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Observer-based finite-time adaptive neural network control for PMSM with state constraints

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Abstract

This article investigates the observer-based finite-time adaptive neural network control for the permanent magnet synchronous motor (PMSM) system. The addressed PMSM system includes unknown nonlinear dynamics and constraint immeasurable states. The neural networks are utilized to approximate the unknown nonlinear dynamics and an equivalent control design model is established, by which a neural network state observer is given to estimate the immeasurable states. By constructing barrier Lyapunov functions and under the framework of adaptive backstepping control design technique and finite-time stability theory, a finite-time adaptive neural network control scheme is developed. It is proved that the proposed control scheme ensures the closed-loop system stable and the angular velocity, stator current and other state variables not to exceed their predefined bounds in a finite time. Finally, the computer simulation and a comparison with the existing controller are provided to confirm the effectiveness of the presented controller.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (under Grant Nos. 62173172 and U22A2043) and in part by the Key Laboratory of Intelligent Manufacturing Technology (Shantou University), Ministry of Education (under grant No. 202109244).

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Authors and Affiliations

  1. College of Science, Liaoning University of Technology, Shiying, Jinzhou, 121001, Liaoning, China

    Sihui Zhou, Shuai Sui, Yongming Li & Shaocheng Tong

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  1. Sihui Zhou
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  2. Shuai Sui
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Correspondence to Shaocheng Tong.

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Zhou, S., Sui, S., Li, Y. et al. Observer-based finite-time adaptive neural network control for PMSM with state constraints. Neural Comput & Applic 35, 6635–6645 (2023). https://doi.org/10.1007/s00521-022-08050-2

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  • DOI: https://doi.org/10.1007/s00521-022-08050-2

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