Novel Fuzzy Neural Nonsingular Terminal Sliding Mode Control of MEMS Gyroscope

Abstract

This paper attempts to improve the robustness and rapidity of a microgyroscope sensor by presenting a double-loop recurrent fuzzy neural network based on a nonsingular terminal sliding mode controller. Compared with the traditional control method, the proposed strategy can obtain faster dynamic response speed and lower steady-state error with high robustness in the presence of system uncertainties and external disturbances. A nonlinear terminal sliding mode controller is designed to guarantee finite-time high-precision convergence of the sliding surface and meanwhile to eliminate the effect of singularity. Moreover, an exponential approach law is used to accelerate the convergence rate of the system to the sliding surface. For suppressing the chattering, the symbolic function in the ideal sliding mode is replaced by the saturation function. To suppress the effect of model uncertainties and external disturbances, a double-loop recurrent fuzzy neural network is introduced to approximate and compensate system nonlinearities for the gyroscope sensor. At the same time, the double-loop recurrent fuzzy neural network can effectively accelerate the speed of parameter learning by introducing the adaptive mechanism. Simulation results indicate that the control system with the proposed controller is easily implemented, and it has higher tracking precision and considerable robustness to model uncertainties compared with the existing controllers.

Recursive Integral Terminal Sliding Mode Control of MEMS Gyroscopes via Composite Neural Learning and Disturbance Observer

“…To further improve the approximation performance of NN, finite time learning [7] and multiloop recurrent NN [8] are proposed. For the external disturbances, when the upper bounds of the disturbances are known, a robust controller is designed in [9,10] to suppress the influence of disturbances. When the upper bounds of the disturbances exist but are unknown, the disturbance observer (DOB) based on Lyapunov method is designed in [11][12][13].…”

Finite time observer‐based output feedback control of MEMS gyroscopes with input saturation

“…7,8 Then various improved NNs, such as double loop NN, 9 composite learning, 10 and finite time learning 11 are further addressed. For the external disturbances, robust control is designed, 12,13 where the upper bounds of disturbances must be known. However, the upper bounds of disturbances can not be prescient in practical applications.…”