TY - JOUR
T1 - A recurrent wavelet-based brain emotional learning network controller for nonlinear systems
AU - Zhang, Juncheng
AU - Chao, Fei
AU - Zeng, Hualin
AU - Lin, Chih Min
AU - Yang, Longzhi
N1 - Funding Information: 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).
PY - 2022/3/1
Y1 - 2022/3/1
N2 - 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.
AB - 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.
KW - Brain emotional learning network
KW - Neural network control systems
KW - Nonlinear systems
KW - Recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=85118950631&partnerID=8YFLogxK
U2 - 10.1007/s00500-021-06422-9
DO - 10.1007/s00500-021-06422-9
M3 - Article
AN - SCOPUS:85118950631
SN - 1432-7643
VL - 26
SP - 3013
EP - 3028
JO - Soft Computing
JF - Soft Computing
IS - 6
ER -