Broad fuzzy neural control using impedance learning

Haohui Huang; Chenguang Yang; Zhaojie Ju; Yuxia Yuan; Zhijun  Li

doi:10.1109/ICARM.2019.8833648

Broad fuzzy neural control using impedance learning

Haohui Huang
, Chenguang Yang
, Zhaojie Ju
, Yuxia Yuan
, Zhijun Li

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

254 Downloads (Pure)

Abstract

This work proposes a novel control strategy based on broad fuzzy neural network (BFNN) by using impedance learning, which is subjected to contact with the unknown dynamic environment. Compared with the original fuzzy neural network, this framework is provided the prominent feature by taking the advantage of broad learning system (BLS) to approximate the unknown dynamic model. Aiming at offering a compliance contact scheme, this paper introduce the impedance learning to establish the robot-environment interaction model. Also, a stable controller, which is able to tackle the problems related to the state constrain, is designed through Barrier Lyapunov Function (BLF). The proposed method can achieve the favourable tracking action while guaranteeing the stability of closed-loop system. In the end, simulation study is performed to verify the effectiveness of BFNN with a two-DOF manipulator.

Original language	English
Title of host publication	2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	173-178
ISBN (Electronic)	978-1-7281-0064-7
ISBN (Print)	978-1-7281-0063-0
DOIs	https://doi.org/10.1109/ICARM.2019.8833648
Publication status	Published - 12 Sept 2019
Event	4th International Conference on Advanced Robotics and Mechatronics - Toyonaka, Japan Duration: 3 Jul 2019 → 5 Jul 2019

Conference

Conference	4th International Conference on Advanced Robotics and Mechatronics
Abbreviated title	ICARM 2019
Country/Territory	Japan
City	Toyonaka
Period	3/07/19 → 5/07/19

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10.1109/ICARM.2019.8833648

Broad Fuzzy Neural Control
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Accepted author manuscript (Post-print), 586 KB

Cite this

@inproceedings{bc7515108d184eac9b19bb2d9d438312,

title = "Broad fuzzy neural control using impedance learning",

abstract = "This work proposes a novel control strategy based on broad fuzzy neural network (BFNN) by using impedance learning, which is subjected to contact with the unknown dynamic environment. Compared with the original fuzzy neural network, this framework is provided the prominent feature by taking the advantage of broad learning system (BLS) to approximate the unknown dynamic model. Aiming at offering a compliance contact scheme, this paper introduce the impedance learning to establish the robot-environment interaction model. Also, a stable controller, which is able to tackle the problems related to the state constrain, is designed through Barrier Lyapunov Function (BLF). The proposed method can achieve the favourable tracking action while guaranteeing the stability of closed-loop system. In the end, simulation study is performed to verify the effectiveness of BFNN with a two-DOF manipulator.",

author = "Haohui Huang and Chenguang Yang and Zhaojie Ju and Yuxia Yuan and Zhijun Li",

year = "2019",

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doi = "10.1109/ICARM.2019.8833648",

language = "English",

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pages = "173--178",

booktitle = "2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "4th International Conference on Advanced Robotics and Mechatronics, ICARM 2019 ; Conference date: 03-07-2019 Through 05-07-2019",

}

Huang, H, Yang, C, Ju, Z, Yuan, Y & Li, Z 2019, Broad fuzzy neural control using impedance learning. in 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM). Institute of Electrical and Electronics Engineers Inc., pp. 173-178, 4th International Conference on Advanced Robotics and Mechatronics, Toyonaka, Japan, 3/07/19. https://doi.org/10.1109/ICARM.2019.8833648

TY - GEN

T1 - Broad fuzzy neural control using impedance learning

AU - Huang, Haohui

AU - Yang, Chenguang

AU - Ju, Zhaojie

AU - Yuan, Yuxia

AU - Li, Zhijun

PY - 2019/9/12

Y1 - 2019/9/12

N2 - This work proposes a novel control strategy based on broad fuzzy neural network (BFNN) by using impedance learning, which is subjected to contact with the unknown dynamic environment. Compared with the original fuzzy neural network, this framework is provided the prominent feature by taking the advantage of broad learning system (BLS) to approximate the unknown dynamic model. Aiming at offering a compliance contact scheme, this paper introduce the impedance learning to establish the robot-environment interaction model. Also, a stable controller, which is able to tackle the problems related to the state constrain, is designed through Barrier Lyapunov Function (BLF). The proposed method can achieve the favourable tracking action while guaranteeing the stability of closed-loop system. In the end, simulation study is performed to verify the effectiveness of BFNN with a two-DOF manipulator.

AB - This work proposes a novel control strategy based on broad fuzzy neural network (BFNN) by using impedance learning, which is subjected to contact with the unknown dynamic environment. Compared with the original fuzzy neural network, this framework is provided the prominent feature by taking the advantage of broad learning system (BLS) to approximate the unknown dynamic model. Aiming at offering a compliance contact scheme, this paper introduce the impedance learning to establish the robot-environment interaction model. Also, a stable controller, which is able to tackle the problems related to the state constrain, is designed through Barrier Lyapunov Function (BLF). The proposed method can achieve the favourable tracking action while guaranteeing the stability of closed-loop system. In the end, simulation study is performed to verify the effectiveness of BFNN with a two-DOF manipulator.

U2 - 10.1109/ICARM.2019.8833648

DO - 10.1109/ICARM.2019.8833648

M3 - Conference contribution

SN - 978-1-7281-0063-0

SP - 173

EP - 178

BT - 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th International Conference on Advanced Robotics and Mechatronics

Y2 - 3 July 2019 through 5 July 2019

ER -

Broad fuzzy neural control using impedance learning

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