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A recurrent wavelet-based brain emotional learning network controller for nonlinear systems

  • Application of soft computing
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Abstract

Conventional control systems often suffer from the coexistence of nonlinearity and uncertainty. This paper proposes a novel brain emotional neural network to support addressing such challenges. The proposed network integrates a wavelet neural network into a conventional brain emotional learning network. This is further enhanced by the introduction of a recurrent structure to employ the two networks as the two channels of the brain emotional learning network. The proposed network therefore combines the advantages of the wavelet function, the recurrent mechanism, and the brain emotional learning system, for optimal performance on nonlinear problems under uncertain environments. The proposed network works with a bounding compensator to mimic an ideal controller, and the parameters are updated based on the laws derived from the Lyapunov stability analysis theory. The proposed system was applied to two uncertain nonlinear systems, including a Duffing-Homes chaotic system and a simulated 3-DOF spherical joint robot. The experiments demonstrated that the proposed system outperformed other popular neural-network-based control systems, indicating the superiority of the proposed system.

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Acknowledgements

The authors are very grateful to the anonymous reviewers for their constructive comments which have helped significantly in revising this work.

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Authors and Affiliations

  1. Department of Artificial Intelligence, School of Informatics, Xiamen University, Xiamen, 361005, China

    Juncheng Zhang, Fei Chao & Hualin Zeng

  2. Department of Computer Science, Institute of Mathematics,Physics and Computer Science, Aberystwyth University, SY23 3DB, Aberystwyth, UK

    Fei Chao

  3. Department of Electrical Engineering, Yuan Ze University, Taoyuan City, Taiwan

    Chih-Min Lin

  4. Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK

    Longzhi Yang

Authors
  1. Juncheng Zhang
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  2. Fei Chao
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  3. Hualin Zeng
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  4. Chih-Min Lin
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  5. Longzhi Yang
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Contributions

JZ involved in methodology, software, writing–original draft preparation; FC involved in conceptualization of this study, methodology, investigation, formal analysis, writing–original draft preparation; C-ML involved in conceptualization of this study; Longzhi Yang involved in writing–Review & Editing.

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Correspondence to Hualin Zeng.

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This work was supported by the National Natural Science Foundation of China (No. 61673322, 61673326, and 91746103), Fundamental Research Funds for the Central Universities (No. 20720190142), and the Key Project of National Key R & D Project (No. 2017YFC1703303).

Integrating a wavelet neural network to a brain emotional network to accelerate the learning rate. Sensitive sequential input detection by recurrent loop mechanisms leading to improved dynamic characteristics.

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Zhang, J., Chao, F., Zeng, H. et al. A recurrent wavelet-based brain emotional learning network controller for nonlinear systems. Soft Comput 26, 3013–3028 (2022). https://doi.org/10.1007/s00500-021-06422-9

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  • DOI: https://doi.org/10.1007/s00500-021-06422-9

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