The safe control of robot manipulators is the major topic of this book. The basic theoretical foundation of deep reinforcement learning, dynamic neural networks, is used to build control schemes.
The control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain settings, and obstacle avoidance in dynamic workspaces in order to improve the safety performance of robot systems.
During laboratory discussions and industrial applications, the concept for this book on the safe control of robot arms emerged.
The majority of the information in this book was taken from articles the authors published in journals like IEEE Transactions on Industrial Electronics, neurocomputing, etc.
This book can be used as a reference for graduate and senior undergraduate students in colleges and institutions as well as researchers and designers of robotic systems and AI-based controllers.
