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Adaptive super-twisting sliding mode control for micro gyroscope based on double loop fuzzy neural network structure

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Abstract

In this paper, a new adaptive super-twisting sliding mode control (STSMC) scheme based on a double loop fuzzy neural network (DLFNN) is proposed to solve the problem of the external disturbances and approximate the unknown model for a micro gyroscopes. The STSMC algorithm can effectively suppress chattering since it can hide the high-frequency switching part in the high-order derivative of the sliding mode variable and transfer the discrete control law to the high-order sliding mode surface. Because it not only combines the advantages of fuzzy systems, but also incorporates the advantages of neural network control, the proposed double loop fuzzy neural network can better approximate the system model with excellent approximation. Moreover, it has the advantage of full adjustment, and the initial values of all parameters in the network can be arbitrarily set, then the parameters can be adjusted to the optimal stable value adaptively according to the adaptive algorithm. Finally, the superiority of the STSMC algorithm is also discussed. Simulation results verify the superiority of the STSMC algorithm, showing it can improve system performance and estimate unknown models more accurately compared with conventional neural network sliding mode control (CNNSMC).

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Acknowledgements

This work is partially supported by National Science Foundation of China under Grant No. 61873085; Natural Science Foundation of Jiangsu Province under Grant No. BK20171198.

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Authors and Affiliations

  1. College of IoT Engineering, Hohai University, Changzhou, 213022, China

    Juntao Fei

  2. Jiangsu Key Lab. of Power Transmission and Distribution Equipment Technology, Changzhou, 213022, China

    Zhilin Feng

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  1. Juntao Fei
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  2. Zhilin Feng
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Correspondence to Juntao Fei.

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Fei, J., Feng, Z. Adaptive super-twisting sliding mode control for micro gyroscope based on double loop fuzzy neural network structure. Int. J. Mach. Learn. & Cyber. 12, 611–624 (2021). https://doi.org/10.1007/s13042-020-01191-7

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  • DOI: https://doi.org/10.1007/s13042-020-01191-7

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