MEMS gyroscopes error compensation method for micro satellite based on integration nerval net

Abstract

A micro satellite MEMS gyroscope error compensation method based on an integrated nerve network comprises: (1) founding an integrated nerve network model, (2) acquiring a studying sample, (3) training the integrated nerve network, (4) compensating the error of the micro satellite MEMS gyroscope. The integrated nerve network fits a trained sample excessively through integrating a plurality of nerve network weak learning devices and overcomes the defects of low precision of predication manifested by a single nerve network and low stability, effectively improves the stability of the system and the precision of the predication, and enforces the generalization ability of the system. The nerve network after being trained can get infinitely close to the nonlinear functions of input or output signals of the gyroscope with high precision and can realize the compensation of high precision of the micro satellite MEMS gyroscope with low calculation cost.

Description

technical field [0001] The invention belongs to the technical field of inertia, and relates to a calibration method for a gyroscope, in particular to a calibration compensation method for a MEMS gyroscope used in micro-satellites, which is applicable to various MEMS gyroscopes. Background technique [0002] With the development of the national economy, there is an urgent need to use wide-range and large-range satellites to conduct high-resolution earth observation and monitoring of ocean, environment, agriculture, forestry and other resources. accomplish. With the rapid development of satellite technology, micro-satellites have become the main direction of satellite development due to their low cost, light weight, short development cycle, flexible launch, and easy formation and networking; Low-cost, high-efficiency distributed high-resolution Earth observation missions. Micro-satellites have been flying in space for a long time, and precise autonomous positioning and attit...

AI Technical Summary

Problems solved by technology

However, MEMS gyroscopes have low precision, and errors such as gyro drift, scale factor errors, and non-orthogonal errors cause the original output data to be nonlinear. The rate calibration method has high accuracy, but only part of the error coefficient can be calibrated, and the rate calibration method alone cannot Complete the overall error calibration; the static multi-position calibration test can calibrate all the error coefficients in the overall error model, but since the static calibration test only uses the two weak input excitations of the earth's rotation angular velocity and the acceleration of gravity to calibrate each error coefficient, Therefore, the accuracy of the calibration results is not high, and the static multi-position calibration method usually uses the least square method to process the test data. This method is simple and easy to process the data, but there are problems that the calibration results obtained by different multi-position schemes are different. In terms of calibration, the above method is obviously not applicable

[0005] In order to solve the above problems, some scholars use the nonlinear mapping function of the neural network to realize the modeling of the input and output signals of the gyroscope and the compensation of the output signal. It also shows the disadvantages of low prediction accuracy and poor stability.

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