Multivariate temperature compensation system and method for hemispherical resonator gyroscope based on BP neural network

Abstract

The invention relates to a multivariate temperature compensation system and method for a hemispherical resonator gyroscope based on a BP neural network, and the method comprises the steps: extracting multiple groups of temperatures, temperature gradients and temperature change rates through a temperature measurement element and a temperature measurement circuit, extracting data information of a harmonic oscillator through an electrode by an analog-to-digital converter, inputting the data information into a normalization module, and carrying out the calculation, acquiring information such as multiple groups of gyroscope frequencies, standing wave azimuth angles and gyroscope output, extracting sample characteristics by adopting a radial basis kernel function, training a BP neural network, and establishing a compensation model weight relation; and embedding the compensation model into a gyroscope control program, so that online compensation of gyroscope output in a temperature change environment is realized. The temperature characteristic of the gyroscope can be effectively improved, and the zero-bias stability of the gyroscope during long-time working is improved.

Description

technical field [0001] The invention belongs to the technical field of inertial instrument control, in particular to a multi-element temperature compensation system and method for hemispherical resonant gyroscopes based on BP neural network. Background technique [0002] As a solid wave gyroscope, the resonant gyroscope includes quartz hemispherical resonant gyroscope, metal cylindrical resonant gyroscope, nested ring gyroscope and micro hemispherical gyroscope, etc. It is a solid wave principle with long life, high reliability and high precision. Gyroscope has the tendency to replace all kinds of optical gyroscopes. The core sensitive element, the resonator, has non-ideal characteristics such as uneven distribution of mass and stiffness and defects due to factors such as material, processing, and process imperfections; at the same time, the geometry and physical properties of the resonator are affected by the external environment, resulting in gain errors , cross damping e...

AI Technical Summary

Problems solved by technology

However, since the two-mode coupling in-phase error component caused by cross damping in the loop response signal is in the same phase as the signal generated by the precession effect of external angular motion, it is difficult to remove this error from the gyroscope output signal

When the gyro is in a vibration environment or a temperature-changing environment, the in-phase error component will drift, resulting in fluctuations in the gyro zero position and reducing the stability of the gyro zero bias

The error is usually compensated by modeling and calibration. However, there is a correlation between the gyro zero bias and parameters such as temperature and temperature gradient, and the coupling is usually nonlinear. It is difficult to achieve the ideal compensation effect by relying on the traditional polynomial modeling method.

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