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• W2018017935 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." @default.
• W2018017935 created "2016-06-24" @default.
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• W2018017935 date "2014-01-01" @default.
• W2018017935 modified "2023-10-18" @default.
• W2018017935 title "Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation" @default.
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• W2018017935 doi "https://doi.org/10.1155/2014/159047" @default.
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