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.
Highlights
MEMS gyroscopes have become the most growing microsensors for measuring angular velocity in recent years due to their compact size, low cost, and high sensitivity
Fabrication imperfections always result in some cross stiffness and damping effects and the performance of the MEMS gyroscope is deteriorated by the effects of time varying parameters, quadrature errors, and external disturbances
In [17], robust adaptive sliding mode control is utilized to estimate the angular velocity of MEMS triaxial gyroscope, and neural network is adopted to estimate the upper bound of system nonlinearities
Summary
MEMS gyroscopes have become the most growing microsensors for measuring angular velocity in recent years due to their compact size, low cost, and high sensitivity. In [17], robust adaptive sliding mode control is utilized to estimate the angular velocity of MEMS triaxial gyroscope, and neural network is adopted to estimate the upper bound of system nonlinearities. Different from [16,17,18], neural controller is proposed to approximate the unknown system model and sliding mode controller is employed to eliminate the approximation error and attenuate the model uncertainties and external disturbances. (1) An adaptive neural sliding mode control is adopted to on-line mimic the unknown model and compensate the approximation error It incorporates RBF neural network into the adaptive sliding control system of MEMS gyroscope to learn the approximate model. The proposed adaptive neural network sliding mode controller can guarantee the stability of the closed-loop system and improve the robustness for external disturbances and model uncertainties.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
