Adaptive fuzzy neural super-twisting control of micro gyroscope sensor

Abstract

Abstract To maintain the vibrations of the gyroscope proof mass, an adaptive super-twisting sliding mode control (STSMC) of a micro gyroscope based on a two-loop recursive fuzzy neural network (TLRFNN) is designed. In order to estimate the unknown parameters of the nonlinear system online, an adaptive technology is adopted based on the parameter linearization of the nonlinear model. In response to the problem of system chattering caused by ordinary sliding mode control, the STSMC with the advantages of high order sliding mode and conventional sliding mode is introduced. Aiming at the problem of uncertainty in system parameters, a two-loop recursive fuzzy neural network approximation with the competence over the storage of a priori information is utilized. All adaptive laws are obtained under the Lyapunov stability framework which contributes to the stability of the system. The simulation research shows the system exhibits good tracking performance and maintains a small tracking error under the control of the suggested control system. The effect is measured by calculating the root mean square error (RMSE) parameter of the tracking error and the proposed control system achieves a tracking effect of $${\text{1}}{\text{0.8590}} \times {\text{1}}{{\text{0}}^{ - 4}}$$ and $${\text{2}}{\text{0.0831}} \times {\text{1}}{{\text{0}}^{ - 4}}$$ in the x and y directions, respectively.

Keywords

• Micro gyroscope
• Two-loop recursive fuzzy neural network
• Super-twisting sliding mode control
• Adaptive control