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2014 Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation
Yuzheng Yang, Juntao Fei
J. Appl. Math. 2014: 1-8 (2014). DOI: 10.1155/2014/159047


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.


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Yuzheng Yang. Juntao Fei. "Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation." J. Appl. Math. 2014 1 - 8, 2014.


Published: 2014
First available in Project Euclid: 2 March 2015

Digital Object Identifier: 10.1155/2014/159047

Rights: Copyright © 2014 Hindawi


Vol.2014 • 2014
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