Adaptive neural dynamic global PID sliding mode control for MEMS gyroscope

In this paper, a dynamic global proportional integral derivative (PID) sliding mode controller based on an adaptive radial basis function (RBF) neural estimator is developed to guarantee the stability and robustness in the presence of a lumped uncertainty for a micro electromechanical systems (MEMS) gyroscope. This approach gives a new dynamic global PID sliding mode manifold, which not only enables system trajectory to run on the global sliding mode surface at the start point more quickly and eliminates the reaching phase of the conventional sliding mode control, but also restrains the steady-state error and reduces the chattering via a dynamic PID sliding surface. A RBF neural network (NN) system is employed to estimate the lumped uncertainty and eliminate the chattering phenomenon at the same time. Additionally, adaptive laws and dynamic global PID sliding control gains that ensure the asymptotic stability of the close-loop system are proposed, together with the techniques for deciding which kind of basis function should be selected. Finally, simulation results demonstrate the effectiveness of RBFNN dynamic global PID sliding mode control method, meanwhile some comparisons are made to verify the good properties of the suggested control approach.