CMOS Voltage-to-Frequency Converter With Temperature Drift Compensation

2011 ◽  
Vol 60 (9) ◽  
pp. 3232-3234 ◽  
Author(s):  
M. R. Valero ◽  
S. Celma ◽  
B. Calvo ◽  
N. Medrano
Keyword(s):  
Frequency Converter ◽  
Temperature Drift ◽  
Drift Compensation

Dual mass MEMS gyroscope temperature drift compensation based on TFPF-MEA-BP algorithm

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huiliang Cao ◽  
Rang Cui ◽  
Wei Liu ◽  
Tiancheng Ma ◽  
Zekai Zhang ◽  
...  
Keyword(s):  
Training Data ◽  
Experimental Result ◽  
Intrinsic Mode Functions ◽  
Temperature Drift ◽  
Connection Weight ◽  
Bp Network ◽  
Content Type ◽  
Mems Gyroscope ◽  
Time Frequency ◽  
Drift Compensation

Purpose To reduce the influence of temperature on MEMS gyroscope, this paper aims to propose a temperature drift compensation method based on variational modal decomposition (VMD), time-frequency peak filter (TFPF), mind evolutionary algorithm (MEA) and BP neural network. Design/methodology/approach First, VMD decomposes gyro’s temperature drift sequence to obtain multiple intrinsic mode functions (IMF) with different center frequencies and then Sample entropy calculates, according to the complexity of the signals, they are divided into three categories, namely, noise signals, mixed signals and temperature drift signals. Then, TFPF denoises the mixed-signal, the noise signal is directly removed and the denoised sub-sequence is reconstructed, which is used as training data to train the MEA optimized BP to obtain a temperature drift compensation model. Finally, the gyro’s temperature characteristic sequence is processed by the trained model. Findings The experimental result proved the superiority of this method, the bias stability value of the compensation signal is 1.279 × 10–3°/h and the angular velocity random walk value is 2.132 × 10–5°/h/vHz, which is improved compared to the 3.361°/h and 1.673 × 10–2°/h/vHz of the original output signal of the gyro. Originality/value This study proposes a multi-dimensional processing method, which treats different noises separately, effectively protects the low-frequency characteristics and provides a high-precision training set for drift modeling. TFPF can be optimized by SEVMD parallel processing in reducing noise and retaining static characteristics, MEA algorithm can search for better threshold and connection weight of BP network and improve the model’s compensation effect.

High Power Actuator Based on Magnetostrictive Composite Core with Temperature Drift Compensation

2014 ◽  
Vol 598 ◽  
pp. 69-74 ◽  
Author(s):  
Jerzy Kaleta ◽  
Krzysztof Kot ◽  
Rafał Mech ◽  
Przemyslaw Wiewiorski
Keyword(s):  
Magnetic Field ◽  
Pid Control ◽  
Feedback Loop ◽  
Initial Conditions ◽  
Signal Generation ◽  
Temperature Drift ◽  
Fiber Bragg Grating Sensors ◽  
The Core ◽  
Drift Compensation ◽  
Initial Settings

The paper presents an actuator based on a coil placed in the casing, with specially prepared connection rods. The construction allows installation of the fiber Bragg grating sensors inside the coil. It allows to measure deformation of the composite that is located in the core of the coil. Thanks to the signal generation with use of DASYLab software, it is possible to precisely control the frequency, value of amplitude excitation and to send the signal to the system with use of the measurement card. The main goal of the experiment is to keep constant value of deformation, by means of a feedback loop with use of PID control, and to change the initial conditions of the test by change of the external force. The system is designed to return to the initial settings by appropriate control of the intensity of magnetic field, and thus the deformation of the sample.

Temperature Drift Compensation Based on Artificial Fish Swarm Algorithm for Quartz Flexible Accelerometer

2014 ◽  
Vol 513-517 ◽  
pp. 4030-4034 ◽  
Author(s):  
Xu Dong Yu ◽  
Jin Long Li ◽  
Shu Kui Yan ◽  
Guo Wei ◽  
Geng Li
Keyword(s):  
Temperature Compensation ◽  
Stepwise Regression ◽  
Temperature Drift ◽  
Artificial Fish Swarm Algorithm ◽  
Sensor Performance ◽  
Artificial Fish Swarm ◽  
Drift Compensation ◽  
Drift Instability ◽  
Relationship Of ◽  
The Relationship

Temperature is an important factor for affecting the accuracy of quartz flexible accelerometer. The relationship of quartz flexible accelerometers drift to temperature should be established accurately which can improve the precision of inertial navigation system. In order to reduce the temperature sensitivity and improve the sensor performance, temperature drift compensation method based on artificial fish swarm (AFS) algorithm is established and the steps and methods are given. The traditional modeling method of stepwise regression is also investigated to provide a comparison with the AFS algorithm. The result shows that the temperature compensation model by AFS algorithm is accurate. The drift instability of accelerometer output is reduced from 160.2ug to 18.0ug over the temperature range from-20°Cto +50°C. The results of the stochastic temperature tests show that this method has reduced the influence of temperature variation effectively and improved the accelerometer accuracy.

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