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Neural adaptive robust control for MEMS gyroscope with output constraints

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Abstract

A neural adaptive robust control method is proposed for the desired tracking of micro-electro-mechanical system (MEMS) triaxial gyroscope. In this work, input constraints and external disturbance are taken into account, and a barrier Lyapunov function (BLF) is used to ensure that the constraints are not violated and that tracking performance is achieved. In the presented control approach, RBFNNs with a non-zero parameter are employed to approximate the lumped uncertainties of the system, where the approximation precision can be modified online by the provided adaptive laws in the control strategy. All of the signals in the closed-loop system are uniformly finally bounded (UUB) thanks to the designed control mechanism, which may overcome the limitation of the finite universal approximation domain. The effectiveness of the suggested control is demonstrated by the comparison of simulation results.

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Acknowledgements

This research was funded by Natural Science Basic Research Program of Shaanxi, Grant No. 2023-JC-QN-0760.

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The authors have not disclosed any funding.

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Author notes

  1. Shangbo Liu, Baowang Lian and Zesheng Dan are contributed equally to this work.

Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi’an, 710072, Shaanxi, China

    Shangbo Liu, Baowang Lian & Zesheng Dan

Authors
  1. Shangbo Liu
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  2. Baowang Lian
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  3. Zesheng Dan
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Contributions

SL wrote the main manuscript text. BL and ZD checked the manuscript text.

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Correspondence to Zesheng Dan.

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Liu, S., Lian, B. & Dan, Z. Neural adaptive robust control for MEMS gyroscope with output constraints. Telecommun Syst 84, 203–213 (2023). https://doi.org/10.1007/s11235-023-01047-9

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  • DOI: https://doi.org/10.1007/s11235-023-01047-9

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