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Adaptive neural network control of an uncertain 2-DOF helicopter system with input backlash and output constraints

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Abstract

This study considers an adaptive neural control for a two degrees of freedom helicopter nonlinear system preceded by system uncertainties, input backlash, and output constraints. First, a neural network is adopted to handle the hybrid effects of input backlash nonlinearities and system uncertainties. Subsequently, a barrier Lyapunov function is introduced to limit the output signals for further ensuring the safe operation of the system. The bounded stability of the closed-loop system is analyzed employing the direct Lyapunov approach. In the end, the simulation and experiment results are provided to demonstrate the validity and efficacy of the derived control.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the Scientific Research Projects of Guangzhou Education Bureau under Grant No. 202032793.

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, China

    Zhijia Zhao, Weitian He & ZhiFu Li

  2. Guangzhou Institute of Industrial Intelligence, Guangzhou, 511458, China

    Jingfeng Yang

  3. Chinese Academy of Sciences, Shenyang, 110016, China

    Jingfeng Yang

Authors
  1. Zhijia Zhao
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  2. Weitian He
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  3. Jingfeng Yang
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  4. ZhiFu Li
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Correspondence to Zhijia Zhao.

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Zhao, Z., He, W., Yang, J. et al. Adaptive neural network control of an uncertain 2-DOF helicopter system with input backlash and output constraints. Neural Comput & Applic 34, 18143–18154 (2022). https://doi.org/10.1007/s00521-022-07463-3

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  • DOI: https://doi.org/10.1007/s00521-022-07463-3

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