Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
