Neural network sliding mode control of MEMS triaxial gyroscope based on RBF sliding gain adjustment

Author

Juntao, Fei ; Hongfei, Ding ; Yuzheng, Yang ; Mingang, Hua

Author_Institution

Coll. of Comput. & Inf., Hohai Univ., Changzhou, China

fYear

2012

fDate

25-27 July 2012

Firstpage

3279

Lastpage

3284

Abstract

In this paper, a neural network sliding mode control of MEMS triaixal gyroscope to adjust the sliding gain using radial basis function (RBF) neural network is presented. First sliding mode control with fix sliding gain is proposed to assure the asymptotic stability of the closed loop system. Then a RBF neural network is adopted to on line adjust the sliding gain in a switching control law. The chattering phenomenon can be eliminated by using the learning function of neural network. Numerical simulation of a MEMS triaxial angular velocity sensor is investigated to verify the effectiveness of the proposed neural network sliding mode control scheme.

Keywords

asymptotic stability; closed loop systems; gyroscopes; learning (artificial intelligence); micromechanical devices; microsensors; neurocontrollers; radial basis function networks; variable structure systems; MEMS triaxial angular velocity sensor; MEMS triaxial gyroscope; RBF sliding gain adjustment; asymptotic stability; chattering phenomenon elimination; closed loop system; learning function; neural network sliding mode control; radial basis function neural network; switching control; Gyroscopes; Mathematical model; Micromechanical devices; Neural networks; Sliding mode control; Uncertainty; Upper bound; MEMS gyroscope; Neural network; RBF; sliding mode control;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2012 31st Chinese

Conference_Location

Hefei

ISSN

1934-1768

Print_ISBN

978-1-4673-2581-3

Type

conf

Filename

6390487