Double Loop Neural Fractional-Order Terminal Sliding Mode Control of MEMS Gyroscope

Abstract

A fractional-order nonsingular terminal sliding mode controller is proposed for a MEMS gyroscope using a double loop recurrent neural network approximator. For higher accuracy and faster convergence, the fractional-order (FO) calculus is employed into the nonsingular terminal sliding mode controller with additional degree of freedom. For the system robustness, the neural network is designed to approximate the lumped uncertainty. The inner recurrent loop and external recurrent loop is employed to provide feedback signal to obtain satisfactory approximation accuracy. Furthermore, the Lyapunov stability theorem is employed to verify the asymptotical stability and convergence of system. Simulations for a MEMS gyroscope are studied to exhibit the superiority of the proposed control strategy.