Adaptive control system based on radial basis function (RBF) neural network sliding mode control for micro-electromechanical system (MEMS) gyroscope

Abstract

The invention discloses an adaptive control system based on a radial basis function (RBF) neural network sliding mode control for a micro-electromechanical system (MEMS) gyroscope, and the system comprises a gyroscope and a control circuit, wherein the control circuit comprises a sliding mode controller and an RBF neural network; the difference of displacement of the three-axis gyroscope in the directions of three coordinate axes x, y and z and displacement of a reference model is taken as the input of the sliding mode controller. In the adaptive control system, an adaptive sliding mode control method is applied in controlling the gyroscope, so as to improve the stability and reliability of the system; and the RBF neural network is adopted to carry out adaptive learning on upper boundary of uncertain interference, thus reducing the influence of measurement error and external interference, effectively lowering the occurrence of buffeting, and achieving a better control effect.

Description

technical field [0001] The invention relates to a control system of a gyroscope and belongs to the field of automatic control systems. Background technique [0002] Gyroscopes are the most commonly used sensors for measuring angular velocity in many applications, such as navigation, guidance, and control stability. A gyroscope is a device that uses the Coriolis force (i.e.: the deflection force of the Earth's rotation) to transfer energy from one axis to another. The traditional mode of operation lacks the drive to drive the gyroscope from one mode to a known oscillating motion. , while the detected Coriolis acceleration is coupled to the sensing mode of vibration. The vibration is perpendicular to the driving mode. The response of the vibration sensing mode provides information about the practical angular velocity. The performance of the gyroscope is also affected by time-varying parameters and parameters such as thermal Constraints of noise sources such as noise, mechanic...

AI Technical Summary

Problems solved by technology

A gyroscope is a device that uses the Coriolis force (i.e.: the deflection force of the Earth's rotation) to transfer energy from one axis to another. The traditional mode of operation lacks the drive to drive the gyroscope from one mode to a known oscillating motion. , while the detected Coriolis acceleration is coupled to the sensing mode of vibration. The vibration is perpendicular to the driving mode. The response of the vibration sensing mode provides information about the practical angular velocity. The performance of the gyroscope is also affected by time-varying parameters and parameters such as thermal Constraints of noise sources such as noise, mechanical noise, sensory circuit noise, environmental variables, integration errors, parameter variables, and external disturbances that generate a vibration detuning frequency between two vibration axes, therefore, it is necessary to use advanced control System Control Gyroscope

[0003] This shows that the above-mentioned existing gyroscope control system obviously still has inconvenience and defects in use, and needs to be further improved urgently.

In order to solve the problems existing in the control system of the gyroscope, relevant manufacturers have tried their best to find a solution, but no suitable design has been developed for a long time.

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