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

Juncheng Zhang, Fei Chao, Hualin Zeng*, Chih Min Lin, Longzhi Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
JournalSoft Computing
Early online date12 Nov 2021
DOIs
Publication statusE-pub ahead of print - 12 Nov 2021

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