Self-organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots

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Self-organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots. / Wu, Qiuxia; Lin, Chih-Min ; Fang, Wubing; Chao, Fei; Yang, Longzhi ; Shang, Changjing; Changle, Zhou .

In: IEEE Access, Vol. 6, 08.10.2018, p. 59096-59108.

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Wu, Qiuxia ; Lin, Chih-Min ; Fang, Wubing ; Chao, Fei ; Yang, Longzhi ; Shang, Changjing ; Changle, Zhou . / Self-organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots. In: IEEE Access. 2018 ; Vol. 6. pp. 59096-59108.

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@article{7da448149f244e59ae6a172f8ba06cd5,
title = "Self-organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots",
abstract = "The trajectory tracking ability of mobile robots suffers from uncertain disturbances. This paper proposes an adaptive control system consisting of a new type of self-organizing neural network controller for mobile robot control. The newly designed neural network contains the key mechanisms of a typical brain emotional learning controller network and a self-organizing radial basis function network. In this system, the input values are delivered to a sensory channel and an emotional channel; and the two channels interact with each other to generate the final outputs of the proposed network. The proposed network possesses the ability of online generation and elimination of fuzzy rules to achieve an optimal neural structure. The parameters of the proposed network are on-line tunable by the brain emotional learning rules and gradient descent method; in addition, the stability analysis theory is used to guarantee the convergence of the proposed controller. In the experimentation, a simulated mobile robot was applied to verify the feasibility and effectiveness of the proposed control system. The comparative study using the cutting-edge neural network-based control systems confirms the proposed network is capable of producing better control performances with high computational efficiency.",
keywords = "mobile robot, neural network control, self-organizing neural network, brain emotional learning controller network",
author = "Qiuxia Wu and Chih-Min Lin and Wubing Fang and Fei Chao and Longzhi Yang and Changjing Shang and Zhou Changle",
year = "2018",
month = "10",
day = "8",
doi = "10.1109/ACCESS.2018.2874426",
language = "English",
volume = "6",
pages = "59096--59108",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "IEEE Press",

}

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TY - JOUR

T1 - Self-organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots

AU - Wu, Qiuxia

AU - Lin, Chih-Min

AU - Fang, Wubing

AU - Chao, Fei

AU - Yang, Longzhi

AU - Shang, Changjing

AU - Changle, Zhou

PY - 2018/10/8

Y1 - 2018/10/8

N2 - The trajectory tracking ability of mobile robots suffers from uncertain disturbances. This paper proposes an adaptive control system consisting of a new type of self-organizing neural network controller for mobile robot control. The newly designed neural network contains the key mechanisms of a typical brain emotional learning controller network and a self-organizing radial basis function network. In this system, the input values are delivered to a sensory channel and an emotional channel; and the two channels interact with each other to generate the final outputs of the proposed network. The proposed network possesses the ability of online generation and elimination of fuzzy rules to achieve an optimal neural structure. The parameters of the proposed network are on-line tunable by the brain emotional learning rules and gradient descent method; in addition, the stability analysis theory is used to guarantee the convergence of the proposed controller. In the experimentation, a simulated mobile robot was applied to verify the feasibility and effectiveness of the proposed control system. The comparative study using the cutting-edge neural network-based control systems confirms the proposed network is capable of producing better control performances with high computational efficiency.

AB - The trajectory tracking ability of mobile robots suffers from uncertain disturbances. This paper proposes an adaptive control system consisting of a new type of self-organizing neural network controller for mobile robot control. The newly designed neural network contains the key mechanisms of a typical brain emotional learning controller network and a self-organizing radial basis function network. In this system, the input values are delivered to a sensory channel and an emotional channel; and the two channels interact with each other to generate the final outputs of the proposed network. The proposed network possesses the ability of online generation and elimination of fuzzy rules to achieve an optimal neural structure. The parameters of the proposed network are on-line tunable by the brain emotional learning rules and gradient descent method; in addition, the stability analysis theory is used to guarantee the convergence of the proposed controller. In the experimentation, a simulated mobile robot was applied to verify the feasibility and effectiveness of the proposed control system. The comparative study using the cutting-edge neural network-based control systems confirms the proposed network is capable of producing better control performances with high computational efficiency.

KW - mobile robot

KW - neural network control

KW - self-organizing neural network

KW - brain emotional learning controller network

U2 - 10.1109/ACCESS.2018.2874426

DO - 10.1109/ACCESS.2018.2874426

M3 - Article

VL - 6

SP - 59096

EP - 59108

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -

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