Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

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Type-2 Fuzzy Hybrid Controller Network for Robotic Systems. / Chao, Fei; Zhou, Dajun; Lin, Chih-Min; Yang, Longzhi ; Zhou, Changle ; Shang, Changjing.

In: IEEE Transactions on Cybernetics, Vol. 50, No. 8, 31.08.2020, p. 3778-3792.

Research output: Contribution to journalArticlepeer-review

Harvard

Chao, F, Zhou, D, Lin, C-M, Yang, L, Zhou, C & Shang, C 2020, 'Type-2 Fuzzy Hybrid Controller Network for Robotic Systems', IEEE Transactions on Cybernetics, vol. 50, no. 8, pp. 3778-3792. https://doi.org/10.1109/TCYB.2019.2919128

APA

Chao, F., Zhou, D., Lin, C-M., Yang, L., Zhou, C., & Shang, C. (2020). Type-2 Fuzzy Hybrid Controller Network for Robotic Systems. IEEE Transactions on Cybernetics, 50(8), 3778-3792. https://doi.org/10.1109/TCYB.2019.2919128

Vancouver

Chao F, Zhou D, Lin C-M, Yang L, Zhou C, Shang C. Type-2 Fuzzy Hybrid Controller Network for Robotic Systems. IEEE Transactions on Cybernetics. 2020 Aug 31;50(8):3778-3792. https://doi.org/10.1109/TCYB.2019.2919128

Author

Chao, Fei ; Zhou, Dajun ; Lin, Chih-Min ; Yang, Longzhi ; Zhou, Changle ; Shang, Changjing. / Type-2 Fuzzy Hybrid Controller Network for Robotic Systems. In: IEEE Transactions on Cybernetics. 2020 ; Vol. 50, No. 8. pp. 3778-3792.

Bibtex - Download

@article{582ea3fa4f93456ebe3a92366e481677,
title = "Type-2 Fuzzy Hybrid Controller Network for Robotic Systems",
abstract = "Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural network and a robust compensator controller. The new fuzzy neural network is implemented by integrating a number of key components embedded in a Type-2 fuzzy cerebellar model articulation controller (CMAC) and a brain emotional learning controller (BELC) network, thereby mimicking an ideal sliding mode controller. The system inputs are fed into the neural network through a Type-2 fuzzy inference system (T2FIS), with the results subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the proposed network estimates the nonlinear equations representing the ideal sliding mode controllers using a powerful compensator controller with the support of T2FIS and BELC, guaranteeing robust tracking of the dynamics of the controlled systems. The adaptive dynamic tuning laws of the network are developed by exploiting the popular brain emotional learning rule and the Lyapunov function. The proposed system was applied to a robot manipulator and a mobile robot, demonstrating its efficacy and potential; and a comparative study with alternatives indicates a significant improvement by the proposed system in performing the intelligent dynamic control.",
author = "Fei Chao and Dajun Zhou and Chih-Min Lin and Longzhi Yang and Changle Zhou and Changjing Shang",
year = "2020",
month = aug,
day = "31",
doi = "10.1109/TCYB.2019.2919128",
language = "English",
volume = "50",
pages = "3778--3792",
journal = "IEEE Transactions on Cybernetics",
issn = "2168-2267",
publisher = "IEEE Press",
number = "8",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

AU - Chao, Fei

AU - Zhou, Dajun

AU - Lin, Chih-Min

AU - Yang, Longzhi

AU - Zhou, Changle

AU - Shang, Changjing

PY - 2020/8/31

Y1 - 2020/8/31

N2 - Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural network and a robust compensator controller. The new fuzzy neural network is implemented by integrating a number of key components embedded in a Type-2 fuzzy cerebellar model articulation controller (CMAC) and a brain emotional learning controller (BELC) network, thereby mimicking an ideal sliding mode controller. The system inputs are fed into the neural network through a Type-2 fuzzy inference system (T2FIS), with the results subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the proposed network estimates the nonlinear equations representing the ideal sliding mode controllers using a powerful compensator controller with the support of T2FIS and BELC, guaranteeing robust tracking of the dynamics of the controlled systems. The adaptive dynamic tuning laws of the network are developed by exploiting the popular brain emotional learning rule and the Lyapunov function. The proposed system was applied to a robot manipulator and a mobile robot, demonstrating its efficacy and potential; and a comparative study with alternatives indicates a significant improvement by the proposed system in performing the intelligent dynamic control.

AB - Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural network and a robust compensator controller. The new fuzzy neural network is implemented by integrating a number of key components embedded in a Type-2 fuzzy cerebellar model articulation controller (CMAC) and a brain emotional learning controller (BELC) network, thereby mimicking an ideal sliding mode controller. The system inputs are fed into the neural network through a Type-2 fuzzy inference system (T2FIS), with the results subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the proposed network estimates the nonlinear equations representing the ideal sliding mode controllers using a powerful compensator controller with the support of T2FIS and BELC, guaranteeing robust tracking of the dynamics of the controlled systems. The adaptive dynamic tuning laws of the network are developed by exploiting the popular brain emotional learning rule and the Lyapunov function. The proposed system was applied to a robot manipulator and a mobile robot, demonstrating its efficacy and potential; and a comparative study with alternatives indicates a significant improvement by the proposed system in performing the intelligent dynamic control.

U2 - 10.1109/TCYB.2019.2919128

DO - 10.1109/TCYB.2019.2919128

M3 - Article

C2 - 31283516

VL - 50

SP - 3778

EP - 3792

JO - IEEE Transactions on Cybernetics

JF - IEEE Transactions on Cybernetics

SN - 2168-2267

IS - 8

ER -

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