Fixed-time adaptive fuzzy control for stochastic MEME gyroscopes with optimized transient behaviors and limited communication resources

Abstract

This study expands the existing model for microelectromechanical system (MEMS) gyroscopes by transitioning from a deterministic model to a stochastic nonlinear model and proposes an adaptive fuzzy control scheme that is triggered by events and ensures prescribed performance for stochastic MEMS gyroscopes. Unlike current control schemes for MEMS gyroscopes, the scheme guarantees full control over output overshoot and input vibration, while eliminating the need for design parameters to meet feasibility conditions in event-triggered mechanisms. Additionally, it introduces a type-3 fuzzy system with improved modeling capabilities to approximate unknown nonlinear terms. By incorporating command filtering techniques, the scheme effectively addresses issues related to complexity explosion and filtering errors in backstepping designs. Through the application of fixed-time Lyapunov stability theory on stochastic systems, it is demonstrated that all closed-loop signals are fixed-time bounded in probability. Moreover, the tracking error consistently remains within predefined performance boundaries. Simulation experiments validate both the effectiveness and superiority of the scheme.