TY - JOUR
T1 - Serial-Parallel Estimation Model-Based Sliding Mode Control of MEMS Gyroscopes
AU - Zhang, Rui
AU - Xu, Bin
AU - Wei, Qi
AU - Yang, Ting
AU - Zhao, Wanliang
AU - Zhang, Pengchao
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021/12/1
Y1 - 2021/12/1
N2 - This article proposes a serial-parallel estimation model (SPEM)-based sliding mode control (SMC) of MEMS gyroscope. For the system nonlinearity, the linear-in-parameterized dynamics are formulated and the updating law of the parameter vector is given. For the system uncertainty, the radial basis function (RBF) neural network (NN) is utilized. To improve the approximation accuracy of the compound nonlinearity, the updating laws of the parameter vector and RBF NN weight are constructed by the tracking error and the filtered modeling error derived from SPEM. Furthermore, the fast terminal (FT) SMC is employed to achieve finite-time convergence. The simulation results show that the proposed controller obtains higher tracking accuracy and faster convergence, while the compound nonlinearity approximation is with higher precision.
AB - This article proposes a serial-parallel estimation model (SPEM)-based sliding mode control (SMC) of MEMS gyroscope. For the system nonlinearity, the linear-in-parameterized dynamics are formulated and the updating law of the parameter vector is given. For the system uncertainty, the radial basis function (RBF) neural network (NN) is utilized. To improve the approximation accuracy of the compound nonlinearity, the updating laws of the parameter vector and RBF NN weight are constructed by the tracking error and the filtered modeling error derived from SPEM. Furthermore, the fast terminal (FT) SMC is employed to achieve finite-time convergence. The simulation results show that the proposed controller obtains higher tracking accuracy and faster convergence, while the compound nonlinearity approximation is with higher precision.
KW - Compound nonlinearity approximation
KW - MEMS gyroscope
KW - fast terminal (FT) sliding mode control (SMC)
KW - radial basis function (RBF) neural network (NN)
KW - serial-parallel estimation model (SPEM)
UR - http://www.scopus.com/inward/record.url?scp=85120379048&partnerID=8YFLogxK
U2 - 10.1109/TSMC.2020.2981807
DO - 10.1109/TSMC.2020.2981807
M3 - 文章
AN - SCOPUS:85120379048
SN - 2168-2216
VL - 51
SP - 7764
EP - 7775
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 12
ER -