Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope

Abstract

This paper investigates an adaptive neural sliding mode controller for MEMS gyroscopes with minimal-learning-parameter technique. Considering the system uncertainty in dynamics, neural network is employed for approximation. Minimal-learningparameter technique is constructed to decrease the number of update parameters, and in this way the computation burden is greatly reduced. Sliding mode control is designed to cancel the effect of time-varying disturbance. The closed-loop stability analysis is established via Lyapunov approach. Simulation results are presented to demonstrate the effectiveness of the method.

Boundary Robust Adaptive Control of a Flexible Timoshenko Manipulator

“…In the end, simulations were executed to show the performance of the control schemes developed. Future interests lie in learning approaches [32][33][34][35][36][37][38] for flexible manipulator system. Moreover, the implementation of the proposed control will be researched, and how to overcome the nonlinearities of the actuators is also a meaningful topic.…”

Event‐Triggered Control for the Stabilization of Probabilistic Boolean Control Networks

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Backstepping control based onL1adaptive theory for large transport aircraft heavy load airdrop

“…With the development of the modern control theory, many methods [16][17][18][19][20][21] are useful in improving the robustness of the system. In earlier works, 17,20 the discrete adaptive backstepping is studied for a class of uncertain systems and they are also applied in helicopter control by Li et al 22 To deal with the calculation explosion, the dynamic surface control and instruction filter are introduced in the study by Xu.…”