AI based Robot Safe Learning and Control

• Main Author: Zhou, Xuefeng (auth)
• Other Authors: Xu, Zhihao (auth), Li, Shuai (auth), Wu, Hongmin (auth), Cheng, Taobo (auth), Lv, Xiaojing (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: Singapore Springer Nature 2020
• Subjects: Robotics; Automatic control engineering; Artificial intelligence; Robotics and Automation; Control and Systems Theory; Artificial Intelligence; Robotic Engineering; Safe Control; Deep Reinforcement Learning; Recurrent Neural Network; Force Control; Obstacle Ovoidance; Adaptive Control; Trajectory Tracking; Open Access
• Online Access: OAPEN Library: download the publication; OAPEN Library: description of the publication

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 and graduate students in colleges and universities.