Command Filter-Based Event-Triggered Control for Stochastic MEMS Gyroscope with Finite-Time Prescribed Performance

This paper proposes an adaptive neural control scheme for a stochastic microelectromechanical system (MEMS) gyroscope, with the aim of achieving a prescribed performance in a finite time. The radial basis function neural network is introduced to address the system’s unknown nonlinear dynamics and stochastic disturbances. Then, the technology of finite-time prescribed performance function, along with the method of command-filtered backstepping design, is utilized to ensure steady-state performance and simultaneously solve the problem of "explosion of complexity." Moreover, an event-triggered control law is developed using a novel switching threshold strategy to cut down on communication load and eliminate corresponding parametric inequality restrictions. The proposed adaptive state feedback controller is able to guarantee that the output tracking error converges to a predefined, arbitrarily small residual set in probability. Additionally, the closed-loop system’s signals can be semi-globally ultimately uniformly bounded in probability. Finally, simulation results validate the effectiveness and superiority of the proposed scheme.