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.
Highlights
Mathematical ModelWhen the moving point moves relative to a moving reference frame and the moving reference frame rotates at the same time, the point will have Coriolis acceleration. e working principle of the microgyroscope is to measure the rotation angular velocity of the motion coordinate system relative to the inertial coordinate system indirectly by detecting the vibration caused by the Coriolis acceleration
Introduction e MicroElectro mechanical system (MEMS) gyroscope is an excellent measuring element for angular velocity sensing in the inertial navigation system due to its outstandingly simple and cheap system integration [1]
In order to clearly show the advantages of the double-loop recurrent fuzzy neural network (DRFNN) of nonsingular terminal sliding mode control (NTSMC) proposed in this paper, we investigated the control performance of the FNN of SMC in the simulation for comparison
Summary
When the moving point moves relative to a moving reference frame and the moving reference frame rotates at the same time, the point will have Coriolis acceleration. e working principle of the microgyroscope is to measure the rotation angular velocity of the motion coordinate system relative to the inertial coordinate system indirectly by detecting the vibration caused by the Coriolis acceleration. E working principle of the microgyroscope is to measure the rotation angular velocity of the motion coordinate system relative to the inertial coordinate system indirectly by detecting the vibration caused by the Coriolis acceleration. Assuming the x-axis direction is the driving mode, y-axis direction is the sensing mode, simplifying the microgyroscope to motion model, as shown, according to the effect of Coriolis acceleration, when the mass m moves harmonically driven by periodic electrostatic force, if the angular velocity input Ω is detected on the z-axis, the mass m will vibrate on the y-axis. Where kxy and dxy are the coupling spring coefficient and coupling damping coefficient caused by the asymmetries of suspension structure These two parameters are unknown, they do not affect the accuracy of the model because they are too small compared with the proof mass. Where D, K, and Ω are unknown parameters that changes over time, d(t) is the external disturbance, and it is bounded by a positive constant L as |d(t)| ≤ L
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