Adaptive neural sliding mode compensator for MEMS gyroscope

In this paper, an adaptive sliding mode controller using radial basis function (RBF) network to approximate the unknown system dynamics for Micro Electro Mechanical systems (MEMS) gyroscope sensor is proposed. Neural network controller is proposed to approximate the unknown system model and sliding mode controller is employed to eliminate the approximation error and attenuate the model uncertainties and external disturbances. On-line neural network (NN) weight tuning algorithms, including correction terms, are designed based on Lyapunov stability theory, which can guarantee bounded tracking errors as well as bounded NN weights. The tracking error bound can be made arbitrarily small by increasing a certain feedback gain. Numerical simulation for a MEMS angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory tracking performance and robustness.