Adaptive Neural Backstepping PID Global Sliding Mode Fuzzy Control of MEMS Gyroscope

Abstract

An adaptive global sliding mode fuzzy control using radial basis function (RBF) neural network (NN) based on backstepping technique is presented for a micro electromechanical systems (MEMS) gyroscope. The proportion integral differential (PID) sliding surface has the capacity of restraining the steadystate error. Meanwhile, we take advantage of the global sliding mode manifold to overcome shortcomings of the conventional sliding mode controller, obtaining the fast response and overall robustness. Furthermore, faced with the unknown dynamic characteristic of the MEMS system, an RBF neural approximator is employed to estimate it. Besides, a fuzzy controller is put forward to suppress the chattering phenomenon caused by the sliding mode controller. The globally asymptotic stability of the closed loop system is guaranteed by the selected adaptive laws and Lyapunov theory. The simulation results demonstrate satisfactory effects of the proposed advanced controller. The comparison studies verify the better properties of the suggested control approach. INDEX TERMS Micro electromechanical systems gyroscope, PID global sliding mode control, RBF neural network control, backstepping control, fuzzy control.

MLP-Based Neural Guaranteed Performance Control for MEMS Gyroscope With Logarithmic Quantizer

“…The schematic diagram of a z-axis vibrational MEMS gyroscope is depicted in Fig. 1 and conventionally, the nondimension model of MEMS gyroscope, as borrowed form [4], [5], [8], [9], [28] can be formulated as:…”

“…where [4], [5], [8], [9], [28] commonly assume that the input control signal u can be performed with arbitrary accuracy. While in fact, the widespread use of digital microprocessor leads to the fact that most electrostatic actuators of MEMS gyroscope cannot meet such requirement.…”

“…Focusing on raising the measuring accuracy of MEMS gyroscope, tremendous advanced control schemes have been proposed, such as sliding-mode control [4]- [6], fuzzy logic control [5], [7], robust and adaptive control [7], [8], as well as neural networks (NNs) [6], [9]. As an example, in [7], the backstepping control, where the controller design contains several steps and, in each step, the virtual control law is recursively designed such that the ultimately uniformly bounded (UUB) properties of MEMS gyroscope can be achieved.…”

Robust Observer Based on Fixed-Time Sliding Mode Control of Position/Velocity for a T-S Fuzzy MEMS Gyroscope

“…An adaptive sliding mode control and fuzzy compensator were introduced for the MEMS gyroscope in [3]. The investigations of neural networks for a MEMS gyroscope can be found in [4][5][6][7]. Wang et al [8] proposed the control of the z-axis of a MEMS gyroscope by using adaptive fractionalorder sliding mode control.…”

Adaptive Neural Backstepping Sliding Mode Heading Control for Underactuated Ships with Drift Angle and Ship-Bank Interaction

“…Recently, backstepping control is used for uncertain nonlinear systems to improve the global ultimate asymptotic stability. Chu proposed an adaptive global sliding mode fuzzy control using a radial basis function neural network based on the backstepping technique [ 6 ]. A RBF neural approximator was employed to estimate uncertainty.…”