Adaptive control of micro-electro-mechanical system gyroscope using neural network compensator

Huan Wang,Juntao Fei,Yuzheng Yang,Yunmei Fang

doi:10.1177/1687814019898325

Abstract

This article proposes an adaptive control scheme with a neural network compensator for controlling a micro-electro-mechanical system gyroscope with disturbance and model errors. The adaptive neural network compensator is used to compensate the nonlinearities in the system based on its universal approximation and improve tracking performance of the gyroscope. The neural compensator, which is trained online, is combined with adaptive control of the Lyapunov framework system to approach the unknown system disturbance and model errors. The system stability is deduced by the Lyapunov stability theory, and the simulation of the micro-electro-mechanical system gyroscope is carried out on Matlab/Simulink, verifying the superior performance of the neural control compensation method.

Highlights

Micro-electro-mechanical system (MEMS) gyroscope is one of the basic measuring elements, which is called an angular velocity meter
In Wu et al.,[10] a MEMS gyroscope design was proposed based on a model predictive control method
Motivated by the above studies and research, an adaptive control method combining with an radial basis function (RBF) neural network compensator is proposed to control the gyroscope system

Summary

Introduction

Micro-electro-mechanical system (MEMS) gyroscope is one of the basic measuring elements, which is called an angular velocity meter. In Wu et al.,[10] a MEMS gyroscope design was proposed based on a model predictive control method. Motivated by the above studies and research, an adaptive control method combining with an RBF neural network compensator is proposed to control the gyroscope system. Neural network approximator is used to approximate system disturbance and model errors, improving the robustness of MEMS gyroscope with model uncertainties and external disturbance. From the equations (8) and (11), the tracking performance of the nominal controller based on the nominal model is greatly reduced for the actual MEMS gyroscope, the system can remain stable. The target is to design a compensation controller to enhance the tracking precision and ensure the system stable under model errors and external disturbances. We use neural networks to adaptively approximate system uncertainties and external disturbances

Introduction of neural network

Design of neural network compensator

Conclusion