4.6 Article

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

Journal

SOFT COMPUTING
Volume 26, Issue 6, Pages 3013-3028

Publisher

SPRINGER
DOI: 10.1007/s00500-021-06422-9

Keywords

Brain emotional learning network; Neural network control systems; Nonlinear systems; Recurrent neural network

Funding

  1. National Natural Science Foundation of China [61673322, 61673326, 91746103]
  2. Fundamental Research Funds for the Central Universities [20720190142]
  3. Key Project of National Key RD Project [2017YFC1703303]

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This paper introduces a novel brain emotional neural network that integrates a wavelet neural network into a conventional brain emotional learning network and employs a recurrent structure to address the challenges of nonlinearity and uncertainty in control systems. The proposed network outperformed other popular neural-network-based control systems in experiments on uncertain nonlinear systems.
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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