A Novel Self-Organizing Emotional CMAC Network for Robotic Control

Juncheng Zhang; Quanfeng Li; Xiang Chang; Fei Chao; Chih Min Lin; Longzhi Yang; Tuan Tu Huynh; Ling Zheng; Changle Zhou; Changjing Shang

doi:10.1109/IJCNN48605.2020.9207710

A Novel Self-Organizing Emotional CMAC Network for Robotic Control

Juncheng Zhang, Quanfeng Li, Xiang Chang, Fei Chao^*, Chih Min Lin, Longzhi Yang, Tuan Tu Huynh, Ling Zheng, Changle Zhou, Changjing Shang

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

5 Citations (Scopus)

Abstract

This paper proposes a self-organizing control system for uncertain nonlinear systems. The proposed neural network is composed of a conventional brain emotional learning network (BEL) and a cerebellar model articulation controller network (CMAC). The input value of the network is feed to a BEL channel and a CMAC channel. The output of the network is generated by the comprehensive action of the two channels. The structure of the network is dynamic, using a self-organizing algorithm allows increasing or decreasing weight layers. The parameters of the proposed network are on-line tuned by the brain emotional learning rules; the updating rules of CMAC and the robust controller are derived from the Lyapunov function; in addition, stability analysis theory is used to guaranty the proposed controller's convergence. A simulated mobile robot is applied to prove the effectiveness of the proposed control system. By comparing with the performance of other neural-network-based control systems, the proposed network produces better performance.

Original language	English
Title of host publication	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings
Publisher	IEEE Press
ISBN (Electronic)	9781728169262
DOIs	https://doi.org/10.1109/IJCNN48605.2020.9207710
Publication status	Published - 28 Sept 2020
Event	2020 International Joint Conference on Neural Networks, IJCNN 2020 - Virtual, Glasgow, United Kingdom of Great Britain and Northern Ireland Duration: 19 Jul 2020 → 24 Jul 2020

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2020 International Joint Conference on Neural Networks, IJCNN 2020
Country/Territory	United Kingdom of Great Britain and Northern Ireland
City	Virtual, Glasgow
Period	19 Jul 2020 → 24 Jul 2020

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10.1109/IJCNN48605.2020.9207710

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Zhang, J., Li, Q., Chang, X., Chao, F., Lin, C. M., Yang, L., Huynh, T. T., Zheng, L., Zhou, C., & Shang, C. (2020). A Novel Self-Organizing Emotional CMAC Network for Robotic Control. In 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings Article 9207710 (Proceedings of the International Joint Conference on Neural Networks). IEEE Press. https://doi.org/10.1109/IJCNN48605.2020.9207710

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title = "A Novel Self-Organizing Emotional CMAC Network for Robotic Control",

abstract = "This paper proposes a self-organizing control system for uncertain nonlinear systems. The proposed neural network is composed of a conventional brain emotional learning network (BEL) and a cerebellar model articulation controller network (CMAC). The input value of the network is feed to a BEL channel and a CMAC channel. The output of the network is generated by the comprehensive action of the two channels. The structure of the network is dynamic, using a self-organizing algorithm allows increasing or decreasing weight layers. The parameters of the proposed network are on-line tuned by the brain emotional learning rules; the updating rules of CMAC and the robust controller are derived from the Lyapunov function; in addition, stability analysis theory is used to guaranty the proposed controller's convergence. A simulated mobile robot is applied to prove the effectiveness of the proposed control system. By comparing with the performance of other neural-network-based control systems, the proposed network produces better performance.",

author = "Juncheng Zhang and Quanfeng Li and Xiang Chang and Fei Chao and Lin, {Chih Min} and Longzhi Yang and Huynh, {Tuan Tu} and Ling Zheng and Changle Zhou and Changjing Shang",

note = "Funding Information: This work was supported by the National Natural Science Foundation of China (No. 61673322, 61673326, and 91746103), the Fundamental Research Funds for the Central Universities (No. 20720190142), and the European Union{\textquoteright}s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 663830. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Joint Conference on Neural Networks, IJCNN 2020 ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

month = sep,

day = "28",

doi = "10.1109/IJCNN48605.2020.9207710",

language = "English",

series = "Proceedings of the International Joint Conference on Neural Networks",

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booktitle = "2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings",

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Zhang, J, Li, Q, Chang, X , Chao, F, Lin, CM, Yang, L, Huynh, TT, Zheng, L, Zhou, C & Shang, C 2020, A Novel Self-Organizing Emotional CMAC Network for Robotic Control. in 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings., 9207710, Proceedings of the International Joint Conference on Neural Networks, IEEE Press, 2020 International Joint Conference on Neural Networks, IJCNN 2020, Virtual, Glasgow, United Kingdom of Great Britain and Northern Ireland, 19 Jul 2020. https://doi.org/10.1109/IJCNN48605.2020.9207710

A Novel Self-Organizing Emotional CMAC Network for Robotic Control. / Zhang, Juncheng; Li, Quanfeng; Chang, Xiang et al.
2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings. IEEE Press, 2020. 9207710 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

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AU - Zhang, Juncheng

AU - Li, Quanfeng

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AU - Yang, Longzhi

AU - Huynh, Tuan Tu

AU - Zheng, Ling

AU - Zhou, Changle

AU - Shang, Changjing

N1 - Funding Information: This work was supported by the National Natural Science Foundation of China (No. 61673322, 61673326, and 91746103), the Fundamental Research Funds for the Central Universities (No. 20720190142), and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 663830. Publisher Copyright: © 2020 IEEE.

PY - 2020/9/28

Y1 - 2020/9/28

N2 - This paper proposes a self-organizing control system for uncertain nonlinear systems. The proposed neural network is composed of a conventional brain emotional learning network (BEL) and a cerebellar model articulation controller network (CMAC). The input value of the network is feed to a BEL channel and a CMAC channel. The output of the network is generated by the comprehensive action of the two channels. The structure of the network is dynamic, using a self-organizing algorithm allows increasing or decreasing weight layers. The parameters of the proposed network are on-line tuned by the brain emotional learning rules; the updating rules of CMAC and the robust controller are derived from the Lyapunov function; in addition, stability analysis theory is used to guaranty the proposed controller's convergence. A simulated mobile robot is applied to prove the effectiveness of the proposed control system. By comparing with the performance of other neural-network-based control systems, the proposed network produces better performance.

AB - This paper proposes a self-organizing control system for uncertain nonlinear systems. The proposed neural network is composed of a conventional brain emotional learning network (BEL) and a cerebellar model articulation controller network (CMAC). The input value of the network is feed to a BEL channel and a CMAC channel. The output of the network is generated by the comprehensive action of the two channels. The structure of the network is dynamic, using a self-organizing algorithm allows increasing or decreasing weight layers. The parameters of the proposed network are on-line tuned by the brain emotional learning rules; the updating rules of CMAC and the robust controller are derived from the Lyapunov function; in addition, stability analysis theory is used to guaranty the proposed controller's convergence. A simulated mobile robot is applied to prove the effectiveness of the proposed control system. By comparing with the performance of other neural-network-based control systems, the proposed network produces better performance.

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AN - SCOPUS:85093819083

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings

PB - IEEE Press

T2 - 2020 International Joint Conference on Neural Networks, IJCNN 2020

Y2 - 19 July 2020 through 24 July 2020

ER -

A Novel Self-Organizing Emotional CMAC Network for Robotic Control

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