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In this paper, an adaptive neural integral sliding mode controller using neural compensator is presented to deal with the strong nonlinearity and parameter uncertainty for a Micro-Electro-Mechanical Systems (MEMS) vibratory gyroscope. The proposed control scheme is designed by two kinds of RBF neural networks, where the self-adaptive RBF NN controller is implemented to online approximate and compensate...
A novel adaptive radial basis function neural network H-infinity control strategy with robust feedback compensator using linear matrix inequality (LMI) approach is proposed for micro electro mechanical systems vibratory gyroscopes involving parametric uncertainties and external disturbances. The proposed system is comprised of a neural network controller, which is designed to mimic an equivalent control...
In this paper, a novel adaptive control scheme that incorporates fully tuned radial basis function (RBF) neural network (NN) is proposed for the control of MEMS gyroscope with respect to external disturbances and model uncertainties. An adaptive fully tuned RBF neural network controller is used to compensate the external disturbances and model uncertainties, thus improving the dynamic characteristics...
A new robust neural sliding mode(RNSM) tracking control scheme using radial basis function(RBF) neural network (NN) is presented for MEMS (MicroElectroMechanical systems) Z-axis gyroscope to achieve robustness and asymptotic tracking error convergence. An adaptive RBF NN controller is developed to approximate and compensate the large uncertain system dynamics, and a robust compensator is designed...
In the paper, a robust adaptive control using robust feedback compensator is presented for a MEMS gyroscope in the presence of external disturbances and parameter uncertainties. An adaptive controller with additional robust controller is used to improve the robustness of the control system and compensate the system nonlinearities. The proposed robust adaptive control can estimate the angular velocity...
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