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

A technology of regression neural network and control method, which is applied in the field of micro-gyroscope double-feedback regression neural network sliding mode control, which can solve the problems of micro-gyroscope performance degradation, driving axis and sensing axis are not completely vertical, etc.

Inactive Publication Date: 2016-02-17
HOHAI UNIV CHANGZHOU
View PDF2 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Micro-gyroscope double-feedback regression neural network sliding-mode control method
  • Micro-gyroscope double-feedback regression neural network sliding-mode control method
  • Micro-gyroscope double-feedback regression neural network sliding-mode control method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0099] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0100] Such as figure 1 As shown, a micro gyroscope double feedback regression neural network sliding mode control method includes the following steps:

[0101] 1. The mathematical model of the micro gyroscope is:

[0102]

[0103] in

[0104] Considering system uncertainty and external interference, the system model can be written as

[0105]

[0106] Among them, ΔA is the uncertain part of the system parameters, and d is the external disturbance.

[0107] The system model can be written as

[0108]

[0109] Among them, F=ΔAX+d, which represents the aggregate disturbance including system parameter uncertainty and external disturbance. Let the upper bound of the existence of agg...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
Inventor 卢成方韵梅费峻涛
Owner HOHAI UNIV CHANGZHOU
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap