Neural network sliding mode control method based on error conversion function

A neural network and transfer function technology, which is applied in the field of neural network sliding mode control based on error transfer function, can solve the problems of uncertain error signal range, influence of approximation performance, inappropriate base width and center value, etc. Accuracy, improved control effect, parameter estimation effect, and stability assurance effect

Active Publication Date: 2020-06-16
NANTONG UNIVERSITY
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the approximation performance of the neural network is affected by the center and base width, and the values ​​of the center and base width need to be within the effective input mapping range. Usually, the RBF neural net

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
  • Neural network sliding mode control method based on error conversion function
  • Neural network sliding mode control method based on error conversion function
  • Neural network sliding mode control method based on error conversion function

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0051] In order to further understand the present invention, the preferred embodiments of the present invention are described below in conjunction with the examples, but it should be understood that these descriptions are only to further illustrate the features and advantages of the present invention, rather than limiting the claims of the present invention.

[0052] Please refer to figure 1 , the invention provides a kind of neural network sliding mode control method based on error transfer function, comprising the following steps:

[0053] 1) Establish a micro-gyroscope dynamic model, and output the micro-gyroscope motion trajectory according to the micro-gyroscope dynamic model:

[0054] The mathematical model of the micro gyroscope is:

[0055]

[0056] Among them, x and y are the displacement of the micro gyroscope in the direction of X and Y axes, u x , u y is the control input of the micro gyroscope in the direction of X and Y axes, d xx 、d yy is the elastic coe...

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 neural network sliding mode control method based on an error conversion function. The neural network sliding mode control method comprises the following steps of: acquiring various parameter matrixes of a micro gyroscope and a designed sliding mode surface; adopting a hyperbolic tangent function as input of an RBF neural network to select a center and a base width on thebasis of a tracking error and the sliding mode surface, then estimating an interference upper bound, and designing a micro-gyroscope control law according to the sliding mode surface and an estimatedinterference upper bound parameter matrix; and finally realizing accurate estimation of spring parameters. The neural network sliding mode control method can guarantee that the input of the RBF neuralnetwork is within a determined range, then the proper center and base width of the network are selected, estimation of an interference upper bound parameter matrix is completed, self-adaptive adjustment of the weight is completed by designing a neural network weight self-adaptive rule, the stability of the system is guaranteed, and the measurement precision of the gyroscope is improved.

Description

technical field [0001] The invention relates to the field of automatic control systems, in particular to a neural network sliding mode control method based on an error conversion function. Background technique [0002] MEMS gyroscopes are commonly used sensors for measuring angular velocity. Mainly used in navigation, mobile phones, quadcopters and other occasions. The working principle of the gyroscope is based on the inertial effect of the proof mass caused by the Coriolis force. When there is an angular velocity input, a Coriolis force that is proportional to the input angular velocity and perpendicular to the angular velocity direction and the initial motion direction will be generated on the micro gyroscope. By detecting the displacement caused by the Coriolis force, and after a series of processing such as demodulation, amplification, and filtering, the required angular velocity signal can be obtained. [0003] Usually, in the gyro control system, due to the uncerta...

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
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 卢成付建源王慧敏张小虎朱宁远
Owner NANTONG UNIVERSITY
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