Adaptive neural dynamic global PID sliding mode control for MEMS gyroscope

Abstract

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.

MLP-Based Neural Guaranteed Performance Control for MEMS Gyroscope With Logarithmic Quantizer

“…T represents the input matrix, ε i is the approximation error and based on universal approximation theorem [32], [34], as long as the number of neural codes is large enough, there always exists an ideal value ω * i such that ε i can be arbitrarily small and one always has…”

“…Furthermore, aiming at testifying the superiorities of MLPNN in terms of computational burden and tracking accuracy, we conduct the comparative simulations between MLPNN and RBFNN [34], the simulation results are demonstrated in TABLE 3, where the average computation time denotes the average time of updating of each neural weight. It can be easily discovered that the heavy computation burden of RBFNN appears due to the learning process of each element of neural weight vectors.…”

Robust adaptive sliding mode control of a MEMS tunable capacitor based on dead-zone method

“…It should be brought into one's mind that our total approach is trying to makeṡ equal to zero. Consequently, we should notice that one term is neglected in Equation (34). It is n i=1 a iv (t)g i and has been crossed out of Equation (34) since a iv (t) shall not be estimated.…”

“…the time-derivative of the parameters are approximately considered zero. (3) Unlike many other works such as [32][33][34][35][36][37], which have been concerned with tracking a sinusoidal displacement trajectory for the displacement of the MEMS movable capacitive plate, this paper addresses displacement control of the movable capacitive plate, regulating its motion about a fixed position. (4) This article deems uncertainty in the movable plate mass which has not been considered in previous works such as [24,29,30,38,39].…”

Adaptive Finite-Time Stabilization of Chaotic Flow with a Single Unstable Node Using a Nonlinear Function-Based Global Sliding Mode

“…In [24], an LMI-based GSMC law is suggested to improve the stability and robustness of the underactuated systems with external disturbances. In [25], a dynamic proportional-integral-derivative (PID) GSMC based on an adaptive RBF neural estimator is developed to satisfy the stability and robustness of MEMS gyroscope. An adaptive super-twisting GSMC is proposed in [26] for the tracking control of n-link rigid robotic manipulators.…”