Fractional-Order Finite-Time Super-Twisting Sliding Mode Control of Micro Gyroscope Based on Double-Loop Fuzzy Neural Network

Abstract

This article proposes a fractional order nonsingular terminal super-twisting sliding mode control (FONT-STSMC) method for a micro gyroscope with unknown uncertainty based on the double-loop fuzzy neural network (DLFNN). First, the advantages of nonsingular terminal sliding control are adopted, a nonlinear function is used to design the sliding hyper plane, then the tracking error in the system could converge to zero in a specified finite time. Second, fractional order control can increase the order of differential and integral, which greatly improves the flexibility of control method. The fractional-order controller has some advantages that integer-order systems cannot achieve, thus obtaining better control effects than that without adding fractional order control. Furthermore, the chattering problem of control input can be effectively solved by using the super-twisting algorithm, which makes the control input smoother. Finally, the unknown model of the micro gyroscope is estimated by using the DLFNN. Because the DLFNN can adjust the base width, the center vector and the feedback gain of the inner and outer layers adaptively, the accurate approximation of the unknown model can be achieved, and the robustness and accuracy can be enhanced. The simulation results and the comparisons with conventional neural sliding mode control prove the presented scheme can realized better tracking property and estimate the unknown model more accurately.