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

Zhang, Juncheng, Chao, Fei, Zeng, Hualin, Lin, Chih-Min and Yang, Longzhi (2022) A recurrent wavelet-based brain emotional learning network controller for nonlinear systems. Soft Computing, 26 (6). pp. 3013-3028. ISSN 1432-7643

A Recurrent Wavelet based Brain Emotional Learning Network Controller for Nonlinear.pdf - Accepted Version

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Official URL: https://doi.org/10.1007/s00500-021-06422-9


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.

Item Type: Article
Uncontrolled Keywords: Brain emotional learning network, Neural network control systems, Nonlinear systems, Recurrent neural network.
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 07 Oct 2022 11:27
Last Modified: 12 Nov 2022 03:30
URI: https://nrl.northumbria.ac.uk/id/eprint/50325

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