TY - JOUR
T1 - An Improved Fuzzy Brain Emotional Learning Model Network Controller for Humanoid Robots
AU - Fang, Wubing
AU - Chao, Fei
AU - Lin, Chih-Min
AU - Yang, Longzhi
AU - Shang, Changjing
AU - Changle, Zhou
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (Nos. 61673322, 61673326, and 91746103), the Fundamental Research Funds for the Central Universities (No. 20720160126), Natural Science Foundation of Fujian Province of China (Nos. 2017J01128 and 2017J01129), and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 663830.
Publisher Copyright:
Copyright © 2019 Fang, Chao, Lin, Yang, Shang and Zhou.
PY - 2019/2/4
Y1 - 2019/2/4
N2 - The brain emotional learning (BEL) system was inspired by the biological amygdala-orbitofrontal model to mimic the high speed of the emotional learning mechanism in the mammalian brain, which has been successfully applied in many real-world applications. Despite of its success, such system often suffers from slow convergence for online humanoid robotic control. This paper presents an improved fuzzy BEL model (iFBEL) neural network by integrating a fuzzy neural network (FNN) to a conventional BEL, in an effort to better support humanoid robots. In particular, the system inputs are passed into a sensory and emotional channels that jointly produce the final outputs of the network. The non-linear approximation ability of the iFBEL is achieved by taking the BEL network as the emotional channel. The proposed iFBEL works with a robust controller in generating the hand and gait motion of a humanoid robot. The updating rules of the iFBEL-based controller are composed of two parts, including a sensory channel followed by the updating rules of the conventional BEL model, and the updating rules of the FNN and the robust controller which are derived from the “Lyapunov” function. The experiments on a three-joint robot manipulator and a six-joint biped robot demonstrated the superiority of the proposed system in reference to a conventional proportional-integral-derivative controller and a fuzzy cerebellar model articulation controller, based on the more accurate and faster control performance of the proposed iFBEL.
AB - The brain emotional learning (BEL) system was inspired by the biological amygdala-orbitofrontal model to mimic the high speed of the emotional learning mechanism in the mammalian brain, which has been successfully applied in many real-world applications. Despite of its success, such system often suffers from slow convergence for online humanoid robotic control. This paper presents an improved fuzzy BEL model (iFBEL) neural network by integrating a fuzzy neural network (FNN) to a conventional BEL, in an effort to better support humanoid robots. In particular, the system inputs are passed into a sensory and emotional channels that jointly produce the final outputs of the network. The non-linear approximation ability of the iFBEL is achieved by taking the BEL network as the emotional channel. The proposed iFBEL works with a robust controller in generating the hand and gait motion of a humanoid robot. The updating rules of the iFBEL-based controller are composed of two parts, including a sensory channel followed by the updating rules of the conventional BEL model, and the updating rules of the FNN and the robust controller which are derived from the “Lyapunov” function. The experiments on a three-joint robot manipulator and a six-joint biped robot demonstrated the superiority of the proposed system in reference to a conventional proportional-integral-derivative controller and a fuzzy cerebellar model articulation controller, based on the more accurate and faster control performance of the proposed iFBEL.
KW - brain emotional learning network
KW - Fuzzy neural network
KW - robot control
KW - Neural network
KW - humanoid robot
KW - Neural network control
KW - Humanoid robot control
KW - Brain emotional learning network
KW - Sliding mode control
UR - http://www.scopus.com/inward/record.url?scp=85065591150&partnerID=8YFLogxK
U2 - 10.3389/fnbot.2019.00002
DO - 10.3389/fnbot.2019.00002
M3 - Article
C2 - 30778294
SN - 1662-5218
VL - 13
JO - Frontiers in Neurorobotics
JF - Frontiers in Neurorobotics
M1 - 2
ER -