Adaptive neural network tracking control of snake-like robots via a deterministic learning approach

Limei Zhao; Qing Xiao; Zhengcai Cao; Ran Huang; Yili Fu

doi:10.1109/ROBIO.2017.8324829

Adaptive neural network tracking control of snake-like robots via a deterministic learning approach

Limei Zhao
, Qing Xiao
, Zhengcai Cao
, Ran Huang
, Yili Fu

Beijing University of Chemical Technology
Harbin Institute of Technology

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

3 Scopus citations

Abstract

This paper proposes a new learning control method for snake-like robots to achieve trajectory tracking. Based on deterministic learning, an adaptive neural networks control algorithm is used to track the desired trajectory and approximate the unknown system dynamics of the snake-like robot. After that the learned knowledge from direct neural networks is stored as constant network weights. These weights improve the response speed and the accuracy of the system in repeating same or similar control tasks. By using the Lyapunov approach, the tracking error is proven to be uniformly ultimately bounded and converges to a residual set. Finally, simulation results are presented to illustrate the effectiveness of the proposed control scheme.

Original language	English
Title of host publication	2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2710-2715
Number of pages	6
ISBN (Electronic)	9781538637418
DOIs	https://doi.org/10.1109/ROBIO.2017.8324829
State	Published - 2 Jul 2017
Externally published	Yes
Event	2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017 - Macau, China Duration: 5 Dec 2017 → 8 Dec 2017

Publication series

Name	2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
Volume	2018-January

Conference

Conference	2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
Country/Territory	China
City	Macau
Period	5/12/17 → 8/12/17

Access to Document

10.1109/ROBIO.2017.8324829

Cite this

Zhao, L., Xiao, Q., Cao, Z., Huang, R., & Fu, Y. (2017). Adaptive neural network tracking control of snake-like robots via a deterministic learning approach. In 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017 (pp. 2710-2715). (2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROBIO.2017.8324829

Zhao, Limei ; Xiao, Qing ; Cao, Zhengcai et al. / Adaptive neural network tracking control of snake-like robots via a deterministic learning approach. 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2710-2715 (2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017).

@inproceedings{da22178b08aa47158a558a6082e84eab,

title = "Adaptive neural network tracking control of snake-like robots via a deterministic learning approach",

abstract = "This paper proposes a new learning control method for snake-like robots to achieve trajectory tracking. Based on deterministic learning, an adaptive neural networks control algorithm is used to track the desired trajectory and approximate the unknown system dynamics of the snake-like robot. After that the learned knowledge from direct neural networks is stored as constant network weights. These weights improve the response speed and the accuracy of the system in repeating same or similar control tasks. By using the Lyapunov approach, the tracking error is proven to be uniformly ultimately bounded and converges to a residual set. Finally, simulation results are presented to illustrate the effectiveness of the proposed control scheme.",

author = "Limei Zhao and Qing Xiao and Zhengcai Cao and Ran Huang and Yili Fu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017 ; Conference date: 05-12-2017 Through 08-12-2017",

year = "2017",

month = jul,

day = "2",

doi = "10.1109/ROBIO.2017.8324829",

language = "英语",

series = "2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017",

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

pages = "2710--2715",

booktitle = "2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017",

address = "美国",

}

Zhao, L, Xiao, Q, Cao, Z, Huang, R & Fu, Y 2017, Adaptive neural network tracking control of snake-like robots via a deterministic learning approach. in 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017. 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 2710-2715, 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017, Macau, China, 5/12/17. https://doi.org/10.1109/ROBIO.2017.8324829

Adaptive neural network tracking control of snake-like robots via a deterministic learning approach. / Zhao, Limei; Xiao, Qing; Cao, Zhengcai et al.
2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2710-2715 (2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017; Vol. 2018-January).

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

TY - GEN

T1 - Adaptive neural network tracking control of snake-like robots via a deterministic learning approach

AU - Zhao, Limei

AU - Xiao, Qing

AU - Cao, Zhengcai

AU - Huang, Ran

AU - Fu, Yili

PY - 2017/7/2

Y1 - 2017/7/2

N2 - This paper proposes a new learning control method for snake-like robots to achieve trajectory tracking. Based on deterministic learning, an adaptive neural networks control algorithm is used to track the desired trajectory and approximate the unknown system dynamics of the snake-like robot. After that the learned knowledge from direct neural networks is stored as constant network weights. These weights improve the response speed and the accuracy of the system in repeating same or similar control tasks. By using the Lyapunov approach, the tracking error is proven to be uniformly ultimately bounded and converges to a residual set. Finally, simulation results are presented to illustrate the effectiveness of the proposed control scheme.

AB - This paper proposes a new learning control method for snake-like robots to achieve trajectory tracking. Based on deterministic learning, an adaptive neural networks control algorithm is used to track the desired trajectory and approximate the unknown system dynamics of the snake-like robot. After that the learned knowledge from direct neural networks is stored as constant network weights. These weights improve the response speed and the accuracy of the system in repeating same or similar control tasks. By using the Lyapunov approach, the tracking error is proven to be uniformly ultimately bounded and converges to a residual set. Finally, simulation results are presented to illustrate the effectiveness of the proposed control scheme.

UR - https://www.scopus.com/pages/publications/85049913089

U2 - 10.1109/ROBIO.2017.8324829

DO - 10.1109/ROBIO.2017.8324829

M3 - 会议稿件

AN - SCOPUS:85049913089

T3 - 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017

SP - 2710

EP - 2715

BT - 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017

Y2 - 5 December 2017 through 8 December 2017

ER -

Zhao L, Xiao Q, Cao Z, Huang R, Fu Y. Adaptive neural network tracking control of snake-like robots via a deterministic learning approach. In 2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2710-2715. (2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017). doi: 10.1109/ROBIO.2017.8324829

Adaptive neural network tracking control of snake-like robots via a deterministic learning approach

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