Robust neural network control of MEMS gyroscope using adaptive sliding mode compensator

A new robust neural sliding mode (RNSM) tracking control scheme using radial basis function (RBF) neural network (NN) is presented for MEMS z-axis gyroscope to achieve robustness and asymptotic tracking error convergence. An adaptive RBF NN controller is developed to approximate and compensate the large uncertain system dynamics, and a robust compensator is designed to eliminate the impact of NN modeling error and external disturbances for guaranteeing the asymptotic stability property. Moreover, another RBF NN is employed to learn the upper bound of NN modeling error and external disturbances, so the prior knowledge of the upper bound of system uncertainties is not required. All the adaptive laws in the RNSM control system are derived in the same Lyapunov framework, which can guarantee the stability of the closed loop system. Comparative numerical simulations for an MEMS gyroscope are investigated to verify the effectiveness of the proposed RNSM tracking control scheme.