Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

Bin Xu,Pengchao Zhang

doi:10.1155/2017/6019175

Bin Xu, Pengchao Zhang

Open Access

https://doi.org/10.1155/2017/6019175

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Journal: Complexity	Publication Date: Jan 1, 2017
Citations: 33	License type: CC BY 4.0

Affiliation: Shaanxi University of Technology

Abstract

This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learning-parameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

Highlights

MEMS gyroscopes have been drawing growing attention because they intend to employ advanced control approaches to realize trajectories tracking and to handle system parametric uncertainties and disturbances
This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique
An adaptive control strategy using radial basis function (RBF) network/Fuzzy Logic System compensator is presented for robust tracking of MEMS gyroscope in the presence of model uncertainties and external disturbances to compensate such system nonlinearities and improve the tracking performance in [17,18,19]

Summary

Introduction

MEMS gyroscopes have been drawing growing attention because they intend to employ advanced control approaches to realize trajectories tracking and to handle system parametric uncertainties and disturbances. These intelligent control methods improve the performance of gyroscopes, so that the applications of MEMS gyroscopes are expanded. An adaptive control strategy using radial basis function (RBF) network/Fuzzy Logic System compensator is presented for robust tracking of MEMS gyroscope in the presence of model uncertainties and external disturbances to compensate such system nonlinearities and improve the tracking performance in [17,18,19].

Problem Formulation

Brief Description of RBF Neural Network

Adaptive Sliding Mode Control with Minimal-Learning-Parameter Technique

Numerical Simulation

Conclusions