Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation

Abstract

An adaptive sliding controller using radial basis function (RBF) network to approximate the unknown system dynamics microelectromechanical systems (MEMS) gyroscope sensor is proposed. Neural controller is proposed to approximate the unknown system model and sliding controller is employed to eliminate the approximation error and attenuate the model uncertainties and external disturbances. Online neural network (NN) weight tuning algorithms, including correction terms, are designed based on Lyapunov stability theory, which can guarantee bounded tracking errors as well as bounded NN weights. The tracking error bound can be made arbitrarily small by increasing a certain feedback gain. Numerical simulation for a MEMS angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory tracking performance and robustness.

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

“…All the dead-zone-based RASMC simulation results are presented on Figure 5(a,b,e). They persuade one to accept the new control system performance, RASMC, as a terrific outcome since the ultimate goal, which is (36) perfect tracking of the desired trajectory, is obtained accurately! It is carried out in such a way that placement of the moveable plate at the pull-in point with a non-destructive transient trajectory is achieved in spite of the existing time-variant uncertainty in the stiffness and damping, the time-invariant uncertainty in the plate mass as well as the matched disturbance.…”

“…It could be considered as a research prospective to design a better adaptation law that makes the adapted coefficients approach zero as a continuous function. It is also worthwhile elaborating and stating that total control system can be shortly described by Equations (36), (82), and (83) as the control law and the parameters update law, respectively.…”

“…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].…”

Modeling FOG Drift Using Back-Propagation Neural Network Optimized by Artificial Fish Swarm Algorithm

“…As for modeling the nonlinear question of describing the FOG output between temperature gradients, many methods have been proposed such as the time-sequence autoregressive and moving average model [5], the adaptive compensation model [6], and the neural network [7,8]. Due to the fact that the neural network has the advantage of approximating the nonlinear function in any expected precision theoretically and has got more and more attention, back-propagation (BP) neural network is an error back-propagation algorithm and can approximate not only all functions but also all step derivatives of them at any given precision.…”