A Projected Zeroing Neural Network Model for the Motion Generation and Control

Xin Luo; Zhibin Li; Long Jin; Shuai Li

doi:10.1007/978-981-99-5766-8_4

A Projected Zeroing Neural Network Model for the Motion Generation and Control

Xin Luo^*, Zhibin Li, Long Jin, Shuai Li

^*Corresponding author for this work

BA56 Cognitive production industry

Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Scientific › peer-review

Abstract

Recently, zeroing neural networks plays a vital role in robot control and trajectory tracking. This chapter designs a projected zeroing neural network for redundant robot control, which achieves high superiority and efficiency. The research background about robot control by zeroing neural networks is presented in Sect. 4.1. In Sect. 4.2, we study the feedback-considered scheme of the robot. Neural network design is briefly discussed in Sect. 4.3. The experiments are given in Sect. 4.4. Lastly, the conclusions and future work are concluded in Sect. 4.5.

Original language	English
Title of host publication	Robot Control and Calibration
Subtitle of host publication	Innovative Control Schemes and Calibration Algorithms
Publisher	Springer
Pages	51-68
ISBN (Electronic)	978-981-99-5766-8
ISBN (Print)	978-981-99-5765-1
DOIs	https://doi.org/10.1007/978-981-99-5766-8_4
Publication status	Published - 2023
MoE publication type	A3 Part of a book or another research book

Publication series

Series	SpringerBriefs in Computer Science
Volume	Part F1465
ISSN	2191-5768

Keywords

Joint errors
Joint-drift-free scheme
Projection zeroing neural network
Redundant robot control
Singular avoidance
Zeroing neural network

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10.1007/978-981-99-5766-8_4

Cite this

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title = "A Projected Zeroing Neural Network Model for the Motion Generation and Control",

abstract = "Recently, zeroing neural networks plays a vital role in robot control and trajectory tracking. This chapter designs a projected zeroing neural network for redundant robot control, which achieves high superiority and efficiency. The research background about robot control by zeroing neural networks is presented in Sect. 4.1. In Sect. 4.2, we study the feedback-considered scheme of the robot. Neural network design is briefly discussed in Sect. 4.3. The experiments are given in Sect. 4.4. Lastly, the conclusions and future work are concluded in Sect. 4.5.",

keywords = "Joint errors, Joint-drift-free scheme, Projection zeroing neural network, Redundant robot control, Singular avoidance, Zeroing neural network",

author = "Xin Luo and Zhibin Li and Long Jin and Shuai Li",

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year = "2023",

doi = "10.1007/978-981-99-5766-8_4",

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A Projected Zeroing Neural Network Model for the Motion Generation and Control. / Luo, Xin; Li, Zhibin; Jin, Long et al.
Robot Control and Calibration: Innovative Control Schemes and Calibration Algorithms. Springer, 2023. p. 51-68 (SpringerBriefs in Computer Science, Vol. Part F1465).

Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Scientific › peer-review

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T1 - A Projected Zeroing Neural Network Model for the Motion Generation and Control

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AU - Li, Zhibin

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AU - Li, Shuai

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PY - 2023

Y1 - 2023

N2 - Recently, zeroing neural networks plays a vital role in robot control and trajectory tracking. This chapter designs a projected zeroing neural network for redundant robot control, which achieves high superiority and efficiency. The research background about robot control by zeroing neural networks is presented in Sect. 4.1. In Sect. 4.2, we study the feedback-considered scheme of the robot. Neural network design is briefly discussed in Sect. 4.3. The experiments are given in Sect. 4.4. Lastly, the conclusions and future work are concluded in Sect. 4.5.

AB - Recently, zeroing neural networks plays a vital role in robot control and trajectory tracking. This chapter designs a projected zeroing neural network for redundant robot control, which achieves high superiority and efficiency. The research background about robot control by zeroing neural networks is presented in Sect. 4.1. In Sect. 4.2, we study the feedback-considered scheme of the robot. Neural network design is briefly discussed in Sect. 4.3. The experiments are given in Sect. 4.4. Lastly, the conclusions and future work are concluded in Sect. 4.5.

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KW - Joint-drift-free scheme

KW - Projection zeroing neural network

KW - Redundant robot control

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A Projected Zeroing Neural Network Model for the Motion Generation and Control

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Keywords

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Robot Control and Calibration: Innovative Control Schemes and Calibration Algorithms

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