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REAL TIME TEMPERATURE INFLUENCES ESTIMATION AND COMPENSATION IN A MINIATURE THREE-AXIAL GYRO DETECTION UNIT
Abstract
A methodology to obtain an intelligent miniaturized three-axial gyro detection unit is exposed in this paper. The presented mechanism is based on the on-line estimation and compensation of the sensors errors generated by the environmental temperature variation. Because these kind of errors are in a strongly nonlinear complex dependence of the values of environmental temperature and of the angular speed exciting the sensor on each of the three sensing axis, their correction may not be done off-line and requires an additional temperature sensor to have the right temperature in the sensor near field. To identify the error model the least square method is used. It process off-line the numerical values obtained from the three-axial gyro detection unit experimental testing for different values of angular speed applied to its axes of sensitivity and for different values of operating temperature. After the testing and error model identification, a final analysis of the error level after the compensation is performed and shown in the paper. It highlights the best variant for the matrix in the error model. In the sections of the paper are shown the results of the experimental testing of the three-axial gyro detection unit, the identification of the error models on each axis by using the least square method, and the validation of the obtained models with experimental values. A reduction of over two orders of magnitude of the angular speed absolute maximum error due to environmental temperature variation was obtained for all sensor channels.
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