Adaptive Backstepping Fuzzy Neural Network Fractional‐Order Control of Microgyroscope Using a Nonsingular Terminal Sliding Mode Controller

Abstract

An adaptive fractional-order nonsingular terminal sliding mode controller for a microgyroscope is presented with uncertainties and external disturbances using a fuzzy neural network compensator based on a backstepping technique. First, the dynamic of the microgyroscope is transformed into an analogical cascade system to guarantee the application of a backstepping design. Then, a fractional-order nonsingular terminal sliding mode surface is designed which provides an additional degree of freedom, higher precision, and finite convergence without a singularity problem. The proposed control scheme requires no prior knowledge of the unknown dynamics of the microgyroscope system since the fuzzy neural network is utilized to approximate the upper bound of the lumped uncertainties and adaptive algorithms are derived to allow online adjustment of the unknown system parameters. The chattering phenomenon can be reduced simultaneously by the fuzzy neural network compensator. The stability and finite time convergence of the system can be established by the Lyapunov stability theorem. Finally, simulation results verify the effectiveness of the proposed controller and the comparison of root mean square error between different fractional orders and integer order is given to signify the high precision tracking performance of the proposed control scheme.

Adaptive Backstepping Fractional Fuzzy Sliding Mode Control of Active Power Filter

“…The three-phase shunt active power filter has the structure of Figure 1, composed of a harmonic current detection module, control system, and main circuit. tools for their systematic and recursive design methodology for nonlinear feedback control [15][16][17][18]. Adaptive fractional fuzzy sliding mode controls and adaptive fuzzy-neural fractional finite-time sliding controllers are developed for active power filters [19][20][21].…”

“…In the last three decades, many engineers have applied fractional calculus and fractional differential equation theory with various applications [9][10][11][12][13][14]. Backstepping control techniques are powerful tools for their systematic and recursive design methodology for nonlinear feedback control [15][16][17][18].…”

Fuzzy adaptive control of a multimachine system with single inverter supply

“…where k is the machine number (k = 1: first machine [I.M1], k = 2: second machine [I.M2]), φ r,k is the rotor flux (FOC), i sd,k and i sq,k are the stator currents, and v sd,k and v sq,k are the stator voltages, which constitute the control inputs; the output to be controlled is the rotor speed ω k . From the expression of the speed in (20), the transfer function of the angular speed is as follows:…”

Adaptive Fuzzy Super‐Twisting Sliding Mode Control for Microgyroscope

“…In order to improve the performance of microgyroscope and its robustness, many researchers have endeavored to study advanced technologies [1][2][3][4][5][6][7][8] applied to microgyroscopes like adaptive control, backstepping control, sliding mode control, and fuzzy control. An adaptive force-balancing control for a micro-electro-mechanical-system z-axis gyroscope using a trajectory-switching algorithm was proposed in [1].…”

“…An adaptive nonsingular terminal sliding mode (NTSM) tracking control method based on backstepping design was presented for MEMS vibratory gyroscopes in [3]. In [5], an adaptive fuzzy sliding mode control problem for a microgyroscope system based on global fast terminal sliding mode approach was discussed. An adaptive sliding mode control system using a double loop recurrent neural network control method was proposed for a class of nonlinear dynamic systems in [6].…”