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A composite neural network-based adaptive sliding mode control method for reluctance actuator maglev system

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Abstract

To achieve high levitation control performances for a maglev system (MLS) with the uncertainties caused by the inherent nonlinearities and external disturbances, this paper proposes a novel composite adaptive sliding mode control (CASMC) method. The CASMC method comprises an equivalent controller, a composite neural network (NN) compensator, and a composite adaptive switching controller. Firstly, a modified prediction error that adds an adaptive switching term is designed to enlarge the effect of the compensation error. Secondly, a composite weight updating law consisting of the modified prediction error and the sliding mode surface is used for NN to accelerate its convergence speed. Thirdly, a new composite adaptive switching control law, including a prediction error-based adaptive switching gain and a prediction error-based proportion switching gain, is proposed for better dynamic response, stronger disturbance suppression capability, and lower chattering. The stability of the closed-loop control system is analyzed by the Lyapunov theorem. Comparative experiments were performed. Results show that the CASMC method can guarantee high levitation control performances with a better dynamic response, stronger robustness, no overshoot, and lower chattering simultaneously.

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Data Availability

All data generated or analyzed during this study are included in this published article.

Abbreviations

ASMC:

Adaptive sliding mode control

CASMC:

Composite adaptive sliding mode control

CSMC:

Composite sliding mode control

Fig:

Figure

GUUB:

Globally uniformly ultimately bounded

NN:

Neural network

NN-ASMC:

Neural network-based adaptive sliding mode control

MLS:

Maglev system

MAE:

Maximal error

PID:

Proportional–integral–differential

RMSE:

Root mean square error

RBF:

Radial basis function

SMC:

Sliding mode control

ST:

Settling time

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (No.52205053) and China Postdoctoral Science Foundation (No.2021TQ0070,2022M720766).

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Authors and Affiliations

  1. School of Microelectronics, Fudan University, Shanghai, 100190, China

    Xu Yunlang, Han Shuo & Yang Xiaofeng

  2. Shanghai Engineering Research Center of Ultra-Precision Motion Control and Measurement, Academy for Engineering and Technology, Fudan University, Shanghai, 100190, China

    Shu Feng, Su Xinyi, Guo Liang & Yang Xiaofeng

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  1. Xu Yunlang
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Correspondence to Yang Xiaofeng.

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Yunlang, X., Feng, S., Xinyi, S. et al. A composite neural network-based adaptive sliding mode control method for reluctance actuator maglev system. Neural Comput & Applic 35, 15877–15890 (2023). https://doi.org/10.1007/s00521-023-08551-8

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