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Adaptive control of MEMS gyroscope using global fast terminal sliding mode control and fuzzy-neural-network

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Abstract

An adaptive control of MEMS gyroscope using global fast terminal sliding mode control (GTSMC) and fuzzy-neural-network (FNN) is presented for micro-electro-mechanical systems (MEMS) vibratory gyroscopes in this paper. This approach gives a new global fast terminal sliding surface, which will guarantee that the designed control system can reach the sliding surface and converge to equilibrium point in a shorter finite time from any initial state. In addition, the proposed adaptive global fast terminal sliding mode controller can real-time estimate the angular velocity and the damping and stiffness coefficients. Moreover, the main feature of this scheme is that an adaptive fuzzy-neural-network is employed to learn the upper bound of model uncertainties and external disturbances, so the prior knowledge of the upper bound of the system uncertainties is not required. All adaptive laws in the control system are derived in the same Lyapunov framework, which can guarantee the globally asymptotical stability of the closed-loop system. Numerical simulations for a MEMS gyroscope are investigated to demonstrate the validity of the proposed control approaches.

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Acknowledgments

The authors thank the anonymous reviewers for their useful comments that improved the quality of the paper. This work is partially supported by National Science Foundation of China under Grant No. 61374100; Natural Science Foundation of Jiangsu Province under Grant No. BK20131136. The Fundamental Research Funds for the Central Universities under Grant No. 2013B19314.

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  1. Jiangsu Key Lab. of Power Transmission and Distribution Equipment Technology, College of Computer and Information, Hohai University, Changzhou, 213022, China

    Juntao Fei & Weifeng Yan

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  1. Juntao Fei
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  2. Weifeng Yan
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Correspondence to Juntao Fei.

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Fei, J., Yan, W. Adaptive control of MEMS gyroscope using global fast terminal sliding mode control and fuzzy-neural-network. Nonlinear Dyn 78, 103–116 (2014). https://doi.org/10.1007/s11071-014-1424-z

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  • DOI: https://doi.org/10.1007/s11071-014-1424-z

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