Neural Network Sliding Mode Control of MEMS Triaxial Gyroscope Based on RBF Sliding Gain Adjustment
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.
Neural network sliding mode control RBF MEMS gyroscope
FEI Juntao DING Hongfei YANG Yuzheng HUA Mingang
College of Computer and Information, Hohai University, Changzhou, 213022, China
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
3279-3284
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）