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AI based Robot Safe Learning and Control / by Xuefeng Zhou, Zhihao Xu, Shuai Li, Hongmin Wu, Taobo Cheng, Xiaojing Lv.

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Format:
Book
Author/Creator:
Zhou, Xuefeng, Author.
Xu, Zhihao, Author.
Li, Shuai, Author.
Wu, Hongmin., Author.
Cheng, Taobo., Author.
Lv, Xiaojing., Author.
Language:
English
Subjects (All):
Robotics.
Automatic control.
Artificial intelligence.
Robotic Engineering.
Control and Systems Theory.
Artificial Intelligence.
Local Subjects:
Robotic Engineering.
Control and Systems Theory.
Artificial Intelligence.
Physical Description:
1 online resource (XVII, 127 p. 42 illus., 35 illus. in color.)
Edition:
1st ed. 2020.
Place of Publication:
Singapore : Springer Nature Singapore : Imprint: Springer, 2020.
Language Note:
English
Summary:
This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate andgraduate students in colleges and universities.
Contents:
Adaptive Jacobian based Trajectory Tracking for Redundant Manipulators with Model Uncertainties in Repetitive Tasks
RNN based Trajectory Control for Manipulators with Uncertain Kinematic Parameters
RNN Based Adaptive Compliance Control for Robots with Model Uncertainties
Deep RNN based Obstacle Avoidance Control for Redundant Manipulators .
ISBN:
981-15-5503-6
OCLC:
1159235503
Access Restriction:
Open access Unrestricted online access

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