TY - JOUR
T1 - Intelligent wavelet fuzzy brain emotional controller using dual function-link network for uncertain nonlinear control systems
AU - Huynh, Tuan Tu
AU - Lin, Chih Min
AU - Le, Nguyen Quoc Khanh
AU - Vu, Mai The
AU - Nguyen, Ngoc Phi
AU - Chao, Fei
N1 - Funding Information:
This research was supported by the Ministry of Science and Technology of Taiwan under grant MOST 109-2811-E-155-504-MY3.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022/2
Y1 - 2022/2
N2 - This study aims to propose a more efficient hybrid algorithm to achieve favorable control performance for uncertain nonlinear systems. The proposed algorithm comprises a dual function-link network-based multilayer wavelet fuzzy brain emotional controller and a sign(.) functional compensator. The proposed algorithm estimates the judgment and emotion of a brain that includes two fuzzy inference systems for the amygdala network and the prefrontal cortex network via using a dual-function-link network and three sub-structures. Three sub-structures are a dual-function-link network, an amygdala network, and a prefrontal cortex network. Particularly, the dual-function-link network is used to adjust the amygdala and orbitofrontal weights separately so that the proposed algorithm can efficiently reduce the tracking error, follow the reference signal well, and achieve good performance. A Lyapunov stability function is used to determine the adaptive laws, which are used to efficiently tune the system parameters online. Simulation and experimental studies for an antilock braking system and a magnetic levitation system are presented to verify the effectiveness and advantage of the proposed algorithm.
AB - This study aims to propose a more efficient hybrid algorithm to achieve favorable control performance for uncertain nonlinear systems. The proposed algorithm comprises a dual function-link network-based multilayer wavelet fuzzy brain emotional controller and a sign(.) functional compensator. The proposed algorithm estimates the judgment and emotion of a brain that includes two fuzzy inference systems for the amygdala network and the prefrontal cortex network via using a dual-function-link network and three sub-structures. Three sub-structures are a dual-function-link network, an amygdala network, and a prefrontal cortex network. Particularly, the dual-function-link network is used to adjust the amygdala and orbitofrontal weights separately so that the proposed algorithm can efficiently reduce the tracking error, follow the reference signal well, and achieve good performance. A Lyapunov stability function is used to determine the adaptive laws, which are used to efficiently tune the system parameters online. Simulation and experimental studies for an antilock braking system and a magnetic levitation system are presented to verify the effectiveness and advantage of the proposed algorithm.
KW - Antilock braking system
KW - Brain emotional learning controller
KW - Dual function-link network
KW - Fuzzy inference system
KW - Magnetic levitation system
KW - Wavelet membership function
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U2 - 10.1007/s10489-021-02482-4
DO - 10.1007/s10489-021-02482-4
M3 - Article
AN - SCOPUS:85124804508
SN - 0924-669X
VL - 52
SP - 2720
EP - 2744
JO - Applied Intelligence
JF - Applied Intelligence
IS - 3
ER -