Adaptive control of micro-electro-mechanical system gyroscope using neural network compensator

This article proposes an adaptive control scheme with a neural network compensator for controlling a micro-electro-mechanical system gyroscope with disturbance and model errors. The adaptive neural network compensator is used to compensate the nonlinearities in the system based on its universal approximation and improve tracking performance of the gyroscope. The neural compensator, which is trained online, is combined with adaptive control of the Lyapunov framework system to approach the unknown system disturbance and model errors. The system stability is deduced by the Lyapunov stability theory, and the simulation of the micro-electro-mechanical system gyroscope is carried out on Matlab/Simulink, verifying the superior performance of the neural control compensation method.