Adaptive global fast terminal sliding mode control of MEMS gyroscope using fuzzy-neural-network

An adaptive global fast terminal sliding mode (GFTSM) tracking control scheme using fuzzy-neural-network (FNN) is presented for Micro-Electro-Mechanical Systems (MEMS) vibratory gyroscopes in this paper. This approach gives a new global fast terminal sliding surface, which will ensure the designed control system to reach the sliding surface and converge to equilibrium point in a shorter finite time from any initial state. In addition, the proposed adaptive global fast terminal sliding mode controller can real-time estimate the angular velocity and the damping and stiffness coefficients. Moreover, the main feature of this scheme is that the adaptive fuzzy-neural-network is employed to learn the upper bound of model uncertainties and external disturbances, so the prior knowledge of the upper bound of the system uncertainties is not required. All adaptive laws in the control system are derived in the same Lyapunov framework, which can guarantee the globally asymptotical stability of the closed loop system. Numerical simulations are investigated to demonstrate the validity of the proposed control approaches.