise and irrational values generated by the Analog-to-Digital Converter.A rational polynomial equation is also introduced to compensate for temperature drift exhibited by the resistive flex sensors,which affect the accuracy of state estimation and control.The processed multi-sensor data is then utilized in an improved PD controller for closed-loop control of the soft robot.The controller incorporates the nonlinear differentiator and drift compensation,enhancing tracking performance.Experimental results validate the effectiveness of the integrated approach,demonstrating improved tracking accuracy and robustness compared to traditional PD controllers.展开更多
基金The work is supported by the Grant of the National Natural Science Foundation of China(Grant No.62104211).
文摘This paper reports an approach of in-operation temperature bias drift compensation based on phase-based calibration for a stiffness-tunable MEMS accelerometer with double-sided parallel plate(DSPP)capacitors.The temperature drifts of the components of the accelerometer are characterized,and analytical models are built on the basis of the measured drift results.Results reveal that the temperature drift of the acceleration output bias is dominated by the sensitive mechanical stiffness.An out-of-bandwidth AC stimulus signal is introduced to excite the accelerometer,and the interference with the acceleration measurement is minimized.The demodulated phase of the excited response exhibits a monotonic relationship with the effective stiffness of the accelerometer.Through the proposed online compensation approach,the temperature drift of the effective stiffness can be detected by the demodulated phase and compensated in real time by adjusting the stiffness-tuning voltage of DSPP capacitors.The temperature drift coefficient(TDC)of the accelerometer is reduced from 0.54 to 0.29 mg/℃,and the Allan variance bias instability of about 2.8μg is not adversely affected.Meanwhile,the pull-in resulting from the temperature drift of the effective stiffness can be prevented.TDC can be further reduced to 0.04 mg/℃through an additional offline calibration based on the demodulated carrier phase representing the temperature drift of the readout circuit.
文摘A method is proposed to compensate the output drift for cooled infrared imaging systems at various ambient temperatures. By calibrating the cryogenic infrared detector which absorbs the radiant flux of blackbody directly, the internal factors can be obtained. Then, by combining the calibration result of infrared imaging system at an arbitrary ambient temperature, the output drift can be calculated and compensated at various integration time and ambient temperatures. Experimental results indicate that the proposed method can eliminate the effect of ambient temperature fluctuation on the system output efficiently.
基金financial support from the National Natural Science Foundation of China(62103039,62073030)the Joint Fund of Ministry of Education for Equipment Pre-Research(8091B03032303).
文摘Cable-driven soft robots exhibit complex deformations,making state estimation challenging.Hence,this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficients.These coefficients combine measurements from proprioceptive sensors,such as resistive flex sensors,to determine the bending angle.Additionally,the fusion strategy adopted provides robust state estimates,overcoming mismatches between the flex sensors and soft robot dimensions.Furthermore,a nonlinear differentiator is introduced to filter the differentiated sensor signals to address noise and irrational values generated by the Analog-to-Digital Converter.A rational polynomial equation is also introduced to compensate for temperature drift exhibited by the resistive flex sensors,which affect the accuracy of state estimation and control.The processed multi-sensor data is then utilized in an improved PD controller for closed-loop control of the soft robot.The controller incorporates the nonlinear differentiator and drift compensation,enhancing tracking performance.Experimental results validate the effectiveness of the integrated approach,demonstrating improved tracking accuracy and robustness compared to traditional PD controllers.
