Super-Twisting Sliding Mode Control for Micro Gyroscope Based on RBF Neural Network

Abstract

This paper presents a hybrid control scheme based on a super-twisting sliding mode control and radial basis function (RBF) neural network control method for micro gyroscopes with unknown model uncertainties and external disturbances. In consideration of unknown model of micro gyroscope system, this paper utilizes the RBF neural network to realize the adaptive approximation of the unknown part of the model and ensure that the controller does not rely on the precise mathematical model of the controlled system. The neural network adaptive law is obtained by using the Lyapunov method, and the stability and convergence of the closed-loop system are guaranteed by adjusting the adaptive weight. The super-twisting sliding mode control method is adopted in order to improve the convergence speed and weaken the chattering. Finally, the simulation results verify the superiority and validity of the proposed method and compare it with the super-twisting sliding mode control without RBF neural network. Simulation results indicate that the control method proposed in this paper is very effective and feasible, achieving the required dynamic and static performance. It not only effectively weakens the chattering of the control system but also ensures the convergence of the system in limited time and improves the property of the control system.