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

Yuzheng Yang | Zendy; Juntao Fei | Zendy

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

Author(s) -

Yuzheng Yang,

Juntao Fei

Publication year - 2014

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2014/159047

Subject(s) - control theory (sociology) , vibrating structure gyroscope , robustness (evolution) , gyroscope , tracking error , artificial neural network , lyapunov stability , bounded function , lyapunov function , computer science , approximation error , sliding mode control , radial basis function , controller (irrigation) , angular velocity , mathematics , algorithm , nonlinear system , engineering , artificial intelligence , control (management) , physics , mathematical analysis , agronomy , biochemistry , chemistry , quantum mechanics , biology , gene , aerospace engineering

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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