Adaptive neural Sliding Mode Control for MEMS gyroscope using fractional calculus

Abstract

An adaptive neural sliding mode control method is proposed for micro-electric-mechanical-system (MEMS) gyroscopes in this paper using fractional calculus. A new sliding surface containing a fractional order term is designed in the paper. And the fractional order term can improve control system dynamics and the fractional calculus is also used in adaptive laws to improve parameter identification performance. Besides, a radial basis function neural network is adopted to online estimate the upper bound of the lumped disturbance to reduce chattering phenomenon. The stability of the control system is proved using the Lyapunov stability theorem where adaptive laws for parameters and neural network weights are online tuned. Simulation results on a Z axis gyroscope is conducted to validate the effectiveness of the control method. performance.