Micro-gyroscope double-feedback regression neural network sliding-mode control method

Abstract

The invention discloses a micro-gyroscope double-feedback regression neural network sliding-mode control method. On the basis of a conventional single-layer regression neural network, the method adds an output regression item, and carries out simulation on an MEMS micro-gyroscope model. A designed sliding-mode neural network controller can guarantee that a system tracking error is converged to be zero. Moreover, the method employs a double-feedback regression neural network and a common regression neural network, and the results of a neural sliding mode controller of the common regression neural network indicate that the double-feedback regression neural network is better in approaching effect and is more stable in internal state. The neural network designed in the invention can set a central vector and the initial value of a base width randomly, and the central vector and the base width can be stabilized to be optimal values along with a designed adaptive algorithm according to different inputs.

Description

technical field [0001] The invention relates to a micro-gyroscope double-feedback regression neural network sliding mode control method, which belongs to the field of automatic control systems. Background technique [0002] Micro gyroscopes are very common sensors for measuring angular velocity and are used in many fields, such as navigation, mobile phones, aircraft models, and military guidance. A micro gyroscope is a device that can transfer energy from one axis to another. The principle is to use the Coriolis force (that is, the deflection force of the earth's rotation). The process of measuring angular velocity needs to add a vibration signal with stable amplitude and frequency to the driving shaft. The sensing axis and the driving shaft are in the same plane and perpendicular to the driving shaft. When there is an angular velocity input perpendicular to both the driving shaft and the sensing axis, The Coriolis force is sensed on the sensing axis, and the magnitude of t...

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Problems solved by technology

[0003] However, due to machining errors, the drive axis and the induction axis are not completely perpendicular, resulting in additional coupling between the two axes

In addition, mechanical noise, thermal noise, noise of the sensing circuit, deviation of the parameters of the micro gyroscope itself and external interference will all cause the performance of the micro gyroscope to degrade

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