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Neural Networks for Cooperative Cont...
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Neural Networks for Cooperative Control of Multiple Robot Arms
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Neural Networks for Cooperative Control of Multiple Robot Arms/ by Shuai Li, Yinyan Zhang.
Author:
Li, Shuai.
other author:
Zhang, Yinyan.
Description:
XV, 74 p. 26 illus., 22 illus. in color.online resource. :
Contained By:
Springer Nature eBook
Subject:
Control engineering. -
Online resource:
https://doi.org/10.1007/978-981-10-7037-2
ISBN:
9789811070372
Neural Networks for Cooperative Control of Multiple Robot Arms
Li, Shuai.
Neural Networks for Cooperative Control of Multiple Robot Arms
[electronic resource] /by Shuai Li, Yinyan Zhang. - 1st ed. 2018. - XV, 74 p. 26 illus., 22 illus. in color.online resource. - SpringerBriefs in Computational Intelligence,2625-3704. - SpringerBriefs in Computational Intelligence,.
Neural Networks Based Single Robot Arm Control for Visual Servoing -- Neural Networks for Robot Arm Cooperation with a Start Control Topology -- Neural Networks for Robot Arm Cooperation with a Hierarchical Control Topology -- Neural Networks for Robot Arm Cooperation with a Full Distributed Control Topology.
This is the first book to focus on solving cooperative control problems of multiple robot arms using different centralized or distributed neural network models, presenting methods and algorithms together with the corresponding theoretical analysis and simulated examples. It is intended for graduate students and academic and industrial researchers in the field of control, robotics, neural networks, simulation and modelling.
ISBN: 9789811070372
Standard No.: 10.1007/978-981-10-7037-2doiSubjects--Topical Terms:
1249728
Control engineering.
LC Class. No.: TJ210.2-211.495
Dewey Class. No.: 629.8
Neural Networks for Cooperative Control of Multiple Robot Arms
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This is the first book to focus on solving cooperative control problems of multiple robot arms using different centralized or distributed neural network models, presenting methods and algorithms together with the corresponding theoretical analysis and simulated examples. It is intended for graduate students and academic and industrial researchers in the field of control, robotics, neural networks, simulation and modelling.
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