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.
