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RBF neural network-based super-twisting sliding mode control method for micro-gyroscope system

A micro-gyroscope, neural network technology, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve problems such as insufficient control methods

Inactive Publication Date: 2018-12-21
HOHAI UNIV CHANGZHOU
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to overcome the defects of the prior art, to provide a micro gyroscope system based on RBF neural network super-distortion sliding mode control method, to improve the performance of the micro gyroscope system, to improve its robustness, and to solve the problem of micro gyroscope Existing defects of gyroscopes and insufficient traditional control methods

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  • RBF neural network-based super-twisting sliding mode control method for micro-gyroscope system
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  • RBF neural network-based super-twisting sliding mode control method for micro-gyroscope system

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[0095] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to more clearly illustrate the technical solutions of the present invention, but cannot limit the protection scope of the present invention with this.

[0096] 1. Mathematical model of micro gyroscope:

[0097] A micro-vibration gyroscope usually consists of three parts: a mass suspended by an elastic material support, an electrostatic drive device, and a sensing device. This can be simplified as figure 1 A damped oscillatory system consisting of a mass and a spring is shown, which shows a simplified z-axis micromechanical vibratory gyroscope model in Cartesian coordinates.

[0098] According to Newton's law in the rotating system, comprehensively considering the influence of various manufacturing errors on the micro-gyroscope, and then through the dimensionless processing of the micro-gyroscope, the mathematical model of the micro-...

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Abstract

The invention discloses an RBF neural network-based super-twisting sliding mode control method for a micro-gyroscope system. The method is characterized by comprising the following steps of 1) building a dimensionless dynamics equation of the micro-gyroscope system, and converting the dimensionless dynamics equation into an equivalent model in a vector form; 2) designing an RBF neural network super-twisting sliding mode controller of the micro-gyroscope system, wherein the controller is composed of an equivalent controller and a super-twisting sliding mode controller; and 3) designing a self-adaptive algorithm of an RBF neural network weight by adopting a Lyapunov stability theory. The method has the advantages that the system can be rapidly converged within a limited time to achieve a stable state, and an RBF design network is used for approximating an unknown model of the system; the design of a control system is simplified, and the requirements of controller design on an accurate model of the system are lowered.

Description

technical field [0001] The invention relates to an RBF neural network-based ultra-twist sliding mode control method for a micro-gyroscope system, belonging to the technical field of micro-gyroscope control. Background technique [0002] Gyroscopes are the basic measurement elements of inertial navigation and inertial guidance systems. Because of its huge advantages in cost, volume, and structure, micro-gyroscopes are widely used in civil and military fields such as navigation, aerospace, aviation, and oil field survey and development, and land vehicle navigation and positioning. Because of the influence of errors and temperature in design and manufacturing, it will lead to differences between the characteristics of the original and the design, resulting in a decrease in the sensitivity and accuracy of the gyroscope system. The main problem of micro gyroscope control is to compensate for manufacturing errors and measure angular velocity . After decades of research and devel...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 冯治琳费峻涛
Owner HOHAI UNIV CHANGZHOU
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